diff --git a/.nojekyll b/.nojekyll index a41c0f9..3299b22 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -0ca92575 \ No newline at end of file +5c950b5f \ No newline at end of file diff --git a/baseball/verlander-pitches.html b/baseball/verlander-pitches.html index 7153081..2da7f35 100644 --- a/baseball/verlander-pitches.html +++ b/baseball/verlander-pitches.html @@ -2,7 +2,7 @@ - + @@ -48,7 +48,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -57,6 +63,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -70,9 +77,9 @@
-
-
-
+
-
+

Justin Verlander Pitches

@@ -509,8 +534,10 @@

Justin Verlander Pitches

+
+

Motivation

After nearly two full seasons due to injury, at the age of 39 Justin Verlander returned for the 2022 season to win the American League Cy Young award and World Series with the Houston Astros. Leading the league in a variety of statistics, Verlander dominated in his starts throughout the season. Pitch selection has played a key role into Verlander’s recent success with the Astros. Verlander throws four types of pitches (using MLB’s abbreviation): fastball (FF), slider (SL), curveball (CB), and changeup (CH). However, pitches are thrown in the context of an at-bat where the ball-strike count starts 0-0, and progresses until either the batter strikes out (reaches three strikes), is walked (reaches four balls), or is either hit-by-pitch or hits the ball in-play. As the count varies, pitchers often decide to favor certain pitches over others, e.g, with three balls (i.e., 3-X counts) the pitcher may favor throwing more accurate fastballs relative to out-of-the-zone offspeed pitches that are favored with two strikes (i.e., X-2 counts).

@@ -641,10 +668,9 @@

References

// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -654,8 +680,17 @@

References

interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -669,6 +704,125 @@

References

const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -711,6 +865,7 @@

References

} div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -736,6 +891,32 @@

References

}); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -801,7 +982,9 @@

References

 
-
Maintained by Ron Yurko
+
+

Maintained by Ron Yurko

+
 
@@ -810,4 +993,5 @@

References

+ \ No newline at end of file diff --git a/basketball/WNBA20Yrs.html b/basketball/WNBA20Yrs.html new file mode 100644 index 0000000..246cfff --- /dev/null +++ b/basketball/WNBA20Yrs.html @@ -0,0 +1,1022 @@ + + + + + + + + + + + + +SCORE Sports Data Repository - Women’s National Baskeball Assosiation game statistics + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Women’s National Baskeball Assosiation game statistics

+
+
Data cleaning
+
Missing Data
+
Tables
+
Logistic Regression
+
+
+ +
+
+ This data set contains team game statistics for 16 WNBA teams and for 20 seasons. +
+
+ + +
+ +
+
Author
+
+

Kristen Varin

+
+
+ +
+
Published
+
+

December 15, 2023

+
+
+ + +
+ + + +
+ + +
+

Motivation

+

All data sets used in this worksheet are from the Wehoop Package.

+

The National Basketball Association (NBA) has dominated over the WNBA in popularity since the creation of the WNBA 50 years after that of the NBA. For this reason, there are way more data sets available for the NBA. Thus, there is a need for high quality WNBA data sets in order to run analysis on the players and teams. This data set can be used to demonstrate the process of cleaning, prepping, and determining quality of data.

+
+
+

Data

+

The WNBA20Yrs data set contains 8920 rows and 9 columns. Each pair of rows represents a game played by two WNBA teams in one of the 2003 to 2022 regular seasons. The first row is for the away team and the second row is for the home team. The columns are as follows:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
WNBA20Yrs.csv
VariableDescription
game_idgame id number
seasonseason number
season_typebinary predictor; 2 if regular season game; 3 if playoff game
game_datedate of the game
team_idteam id number
team_display_namefull team name (name and city)
team_winnerBoolean; True if the team won the game
opponent_team_idid number of the opponent
team_home_awayWhere the game was played; either “home” or “away”
+
+
+

Questions

+

The goal will be to create a worksheet for the students telling them we want to predict whether a team makes playoffs based on their record halfway through the season. First, they will need to clean the data based on prompts. We will ask them what they notice about total games played by each team across seasons. They should recognize that the total games recorded is inconsistent between teams and with online WNBA statistics. We will ask them why this is a problem and how the issue could be solved if we still wanted to run the original regression.

+
    +

  • Create a table including a team’s mid season record, whether they would make playoffs based on that record (teams with the top 8 records make the playoffs), their record at the end of the season, and whether they made playoffs based on that record.

    • +

  • What do you notice about the team IDs in this data set? Filter out the IDs we won’t be using and rename the teams so that the same IDs all have the most recent team name.

    • +

  • What problem is there regarding number of games played by each team? What are some ways we could solve this problem?

    • +

  • Run logistic Regression Models to predict whether a team makes playoffs based on their record at the halfway point of the season. If is significant? Interpret the results.

    • +
+
+
+

References

+

Gilani S, Hutchinson G (2022). wehoop: Access Women’s Basketball Play by Play Data. R package version 1.5.0, https://CRAN.R-project.org/package=wehoop.

+ + +
+ +
+ +
+ + + + + + \ No newline at end of file diff --git a/basketball/nba-player-stats.html b/basketball/nba-player-stats.html index 2ec5655..8138a81 100644 --- a/basketball/nba-player-stats.html +++ b/basketball/nba-player-stats.html @@ -2,7 +2,7 @@ - + @@ -48,7 +48,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -57,6 +63,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -66,15 +73,42 @@ + +
-
-
-
+
-
+

National Basketball Association Player Statistics

@@ -511,8 +563,10 @@

National Basketball Association Player Statistics

+
+

Motivation

The National Basketball Association (NBA) is the top men’s professional basketball league in the world. While players have predefined positions, the sport is becoming increasingly positionless - with centers attempting more three point shots and guards driving the ball inside to dunk. With this dataset, you can explore clustering NBA players based on various types of statistics and compare your players labels to the predefined positions.

@@ -747,10 +801,9 @@

References

// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -760,8 +813,17 @@

References

interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -775,6 +837,125 @@

References

const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -817,6 +998,7 @@

References

} div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -842,6 +1024,32 @@

References

}); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -907,7 +1115,9 @@

References

 
-
Maintained by Ron Yurko
+
+

Maintained by Ron Yurko

+
 
@@ -916,4 +1126,5 @@

References

+ \ No newline at end of file diff --git a/basketball/wnba-shots.html b/basketball/wnba-shots.html index 7f68511..1143ca2 100644 --- a/basketball/wnba-shots.html +++ b/basketball/wnba-shots.html @@ -2,7 +2,7 @@ - + @@ -48,7 +48,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -57,6 +63,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -70,9 +77,9 @@
-
-
-
+
-
+

Women’s National Basketball Association Shots

@@ -513,8 +538,10 @@

Women’s National Basketball Association Shots

+
+

Motivation

The Women’s National Basketball Association (WNBA) is the top professional women’s basketball league in the world. The league records every shot players take along with contextual information about the shot such as its location, a description of the shot type, as well as the outcome. With this dataset, you can predict the success of each shot attempt to compute the expected value of shot types and compare team decision making.

@@ -693,10 +720,9 @@

References

// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -706,8 +732,17 @@

References

interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -721,6 +756,125 @@

References

const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -763,6 +917,7 @@

References

} div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -788,6 +943,32 @@

References

}); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -853,7 +1034,9 @@

References

 
-
Maintained by Ron Yurko
+
+

Maintained by Ron Yurko

+
 
@@ -862,4 +1045,5 @@

References

+ \ No newline at end of file diff --git a/by-statsds-topic.html b/by-statsds-topic.html index 24aaa8d..3609598 100644 --- a/by-statsds-topic.html +++ b/by-statsds-topic.html @@ -2,7 +2,7 @@ - + @@ -47,7 +47,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -56,6 +62,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -94,15 +101,42 @@ + +
-
-
-
+
+
Categories
All (25)
ANOVA for means (1)
Chi-square test for association (1)
Correlation (2)
Data cleaning (1)
Difference in means confidence intervals (1)
Difference in means hypothesis tests (1)
Exploratory Data Analysis (1)
Histograms (1)
Linear Regression (1)
Linear regression (1)
Logistic Regression (1)
Missing Data (1)
Outliers (1)
Pairwise comparisons (1)
Scatter Plots (1)
Summary statistics (1)
Tables (1)
categorical data (3)
chi-squared test (3)
classification (2)
clustering (2)
correlation (3)
correlations (1)
data visualization (2)
data wrangling (4)
density estimation (2)
exploratory data Analysis (1)
five number summary (1)
generalized additive models (2)
indicator variables (1)
joining (1)
linear regression (6)
logistic regression (4)
merging (1)
missing data (1)
model assumptions (1)
model building (1)
multicollinearity (1)
multinomial logistic regression (2)
multiple linear regression (1)
naive bayes classifier (2)
outlier detection (1)
principal component analysis (3)
proportions (1)
regularization (1)
side-by-side boxplots (1)
summary statistics (1)
transformations in regression (1)
two-way tables (1)
variable selection (1)
z-scores (2)
@@ -509,27 +561,62 @@

Datasets By Topic

+
+
-
+
-

 

+

+
 
+

-

-2023 Boston Marathon runners +2022 Divison III Women’s Soccer Results

- + +
+
+
+Correlation +
+
+Chi-square test for association +
+
+
+Division III women’s soccer teams game results from 2022 season +
+
+
+

+
+Hope Donoghue, Robin Lock
+
+
+
+
+

+
 
+

+
+
+

+2023 Boston Marathon runners +

+
+ +
Summary statistics @@ -541,14 +628,12 @@

z-scores

-
-

The data set looks at the 2023 Boston Marathon results.

+The data set looks at the 2023 Boston Marathon results.
-
- +
Jun 27, 2023
@@ -558,19 +643,19 @@

-
+
-

 

+

+
 
+

-

-Australian Open Tennis (2008-2022) +Australian Open Tennis (2008-2022)

- +
-
proportions @@ -579,14 +664,12 @@

two-way tables

-
-

Results from matches at the Men’s Australian Open Tennis Tournament between 2008 and 2022.

+Results from matches at the Men’s Australian Open Tennis Tournament between 2008 and 2022.
-
- +
Aug 3, 2023
@@ -596,19 +679,19 @@

-
+
-

 

+

+
 
+

-

-Batting Statistics for Division I Softball +Batting Statistics for Division I Softball

- +
-
merging @@ -620,14 +703,12 @@

data wrangling

-
-

These data sets contain batting statistics for Division I softball in the 2021 and 2022 seasons.

+These data sets contain batting statistics for Division I softball in the 2021 and 2022 seasons.
-
- +
May 23, 2023
@@ -637,19 +718,19 @@

-
+
-

 

+

+
 
+

-

-Justin Verlander Pitches +Justin Verlander Pitches

- +
-
categorical data @@ -658,14 +739,12 @@

chi-squared test

-
-

Information about pitches thrown by Justin Verlander in the 2019 and 2022 MLB seasons

+Information about pitches thrown by Justin Verlander in the 2019 and 2022 MLB seasons
-
- +
Mar 18, 2023
@@ -675,19 +754,19 @@

-
+
-

 

+

+
 
+

-

-Lacrosse PLL vs. NLL +Lacrosse PLL vs. NLL

- +
-
Difference in means hypothesis tests @@ -696,14 +775,12 @@

Difference in means confidence intervals

-
-

The data set looks at both lacrosse leagues, their respective teams, and their shot and goal amounts.

+The data set looks at both lacrosse leagues, their respective teams, and their shot and goal amounts.
-
- +
Jun 22, 2023
@@ -713,19 +790,19 @@

-
+
-

 

+

+
 
+

-

-Lake Placid Women’s Ironman Triathlon +Lake Placid Women’s Ironman Triathlon

- +
-
Histograms @@ -740,14 +817,12 @@

Linear Regression

-
-

The data set looks at female participatants in the triathlon Ironman.

+The data set looks at female participatants in the triathlon Ironman.
-
- +
Jun 17, 2023
@@ -757,19 +832,19 @@

-
+
-

 

+

+
 
+

-

-League of Legends - Buffing and Nerfing +League of Legends - Buffing and Nerfing

- +
-
outlier detection @@ -781,14 +856,12 @@

z-scores

-
-

Game play statistics for League of Legends champions in two different patches. Can detecting outliers indicate champions that should be enhanced (buffed) or scaled back (nerfed)?

+Game play statistics for League of Legends champions in two different patches. Can detecting outliers indicate champions that should be enhanced (buffed) or scaled back (nerfed)?
-
- +
Jul 1, 2023
@@ -798,19 +871,19 @@

-
+
-

 

+

+
 
+

-

-NASCAR Data Description +NASCAR Data Description

- +
-
data visualization @@ -831,14 +904,12 @@

model building

-
-

NASCAR Driver Ratings and statistics from 2007-2022

+NASCAR Driver Ratings and statistics from 2007-2022
-
- +
Jul 6, 2023
@@ -848,19 +919,19 @@

-
+
-

 

+

+
 
+

-

-NFL Draft Combine +NFL Draft Combine

- +
-
Exploratory Data Analysis @@ -869,14 +940,12 @@

Linear regression

-
-

NFL Scouting Combine Event scores from 2004 - 2023

+NFL Scouting Combine Event scores from 2004 - 2023
-
- +
Aug 3, 2023
@@ -886,32 +955,30 @@

-
+
-

 

+

+
 
+

-

-NHL Team Power Play Perfomance in 2022-2023 +NHL Team Power Play Perfomance in 2022-2023

- +
-
exploratory data Analysis
-
-

Measures for National Hockey League teams from all 5 on 4 power plays in the 2022-2023 season.

+Measures for National Hockey League teams from all 5 on 4 power plays in the 2022-2023 season.
-
- +
Jul 27, 2023
@@ -921,19 +988,55 @@

-
+
-

 

+

+
 
+

-

-National Basketball Association Player Statistics +NHL Team Statistics

- + +
+
+
+linear regression +
+
+correlation +
+
+
+Team-level statistics from the 2022-2023 NHL season +
+
+
+ +
+Dec 15, 2023 +
+
+Taylor Lum, Robin Lock
+
+
+
+
+

+
 
+

+
+
+

+National Basketball Association Player Statistics +

+
+ +
clustering @@ -942,14 +1045,12 @@

principal component analysis

-
-

NBA player statistics from the 2021-2022 regular season

+NBA player statistics from the 2021-2022 regular season
-
- +
Mar 25, 2023
@@ -959,19 +1060,19 @@

-
+
-

 

+

+
 
+

-

-National Football League Team Statistics +National Football League Team Statistics

- +
-
linear regression @@ -989,14 +1090,12 @@

regularization

-
-

NFL team statistics from 1999 to 2022

+NFL team statistics from 1999 to 2022
-
- +
May 24, 2023
@@ -1006,19 +1105,19 @@

-
+
-

 

+

+
 
+

-

-National Hockey League Shots +National Hockey League Shots

- +
-
classification @@ -1039,14 +1138,12 @@

density estimation

-
-

Information about shots during the 2021-2022 NHL season

+Information about shots during the 2021-2022 NHL season
-
- +
Mar 23, 2023
@@ -1056,19 +1153,19 @@

-
+
-

 

+

+
 
+

-

-National Women’s Soccer League Team Statistics +National Women’s Soccer League Team Statistics

- +
-
linear regression @@ -1077,14 +1174,12 @@

principal component analysis

-
-

NWSL team statistics from 2016 to 2022

+NWSL team statistics from 2016 to 2022
-
- +
Mar 18, 2023
@@ -1094,19 +1189,19 @@

-
+
-

 

+

+
 
+

-

-Premier Hockey Federation Shots +Premier Hockey Federation Shots

- +
-
categorical data @@ -1118,14 +1213,12 @@

logistic regression

-
-

Information about shots during the 2021-2022 PHF season

+Information about shots during the 2021-2022 PHF season
-
- +
Mar 23, 2023
@@ -1135,19 +1228,19 @@

-
+
-

 

+

+
 
+

-

-Soccer Heights +Soccer Heights

- +
-
ANOVA for means @@ -1156,14 +1249,12 @@

Pairwise comparisons

-
-

Heights for a sample of 100 soccer players by position

+Heights for a sample of 100 soccer players by position
-
- +
Aug 3, 2023
@@ -1173,19 +1264,19 @@

-
+
-

 

+

+
 
+

-

-Statistics of PGA Tournaments During the 2022 Season +Statistics of PGA Tournaments During the 2022 Season

- +
-
correlation @@ -1194,14 +1285,12 @@

linear regression

-
-

Measures of Putting, Driving, and Success for individual golfers in 19 professional tournaments in 2022

+Measures of Putting, Driving, and Success for individual golfers in 19 professional tournaments in 2022
-
- +
May 19, 2023
@@ -1211,19 +1300,19 @@

-
+
-

 

+

+
 
+

-

-Sumo wrestler characteristics +Sumo wrestler characteristics

- +
-
data wrangling @@ -1232,14 +1321,12 @@

data visualization

-
-

Sumo wrestling data 1957-2023.

+Sumo wrestling data 1957-2023.
-
- +
Jun 29, 2023
@@ -1249,19 +1336,19 @@

-
+
-

 

+

+
 
+

-

-Team Statistics for Division I Women’s Volleyball +Team Statistics for Division I Women’s Volleyball

- +
-
multicollinearity @@ -1276,14 +1363,12 @@

summary statistics

-
-

These data sets contain team statistics for Division I women’s volleyball in the 2022 and 2023 seasons.

+These data sets contain team statistics for Division I women’s volleyball in the 2022 and 2023 seasons.
-
- +
Jul 24, 2023
@@ -1293,19 +1378,19 @@

-
+
-

 

+

+
 
+

-

-Team statistics for NCAA Division 1 Men’s and Women’s Lacrosse +Team statistics for NCAA Division 1 Men’s and Women’s Lacrosse

- +
-
correlation @@ -1320,14 +1405,12 @@

side-by-side boxplots

-
-

These data sets contain team statistics for NCAA Division I women’s and men’s lacrosse in the 2022 and 2023 seasons.

+These data sets contain team statistics for NCAA Division I women’s and men’s lacrosse in the 2022 and 2023 seasons.
-
- +
Jul 24, 2023
@@ -1337,19 +1420,19 @@

-
+
-

 

+

+
 
+

-

-WTA Grand Slam Matches +WTA Grand Slam Matches

- +
-
logistic regression @@ -1367,14 +1450,12 @@

clustering

-
-

WTA Grand Slam Matches from 2018 to 2022

+WTA Grand Slam Matches from 2018 to 2022
-
- +
May 24, 2023
@@ -1384,19 +1465,61 @@

-
+
-

 

+

+
 
+

-

-Women’s National Basketball Association Shots +Women’s National Baskeball Assosiation game statistics

- + +
+
+
+Data cleaning +
+
+Missing Data +
+
+Tables +
+
+Logistic Regression +
+
+
+This data set contains team game statistics for 16 WNBA teams and for 20 seasons. +
+
+
+ +
+Dec 15, 2023 +
+
+Kristen Varin
+
+
+
+
+

+
 
+

+
+
+

+Women’s National Basketball Association Shots +

+
+ +
classification @@ -1417,14 +1540,12 @@

density estimation

-
-

Information about shots during the 2021-2022 WNBA season

+Information about shots during the 2021-2022 WNBA season
-
- +
Mar 25, 2023
@@ -1518,10 +1639,9 @@

// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -1531,8 +1651,17 @@

interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -1546,6 +1675,125 @@

const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -1588,6 +1836,7 @@

} div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -1613,6 +1862,32 @@

}); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -1678,7 +1953,9 @@

 
-
Maintained by Ron Yurko

Edit this page

Report an issue

+
+

Maintained by Ron Yurko

+
 
@@ -1687,4 +1964,5 @@

+ \ No newline at end of file diff --git a/data-sources.html b/data-sources.html index 7ecc8df..3cf83dd 100644 --- a/data-sources.html +++ b/data-sources.html @@ -2,7 +2,7 @@ - + @@ -45,7 +45,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -54,6 +60,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -67,9 +74,9 @@
-
-
-
+
+

+

Here are some resources for finding sports datasets:

  • CRAN Task View for Sports Analytics - catalog of R packages published on the Comprehensive R Archive Network (CRAN) organized by sport (curated by Benjamin S. Baumer, Quang Nguyen, and Gregory J. Matthews)
    • @@ -569,10 +596,9 @@

    Data Sources

    // clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -582,8 +608,17 @@

    Data Sources

    interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -597,6 +632,125 @@

    Data Sources

    const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -639,6 +793,7 @@

    Data Sources

    } div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -664,6 +819,32 @@

    Data Sources

    }); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -729,7 +910,9 @@

    Data Sources

     
    -
    Maintained by Ron Yurko

    Edit this page

    Report an issue

    +
    +

    Maintained by Ron Yurko

    +
     
    @@ -738,4 +921,5 @@

    Data Sources

    + \ No newline at end of file diff --git a/data/WNBA20Yrs.csv b/data/WNBA20Yrs.csv new file mode 100644 index 0000000..8970c14 --- /dev/null +++ b/data/WNBA20Yrs.csv @@ -0,0 +1,8921 @@ +game_id,season,season_type,game_date,team_id,team_display_name,team_winner,opponent_team_id,team_home_away +230916003,2003,3,2003-09-16,6,Los Angeles Sparks,FALSE,3,away +230916003,2003,3,2003-09-16,3,Detroit Shock,TRUE,6,home +230907006,2003,3,2003-09-07,13,Sacramento Monarchs,FALSE,6,away +230907006,2003,3,2003-09-07,6,Los Angeles Sparks,TRUE,13,home +230907003,2003,3,2003-09-07,18,Connecticut Sun,FALSE,3,away +230907003,2003,3,2003-09-07,3,Detroit Shock,TRUE,18,home +230905013,2003,3,2003-09-05,6,Los Angeles Sparks,FALSE,13,away +230905013,2003,3,2003-09-05,13,Sacramento Monarchs,TRUE,6,home +230902004,2003,3,2003-09-02,13,Sacramento Monarchs,TRUE,4,away +230902004,2003,3,2003-09-02,4,Houston Comets,FALSE,13,home +230902003,2003,3,2003-09-02,2,Cleveland Rockers,FALSE,3,away +230902003,2003,3,2003-09-02,3,Detroit Shock,TRUE,2,home +230901006,2003,3,2003-09-01,8,Minnesota Lynx,FALSE,6,away +230901006,2003,3,2003-09-01,6,Los Angeles Sparks,TRUE,8,home +230831004,2003,3,2003-08-31,13,Sacramento Monarchs,FALSE,4,away +230831004,2003,3,2003-08-31,4,Houston Comets,TRUE,13,home +230831003,2003,3,2003-08-31,2,Cleveland Rockers,TRUE,3,away +230831003,2003,3,2003-08-31,3,Detroit Shock,FALSE,2,home +230830001,2003,3,2003-08-30,18,Connecticut Sun,TRUE,1,away +230830001,2003,3,2003-08-30,1,Charlotte Sting,FALSE,18,home +230829002,2003,3,2003-08-29,3,Detroit Shock,TRUE,2,away +230829002,2003,3,2003-08-29,2,Cleveland Rockers,FALSE,3,home +230828008,2003,3,2003-08-28,6,Los Angeles Sparks,FALSE,8,away +230828008,2003,3,2003-08-28,8,Minnesota Lynx,TRUE,6,home +230828018,2003,3,2003-08-28,1,Charlotte Sting,FALSE,18,away +230828018,2003,3,2003-08-28,18,Connecticut Sun,TRUE,1,home +230825014,2003,2,2003-08-25,13,Sacramento Monarchs,FALSE,14,away 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Stars,FALSE,8,away +260709008,2006,2,2006-07-09,8,Minnesota Lynx,TRUE,17,home +260709009,2006,2,2006-07-09,11,Phoenix Mercury,TRUE,9,away +260709009,2006,2,2006-07-09,9,New York Liberty,FALSE,11,home +260709016,2006,2,2006-07-09,19,Chicago Sky,FALSE,16,away +260709016,2006,2,2006-07-09,16,Washington Mystics,TRUE,19,home +260708006,2006,2,2006-07-08,1,Charlotte Sting,FALSE,6,away +260708006,2006,2,2006-07-08,6,Los Angeles Sparks,TRUE,1,home +260708018,2006,2,2006-07-08,11,Phoenix Mercury,FALSE,18,away +260708018,2006,2,2006-07-08,18,Connecticut Sun,TRUE,11,home +260707006,2006,2,2006-07-07,5,Indiana Fever,FALSE,6,away +260707006,2006,2,2006-07-07,6,Los Angeles Sparks,TRUE,5,home +260707019,2006,2,2006-07-07,9,New York Liberty,FALSE,19,away +260707019,2006,2,2006-07-07,19,Chicago Sky,TRUE,9,home +260707008,2006,2,2006-07-07,3,Detroit Shock,TRUE,8,away +260707008,2006,2,2006-07-07,8,Minnesota Lynx,FALSE,3,home +260707017,2006,2,2006-07-07,4,Houston Comets,FALSE,17,away 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Mystics,TRUE,11,away +260702011,2006,2,2006-07-02,11,Phoenix Mercury,FALSE,16,home +260702004,2006,2,2006-07-02,13,Sacramento Monarchs,FALSE,4,away +260702004,2006,2,2006-07-02,4,Houston Comets,TRUE,13,home +260701006,2006,2,2006-07-01,16,Washington Mystics,FALSE,6,away +260701006,2006,2,2006-07-01,6,Los Angeles Sparks,TRUE,16,home +260701014,2006,2,2006-07-01,8,Minnesota Lynx,FALSE,14,away +260701014,2006,2,2006-07-01,14,Seattle Storm,TRUE,8,home +260701017,2006,2,2006-07-01,19,Chicago Sky,FALSE,17,away +260701017,2006,2,2006-07-01,17,San Antonio Silver Stars,TRUE,19,home +260701005,2006,2,2006-07-01,18,Connecticut Sun,TRUE,5,away +260701005,2006,2,2006-07-01,5,Indiana Fever,FALSE,18,home +260701001,2006,2,2006-07-01,13,Sacramento Monarchs,TRUE,1,away +260701001,2006,2,2006-07-01,1,Charlotte Sting,FALSE,13,home +260630006,2006,2,2006-06-30,11,Phoenix Mercury,FALSE,6,away +260630006,2006,2,2006-06-30,6,Los Angeles Sparks,TRUE,11,home +260630009,2006,2,2006-06-30,17,San Antonio Silver Stars,TRUE,9,away +260630009,2006,2,2006-06-30,9,New York Liberty,FALSE,17,home +260630018,2006,2,2006-06-30,3,Detroit Shock,TRUE,18,away +260630018,2006,2,2006-06-30,18,Connecticut Sun,FALSE,3,home +260629013,2006,2,2006-06-29,8,Minnesota Lynx,FALSE,13,away +260629013,2006,2,2006-06-29,13,Sacramento Monarchs,TRUE,8,home +260629019,2006,2,2006-06-29,1,Charlotte Sting,FALSE,19,away +260629019,2006,2,2006-06-29,19,Chicago Sky,TRUE,1,home +260629005,2006,2,2006-06-29,3,Detroit Shock,FALSE,5,away +260629005,2006,2,2006-06-29,5,Indiana Fever,TRUE,3,home +260629004,2006,2,2006-06-29,16,Washington Mystics,FALSE,4,away +260629004,2006,2,2006-06-29,4,Houston Comets,TRUE,16,home +260628006,2006,2,2006-06-28,14,Seattle Storm,TRUE,6,away +260628006,2006,2,2006-06-28,6,Los Angeles Sparks,FALSE,14,home +260628011,2006,2,2006-06-28,8,Minnesota Lynx,FALSE,11,away +260628011,2006,2,2006-06-28,11,Phoenix Mercury,TRUE,8,home +260627014,2006,2,2006-06-27,13,Sacramento Monarchs,FALSE,14,away 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Shock,TRUE,9,home +270825005,2007,3,2007-08-25,18,Connecticut Sun,FALSE,5,away +270825005,2007,3,2007-08-25,5,Indiana Fever,TRUE,18,home +270825017,2007,3,2007-08-25,13,Sacramento Monarchs,FALSE,17,away +270825017,2007,3,2007-08-25,17,San Antonio Silver Stars,TRUE,13,home +270824014,2007,3,2007-08-24,11,Phoenix Mercury,TRUE,14,away +270824014,2007,3,2007-08-24,14,Seattle Storm,FALSE,11,home +270824009,2007,3,2007-08-24,3,Detroit Shock,FALSE,9,away +270824009,2007,3,2007-08-24,9,New York Liberty,TRUE,3,home +270823013,2007,3,2007-08-23,17,San Antonio Silver Stars,FALSE,13,away +270823013,2007,3,2007-08-23,13,Sacramento Monarchs,TRUE,17,home +270823018,2007,3,2007-08-23,5,Indiana Fever,FALSE,18,away +270823018,2007,3,2007-08-23,18,Connecticut Sun,TRUE,5,home +270819006,2007,2,2007-08-19,4,Houston Comets,TRUE,6,away +270819006,2007,2,2007-08-19,6,Los Angeles Sparks,FALSE,4,home +270819011,2007,2,2007-08-19,13,Sacramento Monarchs,FALSE,11,away +270819011,2007,2,2007-08-19,11,Phoenix Mercury,TRUE,13,home +270819005,2007,2,2007-08-19,3,Detroit Shock,FALSE,5,away +270819005,2007,2,2007-08-19,5,Indiana Fever,TRUE,3,home +270819008,2007,2,2007-08-19,17,San Antonio Silver Stars,FALSE,8,away +270819008,2007,2,2007-08-19,8,Minnesota Lynx,TRUE,17,home +270819009,2007,2,2007-08-19,19,Chicago Sky,FALSE,9,away +270819009,2007,2,2007-08-19,9,New York Liberty,TRUE,19,home +270819018,2007,2,2007-08-19,16,Washington Mystics,TRUE,18,away +270819018,2007,2,2007-08-19,18,Connecticut Sun,FALSE,16,home +270817013,2007,2,2007-08-17,11,Phoenix Mercury,TRUE,13,away +270817013,2007,2,2007-08-17,13,Sacramento Monarchs,FALSE,11,home +270817014,2007,2,2007-08-17,6,Los Angeles Sparks,FALSE,14,away +270817014,2007,2,2007-08-17,14,Seattle Storm,TRUE,6,home +270817017,2007,2,2007-08-17,5,Indiana Fever,TRUE,17,away +270817017,2007,2,2007-08-17,17,San Antonio Silver Stars,FALSE,5,home +270817009,2007,2,2007-08-17,18,Connecticut Sun,FALSE,9,away +270817009,2007,2,2007-08-17,9,New York Liberty,TRUE,18,home +270816004,2007,2,2007-08-16,19,Chicago Sky,TRUE,4,away +270816004,2007,2,2007-08-16,4,Houston Comets,FALSE,19,home +270816008,2007,2,2007-08-16,3,Detroit Shock,FALSE,8,away +270816008,2007,2,2007-08-16,8,Minnesota Lynx,TRUE,3,home +270816016,2007,2,2007-08-16,9,New York Liberty,TRUE,16,away +270816016,2007,2,2007-08-16,16,Washington Mystics,FALSE,9,home +270815013,2007,2,2007-08-15,17,San Antonio Silver Stars,FALSE,13,away +270815013,2007,2,2007-08-15,13,Sacramento Monarchs,TRUE,17,home +270815005,2007,2,2007-08-15,18,Connecticut Sun,TRUE,5,away +270815005,2007,2,2007-08-15,5,Indiana Fever,FALSE,18,home +270814006,2007,2,2007-08-14,17,San Antonio Silver Stars,TRUE,6,away +270814006,2007,2,2007-08-14,6,Los Angeles Sparks,FALSE,17,home +270814014,2007,2,2007-08-14,8,Minnesota Lynx,FALSE,14,away +270814014,2007,2,2007-08-14,14,Seattle Storm,TRUE,8,home +270814019,2007,2,2007-08-14,9,New York Liberty,FALSE,19,away +270814019,2007,2,2007-08-14,19,Chicago Sky,TRUE,9,home +270814003,2007,2,2007-08-14,4,Houston Comets,TRUE,3,away +270814003,2007,2,2007-08-14,3,Detroit Shock,FALSE,4,home +270814016,2007,2,2007-08-14,18,Connecticut Sun,FALSE,16,away +270814016,2007,2,2007-08-14,16,Washington Mystics,TRUE,18,home +270812006,2007,2,2007-08-12,8,Minnesota Lynx,FALSE,6,away +270812006,2007,2,2007-08-12,6,Los Angeles Sparks,TRUE,8,home +270812013,2007,2,2007-08-12,16,Washington Mystics,FALSE,13,away +270812013,2007,2,2007-08-12,13,Sacramento Monarchs,TRUE,16,home +270812009,2007,2,2007-08-12,3,Detroit Shock,FALSE,9,away +270812009,2007,2,2007-08-12,9,New York Liberty,TRUE,3,home +270811011,2007,2,2007-08-11,6,Los Angeles Sparks,FALSE,11,away +270811011,2007,2,2007-08-11,11,Phoenix Mercury,TRUE,6,home +270811014,2007,2,2007-08-11,16,Washington Mystics,FALSE,14,away +270811014,2007,2,2007-08-11,14,Seattle Storm,TRUE,16,home +270811017,2007,2,2007-08-11,4,Houston Comets,FALSE,17,away +270811017,2007,2,2007-08-11,17,San Antonio Silver Stars,TRUE,4,home +270811019,2007,2,2007-08-11,18,Connecticut Sun,TRUE,19,away +270811019,2007,2,2007-08-11,19,Chicago Sky,FALSE,18,home +270811003,2007,2,2007-08-11,5,Indiana Fever,FALSE,3,away +270811003,2007,2,2007-08-11,3,Detroit Shock,TRUE,5,home +270810004,2007,2,2007-08-10,13,Sacramento Monarchs,FALSE,4,away +270810004,2007,2,2007-08-10,4,Houston Comets,TRUE,13,home +270809006,2007,2,2007-08-09,16,Washington Mystics,TRUE,6,away +270809006,2007,2,2007-08-09,6,Los Angeles Sparks,FALSE,16,home +270809011,2007,2,2007-08-09,9,New York Liberty,FALSE,11,away +270809011,2007,2,2007-08-09,11,Phoenix Mercury,TRUE,9,home +270809017,2007,2,2007-08-09,13,Sacramento Monarchs,FALSE,17,away +270809017,2007,2,2007-08-09,17,San Antonio Silver Stars,TRUE,13,home +270809003,2007,2,2007-08-09,14,Seattle Storm,FALSE,3,away +270809003,2007,2,2007-08-09,3,Detroit Shock,TRUE,14,home +270807006,2007,2,2007-08-07,11,Phoenix Mercury,TRUE,6,away +270807006,2007,2,2007-08-07,6,Los Angeles 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Angeles Sparks,FALSE,8,away +270727008,2007,2,2007-07-27,8,Minnesota Lynx,TRUE,6,home +270727017,2007,2,2007-07-27,4,Houston Comets,TRUE,17,away +270727017,2007,2,2007-07-27,17,San Antonio Silver Stars,FALSE,4,home +270726003,2007,2,2007-07-26,19,Chicago Sky,TRUE,3,away +270726003,2007,2,2007-07-26,3,Detroit Shock,FALSE,19,home +270726018,2007,2,2007-07-26,9,New York Liberty,FALSE,18,away +270726018,2007,2,2007-07-26,18,Connecticut Sun,TRUE,9,home +270726013,2007,2,2007-07-26,5,Indiana Fever,FALSE,13,away +270726013,2007,2,2007-07-26,13,Sacramento Monarchs,TRUE,5,home +270725008,2007,2,2007-07-25,11,Phoenix Mercury,TRUE,8,away +270725008,2007,2,2007-07-25,8,Minnesota Lynx,FALSE,11,home +270724013,2007,2,2007-07-24,6,Los Angeles Sparks,FALSE,13,away +270724013,2007,2,2007-07-24,13,Sacramento Monarchs,TRUE,6,home +270724018,2007,2,2007-07-24,3,Detroit Shock,TRUE,18,away +270724018,2007,2,2007-07-24,18,Connecticut Sun,FALSE,3,home +270724005,2007,2,2007-07-24,17,San Antonio Silver Stars,TRUE,5,away +270724005,2007,2,2007-07-24,5,Indiana Fever,FALSE,17,home +270724016,2007,2,2007-07-24,14,Seattle Storm,FALSE,16,away +270724016,2007,2,2007-07-24,16,Washington Mystics,TRUE,14,home +270722011,2007,2,2007-07-22,8,Minnesota Lynx,FALSE,11,away +270722011,2007,2,2007-07-22,11,Phoenix Mercury,TRUE,8,home +270722019,2007,2,2007-07-22,17,San Antonio Silver Stars,FALSE,19,away +270722019,2007,2,2007-07-22,19,Chicago Sky,TRUE,17,home +270722009,2007,2,2007-07-22,14,Seattle Storm,TRUE,9,away +270722009,2007,2,2007-07-22,9,New York Liberty,FALSE,14,home +270722018,2007,2,2007-07-22,4,Houston Comets,FALSE,18,away +270722018,2007,2,2007-07-22,18,Connecticut Sun,TRUE,4,home +270721019,2007,2,2007-07-21,5,Indiana Fever,FALSE,19,away +270721019,2007,2,2007-07-21,19,Chicago Sky,TRUE,5,home +270721003,2007,2,2007-07-21,16,Washington Mystics,FALSE,3,away +270721003,2007,2,2007-07-21,3,Detroit Shock,TRUE,16,home +270720011,2007,2,2007-07-20,6,Los Angeles Sparks,TRUE,11,away 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Liberty,TRUE,19,away +300723019,2010,2,2010-07-23,19,Chicago Sky,FALSE,9,home +300722003,2010,2,2010-07-22,11,Phoenix Mercury,TRUE,3,away +300722003,2010,2,2010-07-22,3,Tulsa Shock,FALSE,11,home +300722008,2010,2,2010-07-22,17,San Antonio Silver Stars,TRUE,8,away +300722008,2010,2,2010-07-22,8,Minnesota Lynx,FALSE,17,home +300722005,2010,2,2010-07-22,6,Los Angeles Sparks,FALSE,5,away +300722005,2010,2,2010-07-22,5,Indiana Fever,TRUE,6,home +300721016,2010,2,2010-07-21,20,Atlanta Dream,FALSE,16,away +300721016,2010,2,2010-07-21,16,Washington Mystics,TRUE,20,home +300720018,2010,2,2010-07-20,9,New York Liberty,TRUE,18,away +300720018,2010,2,2010-07-20,18,Connecticut Sun,FALSE,9,home +300720006,2010,2,2010-07-20,3,Tulsa Shock,FALSE,6,away +300720006,2010,2,2010-07-20,6,Los Angeles Sparks,TRUE,3,home +300720017,2010,2,2010-07-20,14,Seattle Storm,TRUE,17,away +300720017,2010,2,2010-07-20,17,San Antonio Silver Stars,FALSE,14,home +300718009,2010,2,2010-07-18,5,Indiana Fever,TRUE,9,away 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WNBA,FALSE,97,home +300708008,2010,2,2010-07-08,17,San Antonio Silver Stars,FALSE,8,away +300708008,2010,2,2010-07-08,8,Minnesota Lynx,TRUE,17,home +300708005,2010,2,2010-07-08,3,Tulsa Shock,FALSE,5,away +300708005,2010,2,2010-07-08,5,Indiana Fever,TRUE,3,home +300707020,2010,2,2010-07-07,18,Connecticut Sun,FALSE,20,away +300707020,2010,2,2010-07-07,20,Atlanta Dream,TRUE,18,home +300706006,2010,2,2010-07-06,11,Phoenix Mercury,TRUE,6,away +300706006,2010,2,2010-07-06,6,Los Angeles Sparks,FALSE,11,home +300706017,2010,2,2010-07-06,18,Connecticut Sun,FALSE,17,away +300706017,2010,2,2010-07-06,17,San Antonio Silver Stars,TRUE,18,home +300706019,2010,2,2010-07-06,5,Indiana Fever,TRUE,19,away +300706019,2010,2,2010-07-06,19,Chicago Sky,FALSE,5,home +300706014,2010,2,2010-07-06,9,New York Liberty,FALSE,14,away +300706014,2010,2,2010-07-06,14,Seattle Storm,TRUE,9,home +300703011,2010,2,2010-07-03,9,New York Liberty,FALSE,11,away +300703011,2010,2,2010-07-03,11,Phoenix Mercury,TRUE,9,home 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York Liberty,TRUE,20,away +310605020,2011,2,2011-06-05,20,Atlanta Dream,FALSE,9,home +310604017,2011,2,2011-06-04,3,Tulsa Shock,FALSE,17,away +310604017,2011,2,2011-06-04,17,San Antonio Silver Stars,TRUE,3,home +310604005,2011,2,2011-06-04,19,Chicago Sky,FALSE,5,away +310604005,2011,2,2011-06-04,5,Indiana Fever,TRUE,19,home +310604018,2011,2,2011-06-04,16,Washington Mystics,FALSE,18,away +310604018,2011,2,2011-06-04,18,Connecticut Sun,TRUE,16,home +400182104,2011,2,2011-06-04,19,Chicago Sky,FALSE,5,away +400182104,2011,2,2011-06-04,5,Indiana Fever,TRUE,19,home +310604014,2011,2,2011-06-04,11,Phoenix Mercury,FALSE,14,away +310604014,2011,2,2011-06-04,14,Seattle Storm,TRUE,11,home +310603006,2011,2,2011-06-03,8,Minnesota Lynx,FALSE,6,away +310603006,2011,2,2011-06-03,6,Los Angeles Sparks,TRUE,8,home +400425932,2012,3,2012-10-21,8,Minnesota Lynx,FALSE,5,away +400425932,2012,3,2012-10-21,5,Indiana Fever,TRUE,8,home +400425931,2012,3,2012-10-19,8,Minnesota Lynx,FALSE,5,away 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Lynx,TRUE,14,home +400409186,2012,3,2012-10-02,20,Atlanta Dream,FALSE,5,away +400409186,2012,3,2012-10-02,5,Indiana Fever,TRUE,20,home +400407864,2012,3,2012-09-30,8,Minnesota Lynx,FALSE,14,away +400407864,2012,3,2012-09-30,14,Seattle Storm,TRUE,8,home +400409185,2012,3,2012-09-30,5,Indiana Fever,TRUE,20,away +400409185,2012,3,2012-09-30,20,Atlanta Dream,FALSE,5,home +400409182,2012,3,2012-09-29,18,Connecticut Sun,TRUE,9,away +400409182,2012,3,2012-09-29,9,New York Liberty,FALSE,18,home +400407863,2012,3,2012-09-29,6,Los Angeles Sparks,TRUE,17,away +400407863,2012,3,2012-09-29,17,San Antonio Silver Stars,FALSE,6,home +400407862,2012,3,2012-09-28,14,Seattle Storm,FALSE,8,away +400407862,2012,3,2012-09-28,8,Minnesota Lynx,TRUE,14,home +400409184,2012,3,2012-09-28,20,Atlanta Dream,TRUE,5,away +400409184,2012,3,2012-09-28,5,Indiana Fever,FALSE,20,home +400407861,2012,3,2012-09-27,17,San Antonio Silver Stars,FALSE,6,away +400407861,2012,3,2012-09-27,6,Los Angeles Sparks,TRUE,17,home 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Antonio Silver Stars,FALSE,14,away +320921014,2012,2,2012-09-21,14,Seattle Storm,TRUE,17,home +320921016,2012,2,2012-09-21,5,Indiana Fever,TRUE,16,away +320921016,2012,2,2012-09-21,16,Washington Mystics,FALSE,5,home +320920006,2012,2,2012-09-20,8,Minnesota Lynx,FALSE,6,away +320920006,2012,2,2012-09-20,6,Los Angeles Sparks,TRUE,8,home +320920003,2012,2,2012-09-20,9,New York Liberty,FALSE,3,away +320920003,2012,2,2012-09-20,3,Tulsa Shock,TRUE,9,home +320920019,2012,2,2012-09-20,20,Atlanta Dream,TRUE,19,away +320920019,2012,2,2012-09-20,19,Chicago Sky,FALSE,20,home +320919018,2012,2,2012-09-19,5,Indiana Fever,FALSE,18,away +320919018,2012,2,2012-09-19,18,Connecticut Sun,TRUE,5,home +320918006,2012,2,2012-09-18,11,Phoenix Mercury,FALSE,6,away +320918006,2012,2,2012-09-18,6,Los Angeles Sparks,TRUE,11,home +320918014,2012,2,2012-09-18,19,Chicago Sky,FALSE,14,away +320918014,2012,2,2012-09-18,14,Seattle Storm,TRUE,19,home +320918017,2012,2,2012-09-18,9,New York Liberty,FALSE,17,away 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Antonio Silver Stars,FALSE,5,home +320907009,2012,2,2012-09-07,19,Chicago Sky,TRUE,9,away +320907009,2012,2,2012-09-07,9,New York Liberty,FALSE,19,home +320907016,2012,2,2012-09-07,6,Los Angeles Sparks,TRUE,16,away +320907016,2012,2,2012-09-07,16,Washington Mystics,FALSE,6,home +320907018,2012,2,2012-09-07,11,Phoenix Mercury,TRUE,18,away +320907018,2012,2,2012-09-07,18,Connecticut Sun,FALSE,11,home +320906014,2012,2,2012-09-06,3,Tulsa Shock,FALSE,14,away +320906014,2012,2,2012-09-06,14,Seattle Storm,TRUE,3,home +320905009,2012,2,2012-09-05,11,Phoenix Mercury,FALSE,9,away +320905009,2012,2,2012-09-05,9,New York Liberty,TRUE,11,home +320905020,2012,2,2012-09-05,5,Indiana Fever,FALSE,20,away +320905020,2012,2,2012-09-05,20,Atlanta Dream,TRUE,5,home +320904008,2012,2,2012-09-04,6,Los Angeles Sparks,FALSE,8,away +320904008,2012,2,2012-09-04,8,Minnesota Lynx,TRUE,6,home +320904016,2012,2,2012-09-04,18,Connecticut Sun,TRUE,16,away +320904016,2012,2,2012-09-04,16,Washington Mystics,FALSE,18,home +320902019,2012,2,2012-09-02,6,Los Angeles Sparks,FALSE,19,away +320902019,2012,2,2012-09-02,19,Chicago Sky,TRUE,6,home +320902020,2012,2,2012-09-02,18,Connecticut Sun,FALSE,20,away +320902020,2012,2,2012-09-02,20,Atlanta Dream,TRUE,18,home +320901011,2012,2,2012-09-01,17,San Antonio Silver Stars,FALSE,11,away +320901011,2012,2,2012-09-01,11,Phoenix Mercury,TRUE,17,home +320901005,2012,2,2012-09-01,19,Chicago Sky,FALSE,5,away +320901005,2012,2,2012-09-01,5,Indiana Fever,TRUE,19,home +320901009,2012,2,2012-09-01,16,Washington Mystics,FALSE,9,away +320901009,2012,2,2012-09-01,9,New York Liberty,TRUE,16,home +320831008,2012,2,2012-08-31,3,Tulsa Shock,FALSE,8,away +320831008,2012,2,2012-08-31,8,Minnesota Lynx,TRUE,3,home +320830014,2012,2,2012-08-30,11,Phoenix Mercury,TRUE,14,away +320830014,2012,2,2012-08-30,14,Seattle Storm,FALSE,11,home +320830003,2012,2,2012-08-30,6,Los Angeles Sparks,FALSE,3,away +320830003,2012,2,2012-08-30,3,Tulsa Shock,TRUE,6,home 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+320710016,2012,2,2012-07-10,16,Washington Mystics,FALSE,18,home +320708014,2012,2,2012-07-08,11,Phoenix Mercury,FALSE,14,away +320708014,2012,2,2012-07-08,14,Seattle Storm,TRUE,11,home +320708006,2012,2,2012-07-08,20,Atlanta Dream,FALSE,6,away +320708006,2012,2,2012-07-08,6,Los Angeles Sparks,TRUE,20,home +320708003,2012,2,2012-07-08,16,Washington Mystics,FALSE,3,away +320708003,2012,2,2012-07-08,3,Tulsa Shock,TRUE,16,home +320708009,2012,2,2012-07-08,17,San Antonio Silver Stars,TRUE,9,away +320708009,2012,2,2012-07-08,9,New York Liberty,FALSE,17,home +320707006,2012,2,2012-07-07,14,Seattle Storm,FALSE,6,away +320707006,2012,2,2012-07-07,6,Los Angeles Sparks,TRUE,14,home +320707011,2012,2,2012-07-07,20,Atlanta Dream,TRUE,11,away +320707011,2012,2,2012-07-07,11,Phoenix Mercury,FALSE,20,home +320707008,2012,2,2012-07-07,18,Connecticut Sun,TRUE,8,away +320707008,2012,2,2012-07-07,8,Minnesota Lynx,FALSE,18,home +320707005,2012,2,2012-07-07,19,Chicago Sky,FALSE,5,away +320707005,2012,2,2012-07-07,5,Indiana Fever,TRUE,19,home +320706019,2012,2,2012-07-06,9,New York Liberty,TRUE,19,away +320706019,2012,2,2012-07-06,19,Chicago Sky,FALSE,9,home +320706003,2012,2,2012-07-06,18,Connecticut Sun,TRUE,3,away +320706003,2012,2,2012-07-06,3,Tulsa Shock,FALSE,18,home +320706016,2012,2,2012-07-06,17,San Antonio Silver Stars,TRUE,16,away +320706016,2012,2,2012-07-06,16,Washington Mystics,FALSE,17,home +320705005,2012,2,2012-07-05,17,San Antonio Silver Stars,TRUE,5,away +320705005,2012,2,2012-07-05,5,Indiana Fever,FALSE,17,home +320705006,2012,2,2012-07-05,8,Minnesota Lynx,FALSE,6,away +320705006,2012,2,2012-07-05,6,Los Angeles Sparks,TRUE,8,home +320703017,2012,2,2012-07-03,11,Phoenix Mercury,FALSE,17,away +320703017,2012,2,2012-07-03,17,San Antonio Silver Stars,TRUE,11,home +320701019,2012,2,2012-07-01,20,Atlanta Dream,FALSE,19,away +320701019,2012,2,2012-07-01,19,Chicago Sky,TRUE,20,home +320701018,2012,2,2012-07-01,14,Seattle Storm,TRUE,18,away +320701018,2012,2,2012-07-01,18,Connecticut Sun,FALSE,14,home +320701016,2012,2,2012-07-01,11,Phoenix Mercury,FALSE,16,away +320701016,2012,2,2012-07-01,16,Washington Mystics,TRUE,11,home +320701017,2012,2,2012-07-01,8,Minnesota Lynx,FALSE,17,away +320701017,2012,2,2012-07-01,17,San Antonio Silver Stars,TRUE,8,home +320630009,2012,2,2012-06-30,14,Seattle Storm,FALSE,9,away +320630009,2012,2,2012-06-30,9,New York Liberty,TRUE,14,home +320629019,2012,2,2012-06-29,11,Phoenix Mercury,TRUE,19,away +320629019,2012,2,2012-06-29,19,Chicago Sky,FALSE,11,home +320629003,2012,2,2012-06-29,20,Atlanta Dream,TRUE,3,away +320629003,2012,2,2012-06-29,3,Tulsa Shock,FALSE,20,home +320629016,2012,2,2012-06-29,18,Connecticut Sun,TRUE,16,away +320629016,2012,2,2012-06-29,16,Washington Mystics,FALSE,18,home +320628017,2012,2,2012-06-28,6,Los Angeles Sparks,FALSE,17,away +320628017,2012,2,2012-06-28,17,San Antonio Silver Stars,TRUE,6,home +320627008,2012,2,2012-06-27,11,Phoenix Mercury,FALSE,8,away +320627008,2012,2,2012-06-27,8,Minnesota Lynx,TRUE,11,home +320627019,2012,2,2012-06-27,5,Indiana Fever,TRUE,19,away +320627019,2012,2,2012-06-27,19,Chicago Sky,FALSE,5,home +320626003,2012,2,2012-06-26,6,Los Angeles Sparks,FALSE,3,away +320626003,2012,2,2012-06-26,3,Tulsa Shock,TRUE,6,home +320626016,2012,2,2012-06-26,14,Seattle Storm,TRUE,16,away +320626016,2012,2,2012-06-26,16,Washington Mystics,FALSE,14,home +320626020,2012,2,2012-06-26,5,Indiana Fever,FALSE,20,away +320626020,2012,2,2012-06-26,20,Atlanta Dream,TRUE,5,home +320624006,2012,2,2012-06-24,17,San Antonio Silver Stars,TRUE,6,away +320624006,2012,2,2012-06-24,6,Los Angeles Sparks,FALSE,17,home +320624014,2012,2,2012-06-24,16,Washington Mystics,FALSE,14,away +320624014,2012,2,2012-06-24,14,Seattle Storm,TRUE,16,home +320624009,2012,2,2012-06-24,20,Atlanta Dream,TRUE,9,away +320624009,2012,2,2012-06-24,9,New York Liberty,FALSE,20,home +320623011,2012,2,2012-06-23,6,Los Angeles Sparks,TRUE,11,away +320623011,2012,2,2012-06-23,11,Phoenix Mercury,FALSE,6,home +320623003,2012,2,2012-06-23,5,Indiana Fever,TRUE,3,away +320623003,2012,2,2012-06-23,3,Tulsa Shock,FALSE,5,home +320623008,2012,2,2012-06-23,19,Chicago Sky,FALSE,8,away +320623008,2012,2,2012-06-23,8,Minnesota Lynx,TRUE,19,home +320622014,2012,2,2012-06-22,17,San Antonio Silver Stars,FALSE,14,away +320622014,2012,2,2012-06-22,14,Seattle Storm,TRUE,17,home +320621008,2012,2,2012-06-21,9,New York Liberty,FALSE,8,away +320621008,2012,2,2012-06-21,8,Minnesota Lynx,TRUE,9,home +320621005,2012,2,2012-06-21,18,Connecticut Sun,FALSE,5,away +320621005,2012,2,2012-06-21,5,Indiana Fever,TRUE,18,home +320620006,2012,2,2012-06-20,3,Tulsa Shock,FALSE,6,away +320620006,2012,2,2012-06-20,6,Los Angeles Sparks,TRUE,3,home +320620011,2012,2,2012-06-20,16,Washington Mystics,FALSE,11,away +320620011,2012,2,2012-06-20,11,Phoenix Mercury,TRUE,16,home +320619018,2012,2,2012-06-19,5,Indiana Fever,FALSE,18,away +320619018,2012,2,2012-06-19,18,Connecticut Sun,TRUE,5,home +320619020,2012,2,2012-06-19,9,New York Liberty,TRUE,20,away +320619020,2012,2,2012-06-19,20,Atlanta Dream,FALSE,9,home +320618006,2012,2,2012-06-18,16,Washington Mystics,FALSE,6,away +320618006,2012,2,2012-06-18,6,Los Angeles Sparks,TRUE,16,home +320617014,2012,2,2012-06-17,8,Minnesota Lynx,FALSE,14,away +320617014,2012,2,2012-06-17,14,Seattle Storm,TRUE,8,home +320617003,2012,2,2012-06-17,11,Phoenix Mercury,FALSE,3,away +320617003,2012,2,2012-06-17,3,Tulsa Shock,TRUE,11,home +320617020,2012,2,2012-06-17,18,Connecticut Sun,TRUE,20,away +320617020,2012,2,2012-06-17,20,Atlanta Dream,FALSE,18,home +320616017,2012,2,2012-06-16,6,Los Angeles Sparks,FALSE,17,away +320616017,2012,2,2012-06-16,17,San Antonio Silver Stars,TRUE,6,home +320616005,2012,2,2012-06-16,19,Chicago Sky,FALSE,5,away +320616005,2012,2,2012-06-16,5,Indiana Fever,TRUE,19,home +320615011,2012,2,2012-06-15,8,Minnesota Lynx,TRUE,11,away +320615011,2012,2,2012-06-15,11,Phoenix Mercury,FALSE,8,home +320615003,2012,2,2012-06-15,14,Seattle Storm,TRUE,3,away +320615003,2012,2,2012-06-15,3,Tulsa Shock,FALSE,14,home +320615020,2012,2,2012-06-15,6,Los Angeles Sparks,FALSE,20,away +320615020,2012,2,2012-06-15,20,Atlanta Dream,TRUE,6,home +320615016,2012,2,2012-06-15,5,Indiana Fever,FALSE,16,away +320615016,2012,2,2012-06-15,16,Washington Mystics,TRUE,5,home +320615018,2012,2,2012-06-15,9,New York Liberty,FALSE,18,away +320615018,2012,2,2012-06-15,18,Connecticut Sun,TRUE,9,home +320613018,2012,2,2012-06-13,6,Los Angeles Sparks,TRUE,18,away +320613018,2012,2,2012-06-13,18,Connecticut Sun,FALSE,6,home +320613019,2012,2,2012-06-13,14,Seattle Storm,FALSE,19,away +320613019,2012,2,2012-06-13,19,Chicago Sky,TRUE,14,home +320610018,2012,2,2012-06-10,20,Atlanta Dream,FALSE,18,away +320610018,2012,2,2012-06-10,18,Connecticut Sun,TRUE,20,home +320610009,2012,2,2012-06-10,19,Chicago Sky,TRUE,9,away +320610009,2012,2,2012-06-10,9,New York Liberty,FALSE,19,home +320609003,2012,2,2012-06-09,8,Minnesota Lynx,TRUE,3,away +320609003,2012,2,2012-06-09,3,Tulsa Shock,FALSE,8,home +320609017,2012,2,2012-06-09,14,Seattle Storm,FALSE,17,away +320609017,2012,2,2012-06-09,17,San Antonio Silver Stars,TRUE,14,home +320608006,2012,2,2012-06-08,11,Phoenix Mercury,FALSE,6,away +320608006,2012,2,2012-06-08,6,Los Angeles Sparks,TRUE,11,home +320608019,2012,2,2012-06-08,3,Tulsa Shock,FALSE,19,away +320608019,2012,2,2012-06-08,19,Chicago Sky,TRUE,3,home +320608020,2012,2,2012-06-08,17,San Antonio Silver Stars,FALSE,20,away +320608020,2012,2,2012-06-08,20,Atlanta Dream,TRUE,17,home +320608005,2012,2,2012-06-08,18,Connecticut Sun,TRUE,5,away +320608005,2012,2,2012-06-08,5,Indiana Fever,FALSE,18,home +320608016,2012,2,2012-06-08,9,New York Liberty,TRUE,16,away +320608016,2012,2,2012-06-08,16,Washington Mystics,FALSE,9,home +320606008,2012,2,2012-06-06,14,Seattle Storm,FALSE,8,away +320606008,2012,2,2012-06-06,8,Minnesota Lynx,TRUE,14,home +320605009,2012,2,2012-06-05,20,Atlanta Dream,FALSE,9,away +320605009,2012,2,2012-06-05,9,New York Liberty,TRUE,20,home +320603006,2012,2,2012-06-03,14,Seattle Storm,FALSE,6,away +320603006,2012,2,2012-06-03,6,Los Angeles Sparks,TRUE,14,home +320603008,2012,2,2012-06-03,17,San Antonio Silver Stars,FALSE,8,away +320603008,2012,2,2012-06-03,8,Minnesota Lynx,TRUE,17,home +320603009,2012,2,2012-06-03,5,Indiana Fever,FALSE,9,away +320603009,2012,2,2012-06-03,9,New York Liberty,TRUE,5,home +320603011,2012,2,2012-06-03,3,Tulsa Shock,FALSE,11,away +320603011,2012,2,2012-06-03,11,Phoenix Mercury,TRUE,3,home +320603018,2012,2,2012-06-03,16,Washington Mystics,FALSE,18,away +320603018,2012,2,2012-06-03,18,Connecticut Sun,TRUE,16,home +320602005,2012,2,2012-06-02,9,New York Liberty,FALSE,5,away +320602005,2012,2,2012-06-02,5,Indiana Fever,TRUE,9,home +320602020,2012,2,2012-06-02,19,Chicago Sky,TRUE,20,away +320602020,2012,2,2012-06-02,20,Atlanta Dream,FALSE,19,home +320601014,2012,2,2012-06-01,3,Tulsa Shock,FALSE,14,away +320601014,2012,2,2012-06-01,14,Seattle Storm,TRUE,3,home +320601019,2012,2,2012-06-01,16,Washington Mystics,FALSE,19,away +320601019,2012,2,2012-06-01,19,Chicago Sky,TRUE,16,home +320601017,2012,2,2012-06-01,11,Phoenix Mercury,FALSE,17,away +320601017,2012,2,2012-06-01,17,San Antonio Silver Stars,TRUE,11,home +320601018,2012,2,2012-06-01,8,Minnesota Lynx,TRUE,18,away +320601018,2012,2,2012-06-01,18,Connecticut Sun,FALSE,8,home +320531020,2012,2,2012-05-31,11,Phoenix Mercury,FALSE,20,away +320531020,2012,2,2012-05-31,20,Atlanta Dream,TRUE,11,home +320530017,2012,2,2012-05-30,19,Chicago Sky,TRUE,17,away +320530017,2012,2,2012-05-30,17,San Antonio Silver Stars,FALSE,19,home +320530016,2012,2,2012-05-30,8,Minnesota Lynx,TRUE,16,away +320530016,2012,2,2012-05-30,16,Washington Mystics,FALSE,8,home +320529006,2012,2,2012-05-29,3,Tulsa Shock,FALSE,6,away +320529006,2012,2,2012-05-29,6,Los Angeles Sparks,TRUE,3,home +320527008,2012,2,2012-05-27,14,Seattle Storm,FALSE,8,away +320527008,2012,2,2012-05-27,8,Minnesota Lynx,TRUE,14,home +320527020,2012,2,2012-05-27,5,Indiana Fever,TRUE,20,away +320527020,2012,2,2012-05-27,20,Atlanta Dream,FALSE,5,home +320526011,2012,2,2012-05-26,6,Los Angeles Sparks,TRUE,11,away +320526011,2012,2,2012-05-26,11,Phoenix Mercury,FALSE,6,home +320526016,2012,2,2012-05-26,3,Tulsa Shock,FALSE,16,away +320526016,2012,2,2012-05-26,16,Washington Mystics,TRUE,3,home +320525019,2012,2,2012-05-25,5,Indiana Fever,TRUE,19,away +320525019,2012,2,2012-05-25,19,Chicago Sky,FALSE,5,home +320525020,2012,2,2012-05-25,9,New York Liberty,FALSE,20,away +320525020,2012,2,2012-05-25,20,Atlanta Dream,TRUE,9,home +320525018,2012,2,2012-05-25,17,San Antonio Silver Stars,FALSE,18,away +320525018,2012,2,2012-05-25,18,Connecticut Sun,TRUE,17,home +320524008,2012,2,2012-05-24,6,Los Angeles Sparks,FALSE,8,away +320524008,2012,2,2012-05-24,8,Minnesota Lynx,TRUE,6,home +320522006,2012,2,2012-05-22,14,Seattle Storm,FALSE,6,away +320522006,2012,2,2012-05-22,6,Los Angeles Sparks,TRUE,14,home +320522003,2012,2,2012-05-22,11,Phoenix Mercury,TRUE,3,away +320522003,2012,2,2012-05-22,3,Tulsa Shock,FALSE,11,home +320522009,2012,2,2012-05-22,8,Minnesota Lynx,TRUE,9,away +320522009,2012,2,2012-05-22,9,New York Liberty,FALSE,8,home +320520018,2012,2,2012-05-20,9,New York Liberty,FALSE,18,away +320520018,2012,2,2012-05-20,18,Connecticut Sun,TRUE,9,home +320520008,2012,2,2012-05-20,11,Phoenix Mercury,FALSE,8,away +320520008,2012,2,2012-05-20,8,Minnesota Lynx,TRUE,11,home +320519003,2012,2,2012-05-19,17,San Antonio Silver Stars,TRUE,3,away +320519003,2012,2,2012-05-19,3,Tulsa Shock,FALSE,17,home +320519005,2012,2,2012-05-19,20,Atlanta Dream,FALSE,5,away +320519005,2012,2,2012-05-19,5,Indiana Fever,TRUE,20,home +320519016,2012,2,2012-05-19,19,Chicago Sky,TRUE,16,away +320519016,2012,2,2012-05-19,16,Washington Mystics,FALSE,19,home +320519009,2012,2,2012-05-19,18,Connecticut Sun,TRUE,9,away +320519009,2012,2,2012-05-19,9,New York Liberty,FALSE,18,home +320518014,2012,2,2012-05-18,6,Los Angeles Sparks,TRUE,14,away +320518014,2012,2,2012-05-18,14,Seattle Storm,FALSE,6,home +400505696,2013,3,2013-10-10,8,Minnesota Lynx,TRUE,20,away +400505696,2013,3,2013-10-10,20,Atlanta Dream,FALSE,8,home +400505695,2013,3,2013-10-08,20,Atlanta Dream,FALSE,8,away +400505695,2013,3,2013-10-08,8,Minnesota Lynx,TRUE,20,home +400505694,2013,3,2013-10-06,20,Atlanta Dream,FALSE,8,away +400505694,2013,3,2013-10-06,8,Minnesota Lynx,TRUE,20,home +400499747,2013,3,2013-09-29,8,Minnesota Lynx,TRUE,11,away +400499747,2013,3,2013-09-29,11,Phoenix Mercury,FALSE,8,home +400499744,2013,3,2013-09-29,20,Atlanta Dream,TRUE,5,away +400499744,2013,3,2013-09-29,5,Indiana Fever,FALSE,20,home +400499746,2013,3,2013-09-26,11,Phoenix Mercury,FALSE,8,away +400499746,2013,3,2013-09-26,8,Minnesota Lynx,TRUE,11,home +400499743,2013,3,2013-09-26,5,Indiana Fever,FALSE,20,away +400499743,2013,3,2013-09-26,20,Atlanta Dream,TRUE,5,home +400496779,2013,3,2013-09-23,11,Phoenix Mercury,TRUE,6,away +400496779,2013,3,2013-09-23,6,Los Angeles Sparks,FALSE,11,home +400496776,2013,3,2013-09-23,16,Washington Mystics,FALSE,20,away +400496776,2013,3,2013-09-23,20,Atlanta Dream,TRUE,16,home +400496784,2013,3,2013-09-22,8,Minnesota Lynx,TRUE,14,away +400496784,2013,3,2013-09-22,14,Seattle Storm,FALSE,8,home +400496781,2013,3,2013-09-22,19,Chicago Sky,FALSE,5,away +400496781,2013,3,2013-09-22,5,Indiana Fever,TRUE,19,home +400496778,2013,3,2013-09-21,6,Los Angeles Sparks,TRUE,11,away +400496778,2013,3,2013-09-21,11,Phoenix Mercury,FALSE,6,home +400496775,2013,3,2013-09-21,20,Atlanta Dream,TRUE,16,away +400496775,2013,3,2013-09-21,16,Washington Mystics,FALSE,20,home +400496783,2013,3,2013-09-20,14,Seattle Storm,FALSE,8,away +400496783,2013,3,2013-09-20,8,Minnesota Lynx,TRUE,14,home +400496780,2013,3,2013-09-20,5,Indiana Fever,TRUE,19,away 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Sparks,TRUE,14,away +400445785,2013,2,2013-07-20,14,Seattle Storm,FALSE,6,home +400445779,2013,2,2013-07-20,9,New York Liberty,FALSE,19,away +400445779,2013,2,2013-07-20,19,Chicago Sky,TRUE,9,home +400445782,2013,2,2013-07-20,18,Connecticut Sun,FALSE,17,away +400445782,2013,2,2013-07-20,17,San Antonio Silver Stars,TRUE,18,home +400445773,2013,2,2013-07-19,8,Minnesota Lynx,TRUE,17,away +400445773,2013,2,2013-07-19,17,San Antonio Silver Stars,FALSE,8,home +400445775,2013,2,2013-07-19,18,Connecticut Sun,FALSE,3,away +400445775,2013,2,2013-07-19,3,Tulsa Shock,TRUE,18,home +400445765,2013,2,2013-07-19,16,Washington Mystics,FALSE,5,away +400445765,2013,2,2013-07-19,5,Indiana Fever,TRUE,16,home +400445761,2013,2,2013-07-18,11,Phoenix Mercury,TRUE,6,away +400445761,2013,2,2013-07-18,6,Los Angeles Sparks,FALSE,11,home +400445732,2013,2,2013-07-18,19,Chicago Sky,TRUE,9,away +400445732,2013,2,2013-07-18,9,New York Liberty,FALSE,19,home +400445727,2013,2,2013-07-17,20,Atlanta Dream,FALSE,6,away 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+400445795,2013,2,2013-07-02,19,Chicago Sky,FALSE,14,home +400445798,2013,2,2013-07-02,3,Tulsa Shock,FALSE,18,away +400445798,2013,2,2013-07-02,18,Connecticut Sun,TRUE,3,home +400445784,2013,2,2013-06-30,14,Seattle Storm,FALSE,5,away +400445784,2013,2,2013-06-30,5,Indiana Fever,TRUE,14,home +400445781,2013,2,2013-06-30,3,Tulsa Shock,FALSE,16,away +400445781,2013,2,2013-06-30,16,Washington Mystics,TRUE,3,home +400445776,2013,2,2013-06-30,17,San Antonio Silver Stars,FALSE,20,away +400445776,2013,2,2013-06-30,20,Atlanta Dream,TRUE,17,home +400445772,2013,2,2013-06-29,6,Los Angeles Sparks,FALSE,19,away +400445772,2013,2,2013-06-29,19,Chicago Sky,TRUE,6,home +400445771,2013,2,2013-06-29,11,Phoenix Mercury,TRUE,18,away +400445771,2013,2,2013-06-29,18,Connecticut Sun,FALSE,11,home +400445769,2013,2,2013-06-28,9,New York Liberty,TRUE,14,away +400445769,2013,2,2013-06-28,14,Seattle Storm,FALSE,9,home +400445767,2013,2,2013-06-28,6,Los Angeles Sparks,FALSE,8,away +400445767,2013,2,2013-06-28,8,Minnesota Lynx,TRUE,6,home +400445762,2013,2,2013-06-28,16,Washington Mystics,FALSE,20,away +400445762,2013,2,2013-06-28,20,Atlanta Dream,TRUE,16,home +400445759,2013,2,2013-06-28,3,Tulsa Shock,FALSE,5,away +400445759,2013,2,2013-06-28,5,Indiana Fever,TRUE,3,home +400445757,2013,2,2013-06-27,11,Phoenix Mercury,TRUE,16,away +400445757,2013,2,2013-06-27,16,Washington Mystics,FALSE,11,home +400445733,2013,2,2013-06-26,9,New York Liberty,FALSE,19,away +400445733,2013,2,2013-06-26,19,Chicago Sky,TRUE,9,home +400445729,2013,2,2013-06-25,11,Phoenix Mercury,TRUE,17,away +400445729,2013,2,2013-06-25,17,San Antonio Silver Stars,FALSE,11,home +400445723,2013,2,2013-06-25,5,Indiana Fever,FALSE,20,away +400445723,2013,2,2013-06-25,20,Atlanta Dream,TRUE,5,home +400445721,2013,2,2013-06-23,16,Washington Mystics,FALSE,6,away +400445721,2013,2,2013-06-23,6,Los Angeles Sparks,TRUE,16,home +400445717,2013,2,2013-06-23,3,Tulsa Shock,FALSE,8,away +400445717,2013,2,2013-06-23,8,Minnesota Lynx,TRUE,3,home +400445709,2013,2,2013-06-23,20,Atlanta Dream,TRUE,18,away +400445709,2013,2,2013-06-23,18,Connecticut Sun,FALSE,20,home +400445714,2013,2,2013-06-23,17,San Antonio Silver Stars,TRUE,9,away +400445714,2013,2,2013-06-23,9,New York Liberty,FALSE,17,home +400445703,2013,2,2013-06-22,14,Seattle Storm,FALSE,3,away +400445703,2013,2,2013-06-22,3,Tulsa Shock,TRUE,14,home +400445701,2013,2,2013-06-22,19,Chicago Sky,TRUE,5,away +400445701,2013,2,2013-06-22,5,Indiana Fever,FALSE,19,home +400445699,2013,2,2013-06-21,8,Minnesota Lynx,FALSE,6,away +400445699,2013,2,2013-06-21,6,Los Angeles Sparks,TRUE,8,home +400445698,2013,2,2013-06-21,16,Washington Mystics,FALSE,11,away +400445698,2013,2,2013-06-21,11,Phoenix Mercury,TRUE,16,home +400445697,2013,2,2013-06-21,14,Seattle Storm,TRUE,17,away +400445697,2013,2,2013-06-21,17,San Antonio Silver Stars,FALSE,14,home +400445884,2013,2,2013-06-20,19,Chicago Sky,FALSE,3,away +400445884,2013,2,2013-06-20,3,Tulsa Shock,TRUE,19,home +400445882,2013,2,2013-06-19,8,Minnesota Lynx,TRUE,11,away +400445882,2013,2,2013-06-19,11,Phoenix Mercury,FALSE,8,home +400445879,2013,2,2013-06-18,16,Washington Mystics,FALSE,14,away +400445879,2013,2,2013-06-18,14,Seattle Storm,TRUE,16,home +400445854,2013,2,2013-06-16,14,Seattle Storm,TRUE,18,away +400445854,2013,2,2013-06-16,18,Connecticut Sun,FALSE,14,home +400445852,2013,2,2013-06-16,11,Phoenix Mercury,TRUE,3,away +400445852,2013,2,2013-06-16,3,Tulsa Shock,FALSE,11,home +400445847,2013,2,2013-06-16,19,Chicago Sky,FALSE,20,away +400445847,2013,2,2013-06-16,20,Atlanta Dream,TRUE,19,home +400445843,2013,2,2013-06-16,5,Indiana Fever,FALSE,16,away +400445843,2013,2,2013-06-16,16,Washington Mystics,TRUE,5,home +400445841,2013,2,2013-06-15,17,San Antonio Silver Stars,FALSE,6,away +400445841,2013,2,2013-06-15,6,Los Angeles Sparks,TRUE,17,home +400445835,2013,2,2013-06-14,6,Los Angeles Sparks,FALSE,11,away +400445835,2013,2,2013-06-14,11,Phoenix Mercury,TRUE,6,home +400445831,2013,2,2013-06-14,8,Minnesota Lynx,TRUE,3,away +400445831,2013,2,2013-06-14,3,Tulsa Shock,FALSE,8,home +400445823,2013,2,2013-06-14,14,Seattle Storm,FALSE,20,away +400445823,2013,2,2013-06-14,20,Atlanta Dream,TRUE,14,home +400445826,2013,2,2013-06-14,18,Connecticut Sun,FALSE,9,away +400445826,2013,2,2013-06-14,9,New York Liberty,TRUE,18,home +400445818,2013,2,2013-06-12,18,Connecticut Sun,TRUE,5,away +400445818,2013,2,2013-06-12,5,Indiana Fever,FALSE,18,home +400445815,2013,2,2013-06-11,17,San Antonio Silver Stars,FALSE,8,away +400445815,2013,2,2013-06-11,8,Minnesota Lynx,TRUE,17,home +400445810,2013,2,2013-06-09,17,San Antonio Silver Stars,FALSE,19,away +400445810,2013,2,2013-06-09,19,Chicago Sky,TRUE,17,home +400445807,2013,2,2013-06-09,20,Atlanta Dream,FALSE,9,away +400445807,2013,2,2013-06-09,9,New York Liberty,TRUE,20,home +400445797,2013,2,2013-06-08,3,Tulsa Shock,FALSE,6,away +400445797,2013,2,2013-06-08,6,Los Angeles Sparks,TRUE,3,home +400445794,2013,2,2013-06-08,8,Minnesota Lynx,FALSE,16,away +400445794,2013,2,2013-06-08,16,Washington Mystics,TRUE,8,home +400445792,2013,2,2013-06-08,11,Phoenix Mercury,TRUE,5,away +400445792,2013,2,2013-06-08,5,Indiana Fever,FALSE,11,home +400445790,2013,2,2013-06-07,3,Tulsa Shock,TRUE,14,away +400445790,2013,2,2013-06-07,14,Seattle Storm,FALSE,3,home +400445788,2013,2,2013-06-07,19,Chicago Sky,FALSE,17,away +400445788,2013,2,2013-06-07,17,San Antonio Silver Stars,TRUE,19,home +400445787,2013,2,2013-06-07,9,New York Liberty,FALSE,20,away +400445787,2013,2,2013-06-07,20,Atlanta Dream,TRUE,9,home +400445783,2013,2,2013-06-07,16,Washington Mystics,TRUE,18,away +400445783,2013,2,2013-06-07,18,Connecticut Sun,FALSE,16,home +400445780,2013,2,2013-06-06,11,Phoenix Mercury,FALSE,8,away +400445780,2013,2,2013-06-06,8,Minnesota Lynx,TRUE,11,home +400445777,2013,2,2013-06-05,5,Indiana Fever,FALSE,9,away +400445777,2013,2,2013-06-05,9,New York Liberty,TRUE,5,home +400445766,2013,2,2013-06-02,11,Phoenix Mercury,FALSE,14,away +400445766,2013,2,2013-06-02,14,Seattle Storm,TRUE,11,home +400445764,2013,2,2013-06-02,3,Tulsa Shock,FALSE,19,away +400445764,2013,2,2013-06-02,19,Chicago Sky,TRUE,3,home +400445760,2013,2,2013-06-02,20,Atlanta Dream,TRUE,16,away +400445760,2013,2,2013-06-02,16,Washington Mystics,FALSE,20,home +400445734,2013,2,2013-06-01,18,Connecticut Sun,FALSE,8,away +400445734,2013,2,2013-06-01,8,Minnesota Lynx,TRUE,18,home +400445756,2013,2,2013-06-01,6,Los Angeles Sparks,FALSE,17,away +400445756,2013,2,2013-06-01,17,San Antonio Silver Stars,TRUE,6,home +400445731,2013,2,2013-05-31,18,Connecticut Sun,FALSE,19,away +400445731,2013,2,2013-05-31,19,Chicago Sky,TRUE,18,home +400445728,2013,2,2013-05-31,3,Tulsa Shock,FALSE,9,away +400445728,2013,2,2013-05-31,9,New York Liberty,TRUE,3,home +400445726,2013,2,2013-05-31,20,Atlanta Dream,TRUE,5,away +400445726,2013,2,2013-05-31,5,Indiana Fever,FALSE,20,home +400445722,2013,2,2013-05-27,19,Chicago Sky,TRUE,11,away +400445722,2013,2,2013-05-27,11,Phoenix Mercury,FALSE,19,home +400445718,2013,2,2013-05-27,16,Washington Mystics,TRUE,3,away +400445718,2013,2,2013-05-27,3,Tulsa Shock,FALSE,16,home +400445715,2013,2,2013-05-26,14,Seattle Storm,FALSE,6,away +400445715,2013,2,2013-05-26,6,Los Angeles Sparks,TRUE,14,home +400445707,2013,2,2013-05-25,3,Tulsa Shock,FALSE,20,away +400445707,2013,2,2013-05-25,20,Atlanta Dream,TRUE,3,home +400445711,2013,2,2013-05-25,9,New York Liberty,FALSE,18,away +400445711,2013,2,2013-05-25,18,Connecticut Sun,TRUE,9,home +400445705,2013,2,2013-05-24,5,Indiana Fever,TRUE,17,away +400445705,2013,2,2013-05-24,17,San Antonio Silver Stars,FALSE,5,home +400582775,2014,3,2014-09-12,11,Phoenix Mercury,TRUE,19,away +400582775,2014,3,2014-09-12,19,Chicago Sky,FALSE,11,home +400582774,2014,3,2014-09-09,19,Chicago Sky,FALSE,11,away +400582774,2014,3,2014-09-09,11,Phoenix Mercury,TRUE,19,home +400582773,2014,3,2014-09-07,19,Chicago Sky,FALSE,11,away +400582773,2014,3,2014-09-07,11,Phoenix Mercury,TRUE,19,home +400581823,2014,3,2014-09-03,19,Chicago Sky,TRUE,5,away +400581823,2014,3,2014-09-03,5,Indiana Fever,FALSE,19,home +400581629,2014,3,2014-09-02,8,Minnesota Lynx,FALSE,11,away +400581629,2014,3,2014-09-02,11,Phoenix Mercury,TRUE,8,home +400581822,2014,3,2014-09-01,5,Indiana Fever,FALSE,19,away +400581822,2014,3,2014-09-01,19,Chicago Sky,TRUE,5,home +400581628,2014,3,2014-08-31,11,Phoenix Mercury,FALSE,8,away +400581628,2014,3,2014-08-31,8,Minnesota Lynx,TRUE,11,home +400581821,2014,3,2014-08-30,19,Chicago Sky,FALSE,5,away +400581821,2014,3,2014-08-30,5,Indiana Fever,TRUE,19,home +400581627,2014,3,2014-08-29,8,Minnesota Lynx,FALSE,11,away +400581627,2014,3,2014-08-29,11,Phoenix Mercury,TRUE,8,home +400580069,2014,3,2014-08-26,19,Chicago Sky,TRUE,20,away +400580069,2014,3,2014-08-26,20,Atlanta Dream,FALSE,19,home +400578291,2014,3,2014-08-24,11,Phoenix Mercury,TRUE,6,away +400578291,2014,3,2014-08-24,6,Los Angeles Sparks,FALSE,11,home +400580068,2014,3,2014-08-24,20,Atlanta Dream,TRUE,19,away +400580068,2014,3,2014-08-24,19,Chicago Sky,FALSE,20,home +400578288,2014,3,2014-08-23,8,Minnesota Lynx,TRUE,17,away +400578288,2014,3,2014-08-23,17,San Antonio Stars,FALSE,8,home +400580071,2014,3,2014-08-23,5,Indiana Fever,TRUE,16,away +400580071,2014,3,2014-08-23,16,Washington Mystics,FALSE,5,home +400578290,2014,3,2014-08-22,6,Los Angeles Sparks,FALSE,11,away +400578290,2014,3,2014-08-22,11,Phoenix Mercury,TRUE,6,home +400580067,2014,3,2014-08-22,19,Chicago Sky,TRUE,20,away +400580067,2014,3,2014-08-22,20,Atlanta Dream,FALSE,19,home +400578287,2014,3,2014-08-21,17,San Antonio Stars,FALSE,8,away +400578287,2014,3,2014-08-21,8,Minnesota Lynx,TRUE,17,home +400580070,2014,3,2014-08-21,16,Washington Mystics,FALSE,5,away +400580070,2014,3,2014-08-21,5,Indiana Fever,TRUE,16,home +400539664,2014,2,2014-08-17,11,Phoenix Mercury,TRUE,14,away +400539664,2014,2,2014-08-17,14,Seattle Storm,FALSE,11,home +400539661,2014,2,2014-08-17,17,San Antonio Stars,TRUE,19,away +400539661,2014,2,2014-08-17,19,Chicago Sky,FALSE,17,home +400539662,2014,2,2014-08-17,5,Indiana Fever,FALSE,9,away +400539662,2014,2,2014-08-17,9,New York Liberty,TRUE,5,home +400539660,2014,2,2014-08-17,20,Atlanta Dream,FALSE,18,away +400539660,2014,2,2014-08-17,18,Connecticut Sun,TRUE,20,home +400539659,2014,2,2014-08-16,6,Los Angeles Sparks,FALSE,11,away +400539659,2014,2,2014-08-16,11,Phoenix Mercury,TRUE,6,home +400544875,2014,2,2014-08-16,3,Tulsa Shock,FALSE,8,away +400544875,2014,2,2014-08-16,8,Minnesota Lynx,TRUE,3,home +400539657,2014,2,2014-08-16,19,Chicago Sky,FALSE,5,away +400539657,2014,2,2014-08-16,5,Indiana Fever,TRUE,19,home +400539658,2014,2,2014-08-16,9,New York Liberty,TRUE,16,away +400539658,2014,2,2014-08-16,16,Washington Mystics,FALSE,9,home +400539656,2014,2,2014-08-15,14,Seattle Storm,FALSE,6,away +400539656,2014,2,2014-08-15,6,Los Angeles Sparks,TRUE,14,home +400539655,2014,2,2014-08-15,8,Minnesota Lynx,FALSE,17,away +400539655,2014,2,2014-08-15,17,San Antonio Stars,TRUE,8,home +400539654,2014,2,2014-08-15,3,Tulsa Shock,FALSE,20,away +400539654,2014,2,2014-08-15,20,Atlanta Dream,TRUE,3,home +400539653,2014,2,2014-08-15,16,Washington Mystics,TRUE,18,away +400539653,2014,2,2014-08-15,18,Connecticut Sun,FALSE,16,home +400539652,2014,2,2014-08-14,9,New York Liberty,FALSE,5,away +400539652,2014,2,2014-08-14,5,Indiana Fever,TRUE,9,home +400539651,2014,2,2014-08-13,11,Phoenix Mercury,FALSE,20,away +400539651,2014,2,2014-08-13,20,Atlanta Dream,TRUE,11,home +400539650,2014,2,2014-08-13,19,Chicago Sky,TRUE,16,away +400539650,2014,2,2014-08-13,16,Washington Mystics,FALSE,19,home +400539649,2014,2,2014-08-12,6,Los Angeles Sparks,TRUE,8,away +400539649,2014,2,2014-08-12,8,Minnesota Lynx,FALSE,6,home +400539648,2014,2,2014-08-12,11,Phoenix Mercury,TRUE,9,away +400539648,2014,2,2014-08-12,9,New York Liberty,FALSE,11,home +400539647,2014,2,2014-08-10,3,Tulsa Shock,FALSE,14,away +400539647,2014,2,2014-08-10,14,Seattle Storm,TRUE,3,home +400539646,2014,2,2014-08-10,20,Atlanta Dream,FALSE,19,away +400539646,2014,2,2014-08-10,19,Chicago Sky,TRUE,20,home +400539645,2014,2,2014-08-10,6,Los Angeles Sparks,FALSE,17,away +400539645,2014,2,2014-08-10,17,San Antonio Stars,TRUE,6,home +400539643,2014,2,2014-08-10,16,Washington Mystics,FALSE,18,away +400539643,2014,2,2014-08-10,18,Connecticut Sun,TRUE,16,home +400539644,2014,2,2014-08-10,5,Indiana Fever,TRUE,9,away +400539644,2014,2,2014-08-10,9,New York Liberty,FALSE,5,home +400539642,2014,2,2014-08-09,8,Minnesota Lynx,FALSE,11,away +400539642,2014,2,2014-08-09,11,Phoenix Mercury,TRUE,8,home +400539641,2014,2,2014-08-08,20,Atlanta Dream,FALSE,6,away +400539641,2014,2,2014-08-08,6,Los Angeles Sparks,TRUE,20,home +400539640,2014,2,2014-08-08,17,San Antonio Stars,FALSE,3,away +400539640,2014,2,2014-08-08,3,Tulsa Shock,TRUE,17,home +400539639,2014,2,2014-08-08,18,Connecticut Sun,FALSE,9,away +400539639,2014,2,2014-08-08,9,New York Liberty,TRUE,18,home +400539638,2014,2,2014-08-08,16,Washington Mystics,TRUE,5,away +400539638,2014,2,2014-08-08,5,Indiana Fever,FALSE,16,home +400539637,2014,2,2014-08-07,20,Atlanta Dream,FALSE,14,away +400539637,2014,2,2014-08-07,14,Seattle Storm,TRUE,20,home +400539635,2014,2,2014-08-07,19,Chicago Sky,FALSE,8,away +400539635,2014,2,2014-08-07,8,Minnesota Lynx,TRUE,19,home +400539636,2014,2,2014-08-07,11,Phoenix Mercury,TRUE,17,away +400539636,2014,2,2014-08-07,17,San Antonio Stars,FALSE,11,home +400539634,2014,2,2014-08-05,3,Tulsa Shock,TRUE,6,away +400539634,2014,2,2014-08-05,6,Los Angeles Sparks,FALSE,3,home +400539633,2014,2,2014-08-05,20,Atlanta Dream,FALSE,11,away +400539633,2014,2,2014-08-05,11,Phoenix Mercury,TRUE,20,home +400539630,2014,2,2014-08-05,19,Chicago Sky,TRUE,18,away +400539630,2014,2,2014-08-05,18,Connecticut Sun,FALSE,19,home +400539631,2014,2,2014-08-05,8,Minnesota Lynx,TRUE,5,away +400539631,2014,2,2014-08-05,5,Indiana Fever,FALSE,8,home +400539632,2014,2,2014-08-05,9,New York Liberty,FALSE,16,away +400539632,2014,2,2014-08-05,16,Washington Mystics,TRUE,9,home +400539629,2014,2,2014-08-03,17,San Antonio Stars,FALSE,14,away +400539629,2014,2,2014-08-03,14,Seattle Storm,TRUE,17,home +400539628,2014,2,2014-08-03,16,Washington Mystics,FALSE,19,away +400539628,2014,2,2014-08-03,19,Chicago Sky,TRUE,16,home +400539627,2014,2,2014-08-03,18,Connecticut Sun,FALSE,6,away +400539627,2014,2,2014-08-03,6,Los Angeles Sparks,TRUE,18,home +400539626,2014,2,2014-08-03,9,New York Liberty,TRUE,20,away +400539626,2014,2,2014-08-03,20,Atlanta Dream,FALSE,9,home +400539625,2014,2,2014-08-02,5,Indiana Fever,FALSE,11,away +400539625,2014,2,2014-08-02,11,Phoenix Mercury,TRUE,5,home +400539624,2014,2,2014-08-02,8,Minnesota Lynx,TRUE,3,away +400539624,2014,2,2014-08-02,3,Tulsa Shock,FALSE,8,home +400539623,2014,2,2014-08-01,18,Connecticut Sun,TRUE,17,away +400539623,2014,2,2014-08-01,17,San Antonio Stars,FALSE,18,home +400539622,2014,2,2014-07-31,5,Indiana Fever,TRUE,14,away +400539622,2014,2,2014-07-31,14,Seattle Storm,FALSE,5,home +400539619,2014,2,2014-07-31,9,New York Liberty,FALSE,19,away +400539619,2014,2,2014-07-31,19,Chicago Sky,TRUE,9,home +400539620,2014,2,2014-07-31,11,Phoenix Mercury,FALSE,8,away +400539620,2014,2,2014-07-31,8,Minnesota Lynx,TRUE,11,home +400539621,2014,2,2014-07-31,20,Atlanta Dream,TRUE,3,away +400539621,2014,2,2014-07-31,3,Tulsa Shock,FALSE,20,home +400539618,2014,2,2014-07-29,6,Los Angeles Sparks,FALSE,11,away +400539618,2014,2,2014-07-29,11,Phoenix Mercury,TRUE,6,home +400539616,2014,2,2014-07-29,19,Chicago Sky,FALSE,17,away +400539616,2014,2,2014-07-29,17,San Antonio Stars,TRUE,19,home +400539617,2014,2,2014-07-29,14,Seattle Storm,FALSE,3,away +400539617,2014,2,2014-07-29,3,Tulsa Shock,TRUE,14,home +400539615,2014,2,2014-07-29,16,Washington Mystics,FALSE,9,away +400539615,2014,2,2014-07-29,9,New York Liberty,TRUE,16,home +400539614,2014,2,2014-07-29,18,Connecticut Sun,FALSE,20,away +400539614,2014,2,2014-07-29,20,Atlanta Dream,TRUE,18,home +400539613,2014,2,2014-07-28,5,Indiana Fever,FALSE,6,away +400539613,2014,2,2014-07-28,6,Los Angeles Sparks,TRUE,5,home +400539612,2014,2,2014-07-27,19,Chicago Sky,FALSE,3,away +400539612,2014,2,2014-07-27,3,Tulsa Shock,TRUE,19,home +400539611,2014,2,2014-07-27,20,Atlanta Dream,FALSE,16,away +400539611,2014,2,2014-07-27,16,Washington Mystics,TRUE,20,home +400539610,2014,2,2014-07-27,8,Minnesota Lynx,TRUE,18,away +400539610,2014,2,2014-07-27,18,Connecticut Sun,FALSE,8,home +400539608,2014,2,2014-07-26,9,New York Liberty,FALSE,11,away +400539608,2014,2,2014-07-26,11,Phoenix Mercury,TRUE,9,home +400539607,2014,2,2014-07-26,5,Indiana Fever,TRUE,17,away +400539607,2014,2,2014-07-26,17,San Antonio Stars,FALSE,5,home +400539609,2014,2,2014-07-26,6,Los Angeles Sparks,TRUE,14,away 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+400539591,2014,2,2014-07-16,3,Tulsa Shock,FALSE,8,away +400539591,2014,2,2014-07-16,8,Minnesota Lynx,TRUE,3,home +400539590,2014,2,2014-07-16,20,Atlanta Dream,FALSE,9,away +400539590,2014,2,2014-07-16,9,New York Liberty,TRUE,20,home +400539589,2014,2,2014-07-15,16,Washington Mystics,FALSE,11,away +400539589,2014,2,2014-07-15,11,Phoenix Mercury,TRUE,16,home +400539588,2014,2,2014-07-15,6,Los Angeles Sparks,TRUE,5,away +400539588,2014,2,2014-07-15,5,Indiana Fever,FALSE,6,home +400539587,2014,2,2014-07-15,18,Connecticut Sun,TRUE,14,away +400539587,2014,2,2014-07-15,14,Seattle Storm,FALSE,18,home +400539585,2014,2,2014-07-13,17,San Antonio Stars,FALSE,11,away +400539585,2014,2,2014-07-13,11,Phoenix Mercury,TRUE,17,home +400539586,2014,2,2014-07-13,19,Chicago Sky,FALSE,20,away +400539586,2014,2,2014-07-13,20,Atlanta Dream,TRUE,19,home +400539584,2014,2,2014-07-13,14,Seattle Storm,FALSE,8,away +400539584,2014,2,2014-07-13,8,Minnesota Lynx,TRUE,14,home +400539583,2014,2,2014-07-13,6,Los Angeles Sparks,TRUE,18,away +400539583,2014,2,2014-07-13,18,Connecticut Sun,FALSE,6,home +400539582,2014,2,2014-07-12,16,Washington Mystics,TRUE,3,away +400539582,2014,2,2014-07-12,3,Tulsa Shock,FALSE,16,home +400539581,2014,2,2014-07-12,20,Atlanta Dream,TRUE,5,away +400539581,2014,2,2014-07-12,5,Indiana Fever,FALSE,20,home +400539580,2014,2,2014-07-11,11,Phoenix Mercury,TRUE,19,away +400539580,2014,2,2014-07-11,19,Chicago Sky,FALSE,11,home +400539579,2014,2,2014-07-11,14,Seattle Storm,TRUE,17,away +400539579,2014,2,2014-07-11,17,San Antonio Stars,FALSE,14,home +400539578,2014,2,2014-07-11,6,Los Angeles Sparks,TRUE,9,away +400539578,2014,2,2014-07-11,9,New York Liberty,FALSE,6,home +400539577,2014,2,2014-07-10,8,Minnesota Lynx,TRUE,3,away +400539577,2014,2,2014-07-10,3,Tulsa Shock,FALSE,8,home +400539576,2014,2,2014-07-10,18,Connecticut Sun,FALSE,5,away +400539576,2014,2,2014-07-10,5,Indiana Fever,TRUE,18,home +400539575,2014,2,2014-07-09,9,New York Liberty,FALSE,17,away 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Storm,TRUE,19,home +400539566,2014,2,2014-07-05,18,Connecticut Sun,TRUE,3,away +400539566,2014,2,2014-07-05,3,Tulsa Shock,FALSE,18,home +400539565,2014,2,2014-07-05,16,Washington Mystics,FALSE,20,away +400539565,2014,2,2014-07-05,20,Atlanta Dream,TRUE,16,home +400539564,2014,2,2014-07-05,17,San Antonio Stars,TRUE,5,away +400539564,2014,2,2014-07-05,5,Indiana Fever,FALSE,17,home +400539563,2014,2,2014-07-03,6,Los Angeles Sparks,TRUE,14,away +400539563,2014,2,2014-07-03,14,Seattle Storm,FALSE,6,home +400539562,2014,2,2014-07-03,17,San Antonio Stars,FALSE,8,away +400539562,2014,2,2014-07-03,8,Minnesota Lynx,TRUE,17,home +400539561,2014,2,2014-07-03,3,Tulsa Shock,TRUE,18,away +400539561,2014,2,2014-07-03,18,Connecticut Sun,FALSE,3,home +400539560,2014,2,2014-07-02,19,Chicago Sky,FALSE,11,away +400539560,2014,2,2014-07-02,11,Phoenix Mercury,TRUE,19,home +400539559,2014,2,2014-07-02,5,Indiana Fever,TRUE,16,away +400539559,2014,2,2014-07-02,16,Washington Mystics,FALSE,5,home 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Shock,TRUE,5,away +400539543,2014,2,2014-06-25,5,Indiana Fever,FALSE,3,home +400539541,2014,2,2014-06-24,14,Seattle Storm,FALSE,6,away +400539541,2014,2,2014-06-24,6,Los Angeles Sparks,TRUE,14,home +400539540,2014,2,2014-06-24,16,Washington Mystics,TRUE,17,away +400539540,2014,2,2014-06-24,17,San Antonio Stars,FALSE,16,home +400539539,2014,2,2014-06-22,16,Washington Mystics,FALSE,14,away +400539539,2014,2,2014-06-22,14,Seattle Storm,TRUE,16,home +400539538,2014,2,2014-06-22,5,Indiana Fever,FALSE,8,away +400539538,2014,2,2014-06-22,8,Minnesota Lynx,TRUE,5,home +400539537,2014,2,2014-06-22,17,San Antonio Stars,TRUE,6,away +400539537,2014,2,2014-06-22,6,Los Angeles Sparks,FALSE,17,home +400539536,2014,2,2014-06-22,20,Atlanta Dream,FALSE,9,away +400539536,2014,2,2014-06-22,9,New York Liberty,TRUE,20,home +400539535,2014,2,2014-06-22,3,Tulsa Shock,TRUE,19,away +400539535,2014,2,2014-06-22,19,Chicago Sky,FALSE,3,home +400539534,2014,2,2014-06-20,3,Tulsa Shock,FALSE,11,away 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Antonio Stars,FALSE,14,home +400539518,2014,2,2014-06-13,6,Los Angeles Sparks,FALSE,3,away +400539518,2014,2,2014-06-13,3,Tulsa Shock,TRUE,6,home +400539515,2014,2,2014-06-13,8,Minnesota Lynx,FALSE,20,away +400539515,2014,2,2014-06-13,20,Atlanta Dream,TRUE,8,home +400539516,2014,2,2014-06-13,18,Connecticut Sun,TRUE,9,away +400539516,2014,2,2014-06-13,9,New York Liberty,FALSE,18,home +400539514,2014,2,2014-06-13,19,Chicago Sky,FALSE,16,away +400539514,2014,2,2014-06-13,16,Washington Mystics,TRUE,19,home +400539513,2014,2,2014-06-12,11,Phoenix Mercury,FALSE,18,away +400539513,2014,2,2014-06-12,18,Connecticut Sun,TRUE,11,home +400539512,2014,2,2014-06-11,14,Seattle Storm,FALSE,5,away +400539512,2014,2,2014-06-11,5,Indiana Fever,TRUE,14,home +400539511,2014,2,2014-06-10,14,Seattle Storm,TRUE,19,away +400539511,2014,2,2014-06-10,19,Chicago Sky,FALSE,14,home +400539510,2014,2,2014-06-10,11,Phoenix Mercury,TRUE,16,away +400539510,2014,2,2014-06-10,16,Washington Mystics,FALSE,11,home 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Wings,TRUE,11,away +400864507,2016,2,2016-06-18,11,Phoenix Mercury,FALSE,3,home +400864506,2016,2,2016-06-18,20,Atlanta Dream,FALSE,16,away +400864506,2016,2,2016-06-18,16,Washington Mystics,TRUE,20,home +400864505,2016,2,2016-06-17,11,Phoenix Mercury,FALSE,6,away +400864505,2016,2,2016-06-17,6,Los Angeles Sparks,TRUE,11,home +400864504,2016,2,2016-06-17,5,Indiana Fever,TRUE,17,away +400864504,2016,2,2016-06-17,17,San Antonio Stars,FALSE,5,home +400864503,2016,2,2016-06-17,19,Chicago Sky,FALSE,20,away +400864503,2016,2,2016-06-17,20,Atlanta Dream,TRUE,19,home +400864502,2016,2,2016-06-16,14,Seattle Storm,FALSE,3,away +400864502,2016,2,2016-06-16,3,Dallas Wings,TRUE,14,home +400864501,2016,2,2016-06-16,9,New York Liberty,TRUE,18,away +400864501,2016,2,2016-06-16,18,Connecticut Sun,FALSE,9,home +400864500,2016,2,2016-06-14,19,Chicago Sky,FALSE,6,away +400864500,2016,2,2016-06-14,6,Los Angeles Sparks,TRUE,19,home +400864498,2016,2,2016-06-14,5,Indiana Fever,FALSE,8,away 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Antonio Stars,FALSE,9,home +400864479,2016,2,2016-06-11,8,Minnesota Lynx,TRUE,16,away +400864479,2016,2,2016-06-11,16,Washington Mystics,FALSE,8,home +400864477,2016,2,2016-06-10,8,Minnesota Lynx,TRUE,20,away +400864477,2016,2,2016-06-10,20,Atlanta Dream,FALSE,8,home +400864471,2016,2,2016-06-10,14,Seattle Storm,FALSE,18,away +400864471,2016,2,2016-06-10,18,Connecticut Sun,TRUE,14,home +400864475,2016,2,2016-06-10,19,Chicago Sky,TRUE,5,away +400864475,2016,2,2016-06-10,5,Indiana Fever,FALSE,19,home +400864467,2016,2,2016-06-09,17,San Antonio Stars,FALSE,11,away +400864467,2016,2,2016-06-09,11,Phoenix Mercury,TRUE,17,home +400864464,2016,2,2016-06-08,16,Washington Mystics,TRUE,3,away +400864464,2016,2,2016-06-08,3,Dallas Wings,FALSE,16,home +400864461,2016,2,2016-06-07,9,New York Liberty,FALSE,6,away +400864461,2016,2,2016-06-07,6,Los Angeles Sparks,TRUE,9,home +400864453,2016,2,2016-06-07,11,Phoenix Mercury,FALSE,8,away +400864453,2016,2,2016-06-07,8,Minnesota Lynx,TRUE,11,home 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Angeles Sparks,TRUE,19,away +400864444,2016,2,2016-05-24,19,Chicago Sky,FALSE,6,home +400864442,2016,2,2016-05-24,20,Atlanta Dream,TRUE,9,away +400864442,2016,2,2016-05-24,9,New York Liberty,FALSE,20,home +400864439,2016,2,2016-05-22,8,Minnesota Lynx,TRUE,14,away +400864439,2016,2,2016-05-22,14,Seattle Storm,FALSE,8,home +400864437,2016,2,2016-05-22,19,Chicago Sky,FALSE,20,away +400864437,2016,2,2016-05-22,20,Atlanta Dream,TRUE,19,home +400864432,2016,2,2016-05-21,17,San Antonio Stars,FALSE,3,away +400864432,2016,2,2016-05-21,3,Dallas Wings,TRUE,17,home +400864429,2016,2,2016-05-21,16,Washington Mystics,TRUE,18,away +400864429,2016,2,2016-05-21,18,Connecticut Sun,FALSE,16,home +400864405,2016,2,2016-05-21,6,Los Angeles Sparks,TRUE,9,away +400864405,2016,2,2016-05-21,9,New York Liberty,FALSE,6,home +400864403,2016,2,2016-05-20,14,Seattle Storm,TRUE,11,away +400864403,2016,2,2016-05-20,11,Phoenix Mercury,FALSE,14,home +400864397,2016,2,2016-05-20,6,Los Angeles Sparks,TRUE,16,away 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Fever,FALSE,8,home +400973288,2017,2,2017-08-29,18,Connecticut Sun,TRUE,16,away +400973288,2017,2,2017-08-29,16,Washington Mystics,FALSE,18,home +400927582,2017,2,2017-08-27,11,Phoenix Mercury,TRUE,14,away +400927582,2017,2,2017-08-27,14,Seattle Storm,FALSE,11,home +400927581,2017,2,2017-08-27,8,Minnesota Lynx,FALSE,6,away +400927581,2017,2,2017-08-27,6,Los Angeles Sparks,TRUE,8,home +400927580,2017,2,2017-08-27,19,Chicago Sky,FALSE,9,away +400927580,2017,2,2017-08-27,9,New York Liberty,TRUE,19,home +400927579,2017,2,2017-08-26,3,Dallas Wings,TRUE,16,away +400927579,2017,2,2017-08-26,16,Washington Mystics,FALSE,3,home +400927578,2017,2,2017-08-26,5,Indiana Fever,FALSE,20,away +400927578,2017,2,2017-08-26,20,Atlanta Dream,TRUE,5,home +400927576,2017,2,2017-08-25,16,Washington Mystics,FALSE,9,away +400927576,2017,2,2017-08-25,9,New York Liberty,TRUE,16,home +400927575,2017,2,2017-08-25,19,Chicago Sky,TRUE,18,away +400927575,2017,2,2017-08-25,18,Connecticut Sun,FALSE,19,home 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+Rochester,2,Emory,3,2022-11-05 +Pacific Lutheran,3,George Fox,1,2022-11-05 +Puget Sound,4,Willamette,0,2022-11-05 +Geneseo State,1,Cortland State,1,2022-11-05 +University of New England,0,Western New England,3,2022-11-05 +CCNY,0,John Jay,1,2022-11-05 +Cornell,1,Grinnell,2,2022-11-05 +Ohio Northern,1,Capital,0,2022-11-05 +Southern Virginia,1,Methodist,3,2022-11-05 +Drew,0,Scranton,1,2022-11-05 +Rose-Hulman,1,Hanover,0,2022-11-05 +Penn State-Harrisburg,0,Penn State-Berks,3,2022-11-05 +Neumann,0,Marywood,1,2022-11-05 +Denison,2,Ohio Wesleyan,0,2022-11-05 +Wesleyan,3,Trinity (Conn.),0,2022-11-05 +Utica,0,Nazareth,1,2022-11-05 +SUNY Delhi,3,Husson,2,2022-11-05 +Penn State-Behrend,2,Pitt-Greensburg,3,2022-11-05 +Clark,0,Emerson,1,2022-11-05 +Maryville (Tenn.),0,Piedmont,0,2022-11-05 +St. Catherine,1,Carleton,0,2022-11-05 +Pacific,0,Lewis and Clark,0,2022-11-05 +Linfield,0,Whitworth,1,2022-11-05 +New York University,3,Brandeis,2,2022-11-05 +Washington U.,1,Chicago,1,2022-11-05 +Farmingdale State,0,Sarah Lawrence,0,2022-11-05 +Bryn Athyn,1,Cedar Crest,0,2022-11-05 +Beloit,1,Lake Forest,0,2022-11-05 +Claremont-Mudd-Scripps,0,Pomona-Pitzer,3,2022-11-05 +North Central (Ill.),1,Illinois Wesleyan,4,2022-11-05 +Westminster (Mo.),0,Greenville,1,2022-11-05 +Northwestern (Minn.),0,UW-Superior,6,2022-11-05 +Western Connecticut,1,Southern Maine,3,2022-11-05 +Franciscan,0,Westminster (Pa.),2,2022-11-05 +Lasell,1,Johnson and Wales,2,2022-11-05 +Dickinson,0,Johns Hopkins,4,2022-11-06 +Mary Washington,0,Christopher Newport,1,2022-11-06 +Texas Lutheran,0,Trinity (Texas),4,2022-11-06 +Wesleyan,0,Amherst,2,2022-11-06 +Ithaca,0,William Smith,0,2022-11-06 +Lynchburg,1,Virginia Wesleyan,1,2022-11-06 +Pacific Lutheran,0,Lewis and Clark,0,2022-11-06 +Puget Sound,4,Pacific,1,2022-11-06 +Fitchburg State,0,Westfield State,1,2022-11-06 +New England College,0,Lesley,2,2022-11-06 +Concordia (Wis.),1,Illinois Tech,0,2022-11-06 +Beloit,0,Grinnell,0,2022-11-06 +Centre,0,Sewanee,0,2022-11-06 +Willamette,0,George Fox,3,2022-11-06 +Linfield,0,Whitman,1,2022-11-06 +Bryn Athyn,0,Carnegie Mellon,6,2022-11-11 +Nazareth,0,Calvin,2,2022-11-11 +Greenville,0,UW-La Crosse,3,2022-11-11 +Concordia (Wis.),2,Illinois Wesleyan,5,2022-11-11 +Marywood,0,Misericordia,6,2022-11-12 +Lesley,0,Johns Hopkins,6,2022-11-12 +John Jay,0,Christopher Newport,6,2022-11-12 +Penn State-Berks,0,Messiah,3,2022-11-12 +Pitt-Greensburg,1,Case Western Reserve,6,2022-11-12 +Pomona-Pitzer,1,Trinity (Texas),1,2022-11-12 +Westminster (Pa.),0,Amherst,3,2022-11-12 +Calvin,0,Carnegie Mellon,2,2022-11-12 +UW-Superior,1,Loras,7,2022-11-12 +SUNY Delhi,0,William Smith,12,2022-11-12 +Illinois Wesleyan,0,UW-La Crosse,1,2022-11-12 +Pacific Lutheran,2,Mary Hardin-Baylor,1,2022-11-12 +Rowan,5,Methodist,1,2022-11-12 +Piedmont,0,Emory,7,2022-11-12 +Puget Sound,0,Virginia Wesleyan,3,2022-11-12 +Lynchburg,0,Montclair State,1,2022-11-12 +Grinnell,2,Wartburg,5,2022-11-12 +Washington and Lee,1,Centre,2,2022-11-12 +Denison,0,Tufts,1,2022-11-12 +Wesleyan,1,Cortland State,1,2022-11-12 +Southern Maine,0,Williams,1,2022-11-12 +Sarah Lawrence,0,MIT,6,2022-11-12 +DeSales,3,Cabrini,2,2022-11-12 +Westfield State,2,Emerson,1,2022-11-12 +Capital,0,Ithaca,1,2022-11-12 +Goucher,0,FDU-Florham,3,2022-11-12 +Johnson and Wales,1,RIT,3,2022-11-12 +Bowdoin,0,Scranton,0,2022-11-12 +Geneseo State,1,Trinity (Conn.),1,2022-11-12 +Rose-Hulman,0,Ohio Northern,1,2022-11-12 +St. Catherine,1,Washington U.,2,2022-11-12 +York (Pa.),0,Western New England,2,2022-11-12 +Western New England,1,Misericordia,5,2022-11-13 +Trinity (Conn.),0,Johns Hopkins,4,2022-11-13 +Christopher Newport,2,Montclair State,0,2022-11-13 +Wesleyan,0,Messiah,1,2022-11-13 +Ithaca,0,Case Western Reserve,6,2022-11-13 +Pacific Lutheran,0,Trinity (Texas),1,2022-11-13 +Amherst,1,RIT,0,2022-11-13 +Ohio Northern,0,Loras,1,2022-11-13 +Tufts,0,William Smith,1,2022-11-13 +Virginia Wesleyan,1,Rowan,0,2022-11-13 +Centre,3,Emory,3,2022-11-13 +Washington U.,3,Wartburg,1,2022-11-13 +Williams,0,MIT,0,2022-11-13 +DeSales,2,FDU-Florham,3,2022-11-13 +Westfield State,0,Scranton,3,2022-11-13 +Scranton,0,Misericordia,2,2022-11-19 +Williams,1,Johns Hopkins,2,2022-11-19 +Trinity (Texas),0,Christopher Newport,1,2022-11-19 +Emory,0,Messiah,1,2022-11-19 +Loras,1,Case Western Reserve,3,2022-11-19 +William Smith,2,Amherst,1,2022-11-19 +Washington U.,2,Carnegie Mellon,1,2022-11-19 +Virginia Wesleyan,2,UW-La Crosse,1,2022-11-19 +Johns Hopkins,2,Misericordia,0,2022-11-20 +Virginia Wesleyan,0,Christopher Newport,0,2022-11-20 +Messiah,2,Washington U.,2,2022-11-20 +William Smith,2,Case Western Reserve,4,2022-11-20 +Johns Hopkins,4,Messiah,1,2022-12-02 +Virginia Wesleyan,0,Case Western Reserve,3,2022-12-02 +Case Western Reserve,1,Johns Hopkins,2,2022-12-04 diff --git a/data/nhl_2223.csv b/data/nhl_2223.csv new file mode 100644 index 0000000..cda8caa --- /dev/null +++ b/data/nhl_2223.csv @@ -0,0 +1,33 @@ +Team,GP,SOG,W,L,OTL,PTS,Reg_PTS,PPG,SO_PTS,Pct_SO,RW,ROW,SOW,SOL,GF,GA,DIFF +BOS,82,7,65,12,5,135,131,1.7466666666666666,4,0.5714285714285714,54,61,4,3,305,177,128 +CAR,82,7,52,21,9,113,109,1.4533333333333334,4,0.5714285714285714,39,48,4,3,266,213,53 +NJ,82,6,52,22,8,112,110,1.4473684210526316,2,0.3333333333333333,39,50,2,4,291,226,65 +TOR,82,3,50,21,11,111,110,1.3924050632911393,1,0.3333333333333333,42,49,1,2,279,222,57 +VGK,82,9,51,22,9,111,106,1.452054794520548,5,0.5555555555555556,38,46,5,4,272,229,43 +EDM,82,4,50,23,9,109,109,1.3974358974358974,0,0,45,50,0,4,325,260,65 +COL,82,9,51,24,7,109,103,1.4109589041095891,6,0.6666666666666666,36,45,6,3,280,226,54 +DAL,82,7,47,21,14,108,104,1.3866666666666667,4,0.5714285714285714,39,43,4,3,285,218,67 +NYR,82,7,47,22,13,107,103,1.3733333333333333,4,0.5714285714285714,37,43,4,3,277,219,58 +LA,82,9,47,25,10,104,98,1.3424657534246576,6,0.6666666666666666,37,41,6,3,280,257,23 +MIN,82,13,46,25,11,103,96,1.391304347826087,7,0.5384615384615384,34,39,7,6,246,225,21 +SEA,82,4,46,28,8,100,100,1.2820512820512822,0,0,37,46,0,4,289,256,33 +TB,82,5,46,30,6,98,95,1.2337662337662338,3,0.6,38,43,3,2,283,254,29 +WPG,82,2,46,33,3,95,94,1.175,1,0.5,36,45,1,1,247,225,22 +NYI,82,6,42,31,9,93,92,1.2105263157894737,1,0.16666666666666666,36,41,1,5,243,222,21 +CGY,82,7,38,27,17,93,91,1.2133333333333334,2,0.2857142857142857,31,36,2,5,260,252,8 +FLA,82,3,42,32,8,92,90,1.139240506329114,2,0.6666666666666666,36,40,2,1,290,273,17 +NSH,82,8,42,32,8,92,86,1.162162162162162,6,0.75,29,36,6,2,229,238,-9 +PIT,82,2,40,31,11,91,90,1.125,1,0.5,31,39,1,1,262,264,-2 +BUF,82,6,42,33,7,91,88,1.1578947368421053,3,0.5,30,39,3,3,296,300,-4 +OTT,82,3,39,35,8,86,84,1.0632911392405062,2,0.6666666666666666,31,37,2,1,261,271,-10 +VAN,82,8,38,37,7,83,77,1.0405405405405406,6,0.75,24,32,6,2,276,298,-22 +STL,82,6,37,38,7,81,78,1.0263157894736843,3,0.5,27,34,3,3,263,301,-38 +WSH,82,6,35,37,10,80,78,1.0263157894736843,2,0.3333333333333333,27,33,2,4,255,265,-10 +DET,82,7,35,37,10,80,77,1.0266666666666666,3,0.42857142857142855,28,32,3,4,240,279,-39 +PHI,82,3,31,38,13,75,73,0.9240506329113924,2,0.6666666666666666,26,29,2,1,222,277,-55 +ARI,82,7,28,40,14,70,67,0.8933333333333333,3,0.42857142857142855,20,25,3,4,228,299,-71 +MTL,82,7,31,45,6,68,63,0.84,5,0.7142857142857143,21,26,5,2,232,307,-75 +SJ,82,7,22,44,16,60,59,0.7866666666666666,1,0.14285714285714285,16,21,1,6,234,321,-87 +CHI,82,4,26,49,7,59,57,0.7307692307692307,2,0.5,18,24,2,2,204,301,-97 +CBJ,82,2,25,48,9,59,58,0.725,1,0.5,15,24,1,1,214,330,-116 +ANA,82,6,23,47,12,58,55,0.7236842105263158,3,0.5,13,20,3,3,209,338,-129 diff --git a/esports/lol-buff-nerf.html b/esports/lol-buff-nerf.html index 9feb4a1..2645076 100644 --- a/esports/lol-buff-nerf.html +++ b/esports/lol-buff-nerf.html @@ -2,7 +2,7 @@ - + @@ -48,7 +48,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -57,6 +63,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -70,9 +77,9 @@
    -
    -
@@ -264,6 +277,12 @@ -

Edit this page

Report an issue

+
-
+

League of Legends - Buffing and Nerfing

@@ -510,8 +535,10 @@

League of Legends - Buffing and Nerfing

+
+

Motivation

League of Legends is a 5 v. 5 multiplayer online battle arena (MOBA) game developed by Riot Games. They collect data to evaluate the effect of every champion, adjusting and fine-tuning various aspects associated with each champion, to ensure fair and competitive gameplay. Through updates (patches) they can reduce the power of champions that are too strong (known as nerfing) or enhance the abilities of less successful champions (nerfing).

@@ -654,10 +681,9 @@

References

// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -667,8 +693,17 @@

References

interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -682,6 +717,125 @@

References

const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -724,6 +878,7 @@

References

} div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -749,6 +904,32 @@

References

}); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -814,7 +995,9 @@

References

 
-
Maintained by Ron Yurko
+
+

Maintained by Ron Yurko

+
 
@@ -823,4 +1006,5 @@

References

+ \ No newline at end of file diff --git a/football/nfl-draft-combine.html b/football/nfl-draft-combine.html index 0e2159c..6e648aa 100644 --- a/football/nfl-draft-combine.html +++ b/football/nfl-draft-combine.html @@ -2,7 +2,7 @@ - + @@ -48,7 +48,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -57,6 +63,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -70,9 +77,9 @@
-
-
-
+
-
+

NFL Draft Combine

@@ -509,8 +534,10 @@

NFL Draft Combine

+
+

Motivation

The data set has statistics with variables for player draft information and how they performed in events at the NFL Scouting Combine. The variables for draft information are position and Round. The variables for the NFL Scouting Combine events are forty, vertical, bench_reps, broad_jump, and shuttle. Some entries are NA if a player was not drafted or did not prticipate in some events.

@@ -652,10 +679,9 @@

References

// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -665,8 +691,17 @@

References

interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -680,6 +715,125 @@

References

const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -722,6 +876,7 @@

References

} div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -747,6 +902,32 @@

References

}); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -812,7 +993,9 @@

References

 
-
Maintained by Ron Yurko
+
+

Maintained by Ron Yurko

+
 
@@ -821,4 +1004,5 @@

References

+ \ No newline at end of file diff --git a/football/nfl-team-statistics.html b/football/nfl-team-statistics.html index a03c68b..3949252 100644 --- a/football/nfl-team-statistics.html +++ b/football/nfl-team-statistics.html @@ -2,7 +2,7 @@ - + @@ -48,7 +48,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -57,6 +63,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -70,9 +77,9 @@
-
-
-
+
-
+

National Football League Team Statistics

@@ -512,8 +537,10 @@

National Football League Team Statistics

+
+

Motivation

The National Football League (NFL) is the top professional American football league in the world. While a team’s record ultimately determines whether or not they make the playoffs, their score differential (points for - points against) is often a better indicator of a team’s ability. But what aspects of a team’s performance are related to their point differential? Is passing more important than rushing? What about offense in comparison to defense? The NFL records a variety of statistics, and the public NFL analytics community have developed advanced metrics such as expected points added (EPA) that provide deeper insight into a team’s performance. With this dataset of statistics dating back to 1999, you can explore variation between teams since as well as which types of statistics are relevant predictor variables of record and point differential.

@@ -751,10 +778,9 @@

References

// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -764,8 +790,17 @@

References

interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -779,6 +814,125 @@

References

const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -821,6 +975,7 @@

References

} div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -846,6 +1001,32 @@

References

}); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -911,7 +1092,9 @@

References

 
-
Maintained by Ron Yurko
+
+

Maintained by Ron Yurko

+
 
@@ -920,4 +1103,5 @@

References

+ \ No newline at end of file diff --git a/golf/pga_drive_putt.html b/golf/pga_drive_putt.html index a80844c..ba6d35e 100644 --- a/golf/pga_drive_putt.html +++ b/golf/pga_drive_putt.html @@ -2,7 +2,7 @@ - + @@ -48,7 +48,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -57,6 +63,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -70,9 +77,9 @@
-
-
-
+
-
+

Statistics of PGA Tournaments During the 2022 Season

@@ -509,8 +534,10 @@

Statistics of PGA Tournaments During the 2022 Season

+
+

Motivation

A common expression among golfers is “Drive for show, putt for dough.” This implies that the long initial tee shots (drives) on each hole are impressive, but the real key to success is the final strokes rolling the ball along the green into the hole (putts). Do data support this adage? The dataset for this activity was obtained from the PGA statistics website. Cases include all golfers who made the cut in each of 19 PGA tournaments in 2022. The dataset includes variables for driving ability, putting ability, and measuring success in the tournament. The “driving” variables include average driving distance (avgDriveDist), driving accuracy percentage (drivePct), and strokes gained off the tee (driveSG). The “putting” variables are average putts per round (avgPuttsPerRound), one putt percentage (onePuttPct), and strokes gained putting (puttsSG). The variables to measure success are scoring average (avgScore), official money won (Money), and Fedex Cup points (Points).

@@ -674,10 +701,9 @@

References

// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -687,8 +713,17 @@

References

interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -702,6 +737,125 @@

References

const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -744,6 +898,7 @@

References

} div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -769,6 +924,32 @@

References

}); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -834,7 +1015,9 @@

References

 
-
Maintained by Ron Yurko
+
+

Maintained by Ron Yurko

+
 
@@ -843,4 +1026,5 @@

References

+ \ No newline at end of file diff --git a/hockey/nhl-shots.html b/hockey/nhl-shots.html index 5081ec3..b535ef2 100644 --- a/hockey/nhl-shots.html +++ b/hockey/nhl-shots.html @@ -2,7 +2,7 @@ - + @@ -48,7 +48,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -57,6 +63,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -70,9 +77,9 @@
-
-
-
+
-
+

National Hockey League Shots

@@ -513,8 +538,10 @@

National Hockey League Shots

+
+

Motivation

The National Hockey League (NHL) is the top professional men’s hockey league in the world. The league records every shot players take along with contextual information about the shot such as its location, the player’s distance and angle to the goal when attempting the shot, as well as the outcome (blocked, missed, or goal). Using this information, the hockey analytics community have developed measures of shot quality known as expected goals. With this dataset, you can create your own expected goals model to predict the shot outcome given relevant features.

@@ -718,10 +745,9 @@

References

// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -731,8 +757,17 @@

References

interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -746,6 +781,125 @@

References

const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -788,6 +942,7 @@

References

} div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -813,6 +968,32 @@

References

}); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -878,7 +1059,9 @@

References

 
-
Maintained by Ron Yurko
+
+

Maintained by Ron Yurko

+
 
@@ -887,4 +1070,5 @@

References

+ \ No newline at end of file diff --git a/hockey/nhl_2223.html b/hockey/nhl_2223.html new file mode 100644 index 0000000..ee5af39 --- /dev/null +++ b/hockey/nhl_2223.html @@ -0,0 +1,1055 @@ + + + + + + + + + + + + +SCORE Sports Data Repository - NHL Team Statistics + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

NHL Team Statistics

+
+
linear regression
+
correlation
+
+
+ +
+
+ Team-level statistics from the 2022-2023 NHL season +
+
+ + +
+ +
+
Author
+
+

Taylor Lum, Robin Lock

+
+
+ +
+
Published
+
+

December 15, 2023

+
+
+ + +
+ + + +
+ + +
+

Motivation

+

In 2005 the National Hockey League added shootouts to regular season games. In the event that regulation time ends in a tie and no goals are scored in overtime, the game goes to a 3-player shootout. This data set can be used to explore the relationship between various team-level statistics.

+

Regulation time in an NHL game consists of three 20 minute periods. If regulation time ends and the score is tied (both teams have the same number of goals), the game goes into a 5 minute 3-on-3 overtime, where the next goal wins. If the game is still not resolved after overtime, it goes into a 3 round shootout, where players take a 1-on-1 breakaway against the goalie. If still tied after three shootout rounds, shootout rounds continue until one team is victorious. A team gets 2 points for a win (whether it’s regulation, overtime, or shootout), and the losing team gets 0 points for a regulation loss, or 1 point for an overtime/shootout loss.

+
+
+

Data

+

The data set contains the final standings of the 2022-2023 NHL Season with team level statistics.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
nhl_2223.csv
VariableDescription
TeamNHL team abbreviation
GPTotal games played
SOGNumber of games that went to shootout
WWins
LLosses
OTLOvertime losses
PTSPoints
Reg_PTSPoints won during regulation time or overtime (2 points per game)
PPGPoints won per game in regulation time or overtime (# of non-shootout points / # of non-shootout games)
SO_PTSPoints won from shootout (1 extra point per shootout)
Pct_SOProportion of possible points from shootout won (# of shootout points / # of shootout games)
RWNumber of regulation wins
ROWNumber of regulation and overtime wins
SOWNumber of shootout wins
SOLNumber of shootout losses
GFTotal goals scored
GATotal goals against
DIFFGoal differential (GF-GA)
+
+
+

Questions

+
    +

  1. Plot GA versus GF. Describe the trend. Do teams that score more goals tend to allow more or less goals against?

    2. +

  2. Is offensive ability (‘GF’) or defensive ability (‘GA’) a better predictor of overall performance of a team (as measured by ‘PTS’)?

    3. +

  3. Is there a relationship between the ability to win shootouts (‘Pct_SO’) the ability to win games before shootouts (‘PPG’)?

    4. +

  4. Is there a relationship between the total number of goals a team scored during the season (GF) and the proportion of shootout points they won (Pct_SO)?

    5. +
+
+
+

References

+

NHL Standings 2022-23. ESPN (n.d.). https://www.espn.com/nhl/standings/_/season/2023/group/league

+ + +
+ +
+ +
+ + + + + + \ No newline at end of file diff --git a/hockey/nhl_powerplay.html b/hockey/nhl_powerplay.html index 907738d..ad53984 100644 --- a/hockey/nhl_powerplay.html +++ b/hockey/nhl_powerplay.html @@ -2,7 +2,7 @@ - + @@ -48,7 +48,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -57,6 +63,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -70,9 +77,9 @@
-
-
-
+
-
+

NHL Team Power Play Perfomance in 2022-2023

@@ -508,8 +533,10 @@

NHL Team Power Play Perfomance in 2022-2023

+
+

Motivation

Certain infractions in a hockey game can lead to one or more players being forced to sit out for a period of time while their team has to skate with fewer payers. A common scenario that results is a 5 on 4 power play where one team has five skates (plus a goalie) and the penalized team has only four skaters (plus a goalie). The team with fewer skates is said to be shorthanded. Typical penalty times are for two minutes and the penalized player is allowed to return to the ice if the opponent scores a goal while on the power play. Rare, but more serious infractions, produce a major penalty (5 minutes) where the penalized player is not allowed to return to the ice when a goal is scored. A power play can also end before two minutes when the team on the power play commits a penalty which results in one of its players being sent off the ice to even the sides.

@@ -654,10 +681,9 @@

References

// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -667,8 +693,17 @@

References

interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -682,6 +717,125 @@

References

const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -724,6 +878,7 @@

References

} div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -749,6 +904,32 @@

References

}); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -814,7 +995,9 @@

References

 
-
Maintained by Ron Yurko
+
+

Maintained by Ron Yurko

+
 
@@ -823,4 +1006,5 @@

References

+ \ No newline at end of file diff --git a/hockey/phf-shots.html b/hockey/phf-shots.html index f606e48..2f50282 100644 --- a/hockey/phf-shots.html +++ b/hockey/phf-shots.html @@ -2,7 +2,7 @@ - + @@ -48,7 +48,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -57,6 +63,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -70,9 +77,9 @@
-
-
-
+
-
+

Premier Hockey Federation Shots

@@ -510,8 +535,10 @@

Premier Hockey Federation Shots

+
+

Motivation

The Premier Hockey Federation (PHF) is a professional women’s hockey league in North America. Recently, more data has become available for people to explore and join the women’s hockey analytics community. This dataset lets you explore shots taken during the 2021-2022 PHF season.

@@ -694,10 +721,9 @@

References

// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -707,8 +733,17 @@

References

interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -722,6 +757,125 @@

References

const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -764,6 +918,7 @@

References

} div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -789,6 +944,32 @@

References

}); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -854,7 +1035,9 @@

References

 
-
Maintained by Ron Yurko
+
+

Maintained by Ron Yurko

+
 
@@ -863,4 +1046,5 @@

References

+ \ No newline at end of file diff --git a/index.html b/index.html index a521cba..edec875 100644 --- a/index.html +++ b/index.html @@ -2,7 +2,7 @@ - + @@ -45,7 +45,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -54,6 +60,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -67,9 +74,9 @@
-
-
-
+
+
+

The SCORE Network Sports Data Repository curates interesting datasets across a variety of sports for use in statistics and data science education. Each dataset has the following properties:

  • A sports question of interest, with context motivating why the dataset is relevant and interesting to explore.
    • @@ -570,10 +597,9 @@

    SCORE Sports Data Repository

    // clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -583,8 +609,17 @@

    SCORE Sports Data Repository

    interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -598,6 +633,125 @@

    SCORE Sports Data Repository

    const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -640,6 +794,7 @@

    SCORE Sports Data Repository

    } div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -665,6 +820,32 @@

    SCORE Sports Data Repository

    }); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -730,7 +911,9 @@

    SCORE Sports Data Repository

     
    -
    Maintained by Ron Yurko

    Edit this page

    Report an issue

    +
    +

    Maintained by Ron Yurko

    +
     
    @@ -739,4 +922,5 @@

    SCORE Sports Data Repository

    + \ No newline at end of file diff --git a/lacrosse/lacrosse_ncaa_div1.html b/lacrosse/lacrosse_ncaa_div1.html index e1358af..3c3dc32 100644 --- a/lacrosse/lacrosse_ncaa_div1.html +++ b/lacrosse/lacrosse_ncaa_div1.html @@ -2,7 +2,7 @@ - + @@ -48,7 +48,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -57,6 +63,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -70,9 +77,9 @@
    -
    -
@@ -264,6 +277,12 @@ -

Edit this page

Report an issue

+
-
+

Team statistics for NCAA Division 1 Men’s and Women’s Lacrosse

@@ -511,8 +536,10 @@

Team statistics for NCAA Division 1 Men’s and Women’s Lacr

+
+

Motivation

Over the years, lacrosse has experienced remarkable growth, with increased popularity and participation propelling the sport to new heights. This surge has led to a rise in skilled athletes competing in high-level leagues, notably NCAA Division I Men’s Lacrosse. Within this heightened competition, faceoffs are known to be a vital aspect of the game. Due to their important impact, players train to specifically specialize in them, as winning a faceoff often grants their teams crucial opportunities for offensive possession which can profoundly impact the course of the game.

@@ -857,10 +884,9 @@

References

// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -870,8 +896,17 @@

References

interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -885,6 +920,125 @@

References

const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -927,6 +1081,7 @@

References

} div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -952,6 +1107,32 @@

References

}); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -1017,7 +1198,9 @@

References

 
-
Maintained by Ron Yurko
+
+

Maintained by Ron Yurko

+
 
@@ -1026,4 +1209,5 @@

References

+ \ No newline at end of file diff --git a/lacrosse/lacrosse_pll_vs_nll.html b/lacrosse/lacrosse_pll_vs_nll.html index 0cfe548..621ef0a 100644 --- a/lacrosse/lacrosse_pll_vs_nll.html +++ b/lacrosse/lacrosse_pll_vs_nll.html @@ -2,7 +2,7 @@ - + @@ -48,7 +48,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -57,6 +63,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -70,9 +77,9 @@
-
-
-
+
-
+

Lacrosse PLL vs. NLL

@@ -509,8 +534,10 @@

Lacrosse PLL vs. NLL

+
+

Motivation

This data set examines the goals and shots in two prominent lacrosse leagues: the Premier Lacrosse League (PLL) and the National Lacrosse League (NLL).

@@ -662,10 +689,9 @@

References

// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -675,8 +701,17 @@

References

interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -690,6 +725,125 @@

References

const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -732,6 +886,7 @@

References

} div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -757,6 +912,32 @@

References

}); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -822,7 +1003,9 @@

References

 
-
Maintained by Ron Yurko
+
+

Maintained by Ron Yurko

+
 
@@ -831,4 +1014,5 @@

References

+ \ No newline at end of file diff --git a/listings.json b/listings.json index 20b1c91..657f38d 100644 --- a/listings.json +++ b/listings.json @@ -2,6 +2,7 @@ { "listing": "/by-statsds-topic.html", "items": [ + "/soccer/D3_wsoc2022.html", "/running/boston_marathon_2023.html", "/tennis/australian-open.html", "/softball/softball_d1_2021-2022.html", @@ -12,6 +13,7 @@ "/motor_sports/NASCAR_driver_ratings.html", "/football/nfl-draft-combine.html", "/hockey/nhl_powerplay.html", + "/hockey/nhl_2223.html", "/basketball/nba-player-stats.html", "/football/nfl-team-statistics.html", "/hockey/nhl-shots.html", @@ -23,6 +25,7 @@ "/volleyball/volleyball_ncaa_team_stats.html", "/lacrosse/lacrosse_ncaa_div1.html", "/tennis/wta-grand-slam-matches.html", + "/basketball/WNBA20Yrs.html", "/basketball/wnba-shots.html" ] } diff --git a/motor_sports/NASCAR_driver_ratings.html b/motor_sports/NASCAR_driver_ratings.html index c97efea..d0a3e9a 100644 --- a/motor_sports/NASCAR_driver_ratings.html +++ b/motor_sports/NASCAR_driver_ratings.html @@ -2,7 +2,7 @@ - + @@ -48,7 +48,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -57,6 +63,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -70,9 +77,9 @@
-
-
-
+
-
+

NASCAR Data Description

@@ -513,8 +538,10 @@

NASCAR Data Description

+
+

Motivation

@@ -695,10 +722,9 @@

References

// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -708,8 +734,17 @@

References

interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -723,6 +758,125 @@

References

const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -765,6 +919,7 @@

References

} div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -790,6 +945,32 @@

References

}); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -855,7 +1036,9 @@

References

 
-
Maintained by Ron Yurko
+
+

Maintained by Ron Yurko

+
 
@@ -864,4 +1047,5 @@

References

+ \ No newline at end of file diff --git a/running/boston_marathon_2023.html b/running/boston_marathon_2023.html index be129ed..389b031 100644 --- a/running/boston_marathon_2023.html +++ b/running/boston_marathon_2023.html @@ -2,7 +2,7 @@ - + @@ -48,7 +48,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -57,6 +63,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -70,9 +77,9 @@
-
-
-
+
-
+

2023 Boston Marathon runners

@@ -510,8 +535,10 @@

2023 Boston Marathon runners

+
+

Motivation

The Boston Marathon is an annual road race that takes place in the City of Boston Massachusetts. Attracting thousands of runners of varying skill and experience, year after year participants from around the world gather to compete for world records, divisional records, and personal bests. Examining the results of these athletes yields an interesting look into the spread and distribution of the times of all runners who successfully completed the historic race.

@@ -697,10 +724,9 @@

References

// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -710,8 +736,17 @@

References

interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -725,6 +760,125 @@

References

const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -767,6 +921,7 @@

References

} div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -792,6 +947,32 @@

References

}); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -857,7 +1038,9 @@

References

 
-
Maintained by Ron Yurko
+
+

Maintained by Ron Yurko

+
 
@@ -866,4 +1049,5 @@

References

+ \ No newline at end of file diff --git a/search.json b/search.json index 5ff344a..c84b1a7 100644 --- a/search.json +++ b/search.json @@ -1,807 +1,1552 @@ [ { - "objectID": "esports/lol-buff-nerf.html", - "href": "esports/lol-buff-nerf.html", - "title": "League of Legends - Buffing and Nerfing", + "objectID": "data-sources.html", + "href": "data-sources.html", + "title": "Data Sources", "section": "", - "text": "League of Legends is a 5 v. 5 multiplayer online battle arena (MOBA) game developed by Riot Games. They collect data to evaluate the effect of every champion, adjusting and fine-tuning various aspects associated with each champion, to ensure fair and competitive gameplay. Through updates (patches) they can reduce the power of champions that are too strong (known as nerfing) or enhance the abilities of less successful champions (nerfing)." + "text": "Here are some resources for finding sports datasets:\n\nCRAN Task View for Sports Analytics - catalog of R packages published on the Comprehensive R Archive Network (CRAN) organized by sport (curated by Benjamin S. Baumer, Quang Nguyen, and Gregory J. Matthews)\nSportsDataverse initiative - collection of software tools for accessing publicly available sports data (organized by Saiem Gilani)\nKaggle\nTidyTuesday - GitHub repository of datasets updated weekly with example code for accessing data (featuring a variety of sports examples)\nFiveThirtyEight - GitHub repository of datasets used in FiveThirtyEight articles", + "crumbs": [ + "Home", + "Data Sources" + ] }, { - "objectID": "esports/lol-buff-nerf.html#motivation", - "href": "esports/lol-buff-nerf.html#motivation", - "title": "League of Legends - Buffing and Nerfing", + "objectID": "lacrosse/lacrosse_ncaa_div1.html", + "href": "lacrosse/lacrosse_ncaa_div1.html", + "title": "Team statistics for NCAA Division 1 Men’s and Women’s Lacrosse", "section": "", - "text": "League of Legends is a 5 v. 5 multiplayer online battle arena (MOBA) game developed by Riot Games. They collect data to evaluate the effect of every champion, adjusting and fine-tuning various aspects associated with each champion, to ensure fair and competitive gameplay. Through updates (patches) they can reduce the power of champions that are too strong (known as nerfing) or enhance the abilities of less successful champions (nerfing)." + "text": "Over the years, lacrosse has experienced remarkable growth, with increased popularity and participation propelling the sport to new heights. This surge has led to a rise in skilled athletes competing in high-level leagues, notably NCAA Division I Men’s Lacrosse. Within this heightened competition, faceoffs are known to be a vital aspect of the game. Due to their important impact, players train to specifically specialize in them, as winning a faceoff often grants their teams crucial opportunities for offensive possession which can profoundly impact the course of the game.", + "crumbs": [ + "Home", + "Lacrosse", + "Team statistics for NCAA Division 1 Men's and Women's Lacrosse" + ] }, { - "objectID": "esports/lol-buff-nerf.html#data", - "href": "esports/lol-buff-nerf.html#data", - "title": "League of Legends - Buffing and Nerfing", + "objectID": "lacrosse/lacrosse_ncaa_div1.html#motivation", + "href": "lacrosse/lacrosse_ncaa_div1.html#motivation", + "title": "Team statistics for NCAA Division 1 Men’s and Women’s Lacrosse", + "section": "", + "text": "Over the years, lacrosse has experienced remarkable growth, with increased popularity and participation propelling the sport to new heights. This surge has led to a rise in skilled athletes competing in high-level leagues, notably NCAA Division I Men’s Lacrosse. Within this heightened competition, faceoffs are known to be a vital aspect of the game. Due to their important impact, players train to specifically specialize in them, as winning a faceoff often grants their teams crucial opportunities for offensive possession which can profoundly impact the course of the game.", + "crumbs": [ + "Home", + "Lacrosse", + "Team statistics for NCAA Division 1 Men's and Women's Lacrosse" + ] + }, + { + "objectID": "lacrosse/lacrosse_ncaa_div1.html#data", + "href": "lacrosse/lacrosse_ncaa_div1.html#data", + "title": "Team statistics for NCAA Division 1 Men’s and Women’s Lacrosse", "section": "Data", - "text": "Data\nEach row is one of the 162 League of Legends champions. The two datasets show play results (e.g. win rates) for two consecutive patches of the game (12.22 and 12.23).\n\nLOL_patch_12.22.csv, LOL_patch_12.23.csv\n\n\nVariable\nDescription\n\n\n\n\nName\nName of the champion\n\n\nRole\nRole of the champion in a game\n\n\nKDA\nAverage kills, deaths and assists associated with each champion\n\n\nWRate\nWin rates of each champion\n\n\nPickRate\nSPick rates of each champion\n\n\nRolePerc\nPercent of time the champion is used in the expected role\n\n\nBanPerc\nBan percentages associated with each champion" + "text": "Data\n\nThere are three data sets: 2022-2023 statistics for Men’s teams, 2022-2023 statsitics for Women’s teams, and a dataset combining the Men’s and Women’s statisitcs (that only includes the variables available in each of the other two).\n\nlacrosse_men_ncaa_div1_2022_2023.csv\n\n\nThis data set contains 72 rows and 22 columns. Each row represents a men’s lacrosse team at the NCAA Division 1 level from the 2022-2023 season.\n\nlacrosse_women_ncaa_div1_2022_2023.csv\n\n\nVariable\nDescription\n\n\n\n\nTeam\ncollege of the team\n\n\navg_assists\naverage assists to goals per game\n\n\navg_caused_turnovers\naverage turnovers forced by the team per game\n\n\nclearing_pctg\npercentage of successful attempts to earn an offensive opportunity after gaining the ball in the teams own half\n\n\ntotal_faceoffs\ntotal faceoffs taken by a team for the season\n\n\nfaceoff_wins\ntotal faceoff wins by a team for the season\n\n\nfaceoff_win_pct\nproportion of total faceoff wins out of total faceoffs. Equivalent to the draw percentage in women’s lacrosse.\n\n\navg_goals\naverage goals per game\n\n\navg_goals_allowed\naverage goals allowed by the team per game\n\n\navg_ground_balls\naverage loose balls picked up by the team per game\n\n\nman_down_defense_pctg\nproportion of times a team stops the opponent from scoring while man down due to a penalty\n\n\nman_up_offense_pctg\nproportion of times the offense scores out of total opportunities while man up\n\n\navg_scoring_margin\naverage margin of goals per game\n\n\nopp_clear_pctg\nopponents clearing percentage averaged by game\n\n\navg_points\naverage team points per game\n\n\navg_saves\naverage saves per game\n\n\nshot_pctg\nproportion of shots that go in out of total shots\n\n\navg_turnovers\naverage turnovers that are directly the fault of a player per game\n\n\nW\ntotal wins by the team\n\n\nL\ntotal losses by the team\n\n\nwin_loss_pctg\nproportion of games won out of total games\n\n\n\nThis data set contains 120 rows and 18 columns. Each row represents a women’s lacrosse team at the NCAA Division 1 level from the 2022-2023 season.\n\n\n\n\n\n\n\nVariable Name\nDescription\n\n\n\n\nTeam\nThe name of the women’s NCAA lacrosse team.\n\n\ndraw_pctg\nDraw percentage, the proportion of draw controls won by the team in a game. Equivalent to the Faceoff percentage in Men’s lacrosse.\n\n\nturnovers_per_game\nAverage number of turnovers committed by the team per game.\n\n\nsog_per_game\nShots on goal (SOG) per game, the average number of shots on goal taken by the team.\n\n\nassists_per_game\nAverage number of assists made by the team per game.\n\n\ncaused_turnovers_per_game\nThe average number of caused turnovers by the team per game.\n\n\nclearing_pctg\nClearing percentage, the rate at which the team successfully clears the ball from defense to offense.\n\n\ndraw_controls_per_game\nThe average number of draw controls won by the team per game.\n\n\nfouls_per_game\nAverage number of fouls committed by the team per game.\n\n\nfree_position_pctg\nFree position percentage, the rate of successful free position shots taken by the team.\n\n\nground_balls_per_game\nThe average number of ground balls scooped by the team per game.\n\n\npoints_per_game\nAverage number of total points (goals + assists) scored by the team per game.\n\n\nsave_pctg\nSave percentage, the proportion of shots on goal saved by the team’s goalkeeper.\n\n\ngoals_allowed_per_game\nAverage number of goals allowed by the team’s defense per game.\n\n\ngoals_per_game\nAverage number of goals scored by the team per game.\n\n\nshots_per_game\nAverage number of total shots (on goal + off target) taken by the team per game.\n\n\nshot_pctg\nShooting percentage, the proportion of shots on goal that result in a goal.\n\n\nwin_pctg\nThe team’s winning percentage, calculated as the number of wins divided by total games played.\n\n\n\n\nNote: The table provides a brief description of each variable, but more context and understanding might be required to interpret and analyze these statistics effectively in the context of women’s NCAA lacrosse.\n\nlacrosse_men_and_women_ncaa_div1_2022_2023.csv\n\n\nThis data set contains 192 rows and 6 columns. Each row represents a lacrosse team at the NCAA Division 1 level from the 2022-2023 season. Teams come from both the men’s and womens division.\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nTeam\ncollege of the team\n\n\nfaceoff_win_pct\nthe proportion of faceoffs win out of total faceoffs taken\n\n\nshots_per_game\naverage amount of shots per game\n\n\nshot_pctg\nproportion of shots that are goals out of total shots\n\n\nwin_loss_percentage\namount of wins out of total games played\n\n\nDivision\nthe division the team is in (Mens or Womens)", + "crumbs": [ + "Home", + "Lacrosse", + "Team statistics for NCAA Division 1 Men's and Women's Lacrosse" + ] }, { - "objectID": "esports/lol-buff-nerf.html#questions", - "href": "esports/lol-buff-nerf.html#questions", - "title": "League of Legends - Buffing and Nerfing", + "objectID": "lacrosse/lacrosse_ncaa_div1.html#questions", + "href": "lacrosse/lacrosse_ncaa_div1.html#questions", + "title": "Team statistics for NCAA Division 1 Men’s and Women’s Lacrosse", "section": "Questions", - "text": "Questions\n\nLook for outliers (e.g. in win rate) for the 12.22 patch to identify champions that might need to be buffed or nerfed.\nLook at results for the 12.23 patch to see if outliers identified from 12.22 might have been adjusted of if any different champions are now outliers." + "text": "Questions\nUsing either Women’s or Men’s\n\nWhich teams have better faceoff win proportions than the average?\nWhich teams have worse faceoff win proportions than the average?\nWhat sort of impact does faceoff percentage have on the success of the team?\n\nUsing the data with both Women’s and Men’s\n\nIn terms of a team’s win percentage, are faceoffs more important for men or women?\nWhat factors could make faceoffs more important for men or women?\nHow does faceoff win percentage affect winning percentage?", + "crumbs": [ + "Home", + "Lacrosse", + "Team statistics for NCAA Division 1 Men's and Women's Lacrosse" + ] }, { - "objectID": "esports/lol-buff-nerf.html#references", - "href": "esports/lol-buff-nerf.html#references", - "title": "League of Legends - Buffing and Nerfing", + "objectID": "lacrosse/lacrosse_ncaa_div1.html#references", + "href": "lacrosse/lacrosse_ncaa_div1.html#references", + "title": "Team statistics for NCAA Division 1 Men’s and Women’s Lacrosse", "section": "References", - "text": "References\nLol champion stats, 12.22 master, win rates. METAsrc. (n.d.). https://www.metasrc.com/5v5/12.22/stats?ranks=master\nLol champion stats, 12.23 master, win rates. METAsrc. (n.d.-b). https://www.metasrc.com/5v5/12.23/stats?ranks=master" + "text": "References\nNCAA Website: Women’s and Men’s Lacrosse Division I\nhttp://stats.ncaa.org/rankings/change_sport_year_div", + "crumbs": [ + "Home", + "Lacrosse", + "Team statistics for NCAA Division 1 Men's and Women's Lacrosse" + ] }, { - "objectID": "wrestling/sumo_wrestingling_since_1957.html", - "href": "wrestling/sumo_wrestingling_since_1957.html", - "title": "Sumo wrestler characteristics", + "objectID": "motor_sports/NASCAR_driver_ratings.html#data", + "href": "motor_sports/NASCAR_driver_ratings.html#data", + "title": "NASCAR Data Description", + "section": "Data", + "text": "Data\nThe data comes from the NASCAR website and shows the season statistics from 2007-2022. Each row displays the metrics of a racer for that specific year. The data frame contains 1111 rows of observations and 20 variables.\n\nnascar_driver_statistics.csv\n\n\nVariable\nDescription\n\n\n\n\nWins\nThe sum of the driver’s victories\n\n\nAvgStart\nThe sum of the driver’s starting positions divided by the number of races\n\n\nAvgMidRace\nThe sum of the driver’s mid race positions divided by the number of races\n\n\nAvgFinish\nThe sum of the driver’s finishing positions divided by the number of races\n\n\nAvgPos\nThe sum of the driver’s position each lap divided by the number of laps\n\n\nPassDiff\nThe sum of green flag passes minus green times passed\n\n\nGreenFlagPasses\nNumber of green flag passes performed by the driver\n\n\nGreenFlagPassed\nNumber of times driver is passed during green flag\n\n\nQualityPasses\nNumber of passes in the top 15 while under green flag conditions by driver\n\n\nPercentQualityPasses\nThe sum of quality passes divided by green flag passes\n\n\nNumFastestLaps\nNumber of where the driver had the fastest speed on the lap\n\n\nLapsInTop15\nNumber of laps completed while running in a top 15 position\n\n\nPercentLapsInTop15\nThe sum of the laps run in the top 15 divided by total laps completed\n\n\nLapsLed\nThe sum of the laps led in a race\n\n\nPercentLapsLed\nThe sum of the laps led in the race\n\n\nTotalLaps\nThe sum of the laps completed by a driver that year\n\n\nDriverRating\nFormula combining wins, finish, top15-finish, average running position while on lead lap, average speed under green, fastest lap, led most laps, and lead lap finish with a maximum rating of 150 points", + "crumbs": [ + "Home", + "Motor Sports", + "NASCAR Data Description" + ] + }, + { + "objectID": "motor_sports/NASCAR_driver_ratings.html#questions", + "href": "motor_sports/NASCAR_driver_ratings.html#questions", + "title": "NASCAR Data Description", + "section": "Questions", + "text": "Questions\n\nWhat is the best way to model the relationship between average finish and driver rating? Are the linear model assumptions met?\nWhich predictors best predict driver rating?", + "crumbs": [ + "Home", + "Motor Sports", + "NASCAR Data Description" + ] + }, + { + "objectID": "motor_sports/NASCAR_driver_ratings.html#references", + "href": "motor_sports/NASCAR_driver_ratings.html#references", + "title": "NASCAR Data Description", + "section": "References", + "text": "References\nhttps://www.nascar.com/stats/", + "crumbs": [ + "Home", + "Motor Sports", + "NASCAR Data Description" + ] + }, + { + "objectID": "tennis/wta-grand-slam-matches.html", + "href": "tennis/wta-grand-slam-matches.html", + "title": "WTA Grand Slam Matches", "section": "", - "text": "The Banzuke is a ranking system used in Japanese sumo wrestling to determine the order and matchups for tournaments. In the Makuuchi Banzuke division, which is the top division, the history can be traced back to the 18th century. The Banzuke lists the wrestlers’ ranks, names, stables, and more in a hierarchical order based on their performance and records in previous tournaments. Over the years, the Makuuchi Banzuke has evolved, incorporating changes in the number of ranks and divisions. Today, it plays a crucial role in organizing and structuring sumo tournaments, reflecting the wrestlers’ skill, rank, and accomplishments in the sport." + "text": "The Women’s Tennis Associate (WTA) organizes the top women’s professional tennis tour in the world. Throughout the year, there are four major tournaments yielding the most ranking points, prize money, and fame. These are known as the Grand Slam tournaments, consisting of (in order): Australian Open, French Open (aka Roland Garros), Wimbledon, and the US Open. With this dataset of information about winners and losers in WTA Grand Slam matches from 2018 to 2022, you’ll be able to explore statistics collected during matches and information about the athletes to predict match outcomes.", + "crumbs": [ + "Home", + "Tennis", + "WTA Grand Slam Matches" + ] }, { - "objectID": "wrestling/sumo_wrestingling_since_1957.html#motivation", - "href": "wrestling/sumo_wrestingling_since_1957.html#motivation", - "title": "Sumo wrestler characteristics", + "objectID": "tennis/wta-grand-slam-matches.html#motivation", + "href": "tennis/wta-grand-slam-matches.html#motivation", + "title": "WTA Grand Slam Matches", "section": "", - "text": "The Banzuke is a ranking system used in Japanese sumo wrestling to determine the order and matchups for tournaments. In the Makuuchi Banzuke division, which is the top division, the history can be traced back to the 18th century. The Banzuke lists the wrestlers’ ranks, names, stables, and more in a hierarchical order based on their performance and records in previous tournaments. Over the years, the Makuuchi Banzuke has evolved, incorporating changes in the number of ranks and divisions. Today, it plays a crucial role in organizing and structuring sumo tournaments, reflecting the wrestlers’ skill, rank, and accomplishments in the sport." + "text": "The Women’s Tennis Associate (WTA) organizes the top women’s professional tennis tour in the world. Throughout the year, there are four major tournaments yielding the most ranking points, prize money, and fame. These are known as the Grand Slam tournaments, consisting of (in order): Australian Open, French Open (aka Roland Garros), Wimbledon, and the US Open. With this dataset of information about winners and losers in WTA Grand Slam matches from 2018 to 2022, you’ll be able to explore statistics collected during matches and information about the athletes to predict match outcomes.", + "crumbs": [ + "Home", + "Tennis", + "WTA Grand Slam Matches" + ] }, { - "objectID": "wrestling/sumo_wrestingling_since_1957.html#data", - "href": "wrestling/sumo_wrestingling_since_1957.html#data", - "title": "Sumo wrestler characteristics", + "objectID": "tennis/wta-grand-slam-matches.html#data", + "href": "tennis/wta-grand-slam-matches.html#data", + "title": "WTA Grand Slam Matches", "section": "Data", - "text": "Data\nThe data set has 15760 rows with 10 columns. Each row represents an appearance of a sumo wrestler in the Makuuchi Banzuke, Japan’s most elite sumo wrestling division. The data dates from 1957 through mid 2023 and is comprised of multiple Bashos (tournaments) each year. An addition to the standard columns of wrestler names and records, the data contains many other metrics pertaining to each wrestlers birth place and physical stature.\n\nsumo_since_1957.csv\n\n\nVariable\nDescription\n\n\n\n\nRank\nRank of the wrestler in the Basho\n\n\nRikishi\nThe ring name of a the sumo wrestler\n\n\nHeya\nThe establishment where a wrestler trains\n\n\nShusshin\nThe birthplace, or place of origin of the wrestler\n\n\nBirth Date\nThe birth date of the wrestler\n\n\nheight_cm\nThe height of the wrestler at the time of the event in centimeters\n\n\nweight_kg\nThe weight of the wrestler at the time of the even in kilograms\n\n\nwins\nAmount of wins in the specific Basho\n\n\nlosses\nAmount of losses in the specific Basho\n\n\nties\nAmount of ties in the specific Basho" + "text": "Data\nThis dataset contains all WTA matches between 2018 and 2022, courtesy of Jeff Sackmann’s famous tennis repository.\nThere are 2,413 rows in this dataset where each row corresponds to a single WTA Grand Slam match. Each row has 38 columns with general information about the matches, as well as columns describing the winner and loser of the matches:\n\nwta-grand-slam-matches-2018to2022.csv\n\n\nVariable\nDescription\n\n\n\n\ntourney_name\nname of the Grand Slam Tournament (French Open is recorded as ROLAND GARROS)\n\n\nsurface\ntype of court surface\n\n\ntourney_date\neight digits, YYYYMMDD, usually the Monday of the tournament week\n\n\nwinner/loser_seed\nseed of winning/losing player\n\n\nwinner/loser_name\nName of the winning/losing player\n\n\nwinner/loser_hand\nR = right, L = left, U = unknown. For ambidextrous players, this is their serving hand\n\n\nwinner/loser_ht\nheight in centimeters, where available\n\n\nwinner/loser_ioc\nthree-character country code\n\n\nwinner/loser_age\nage, in years, as of the tourney_date\n\n\nscore\nfinal match score\n\n\nround\ntournament round\n\n\nminutes\nmatch length in minutes\n\n\nw/l_ace\nwinner/loser’s number of aces\n\n\nw/l_df\nwinner/loser’s number of doubles faults\n\n\nw/l_svpt\nwinner/loser’s number of serve points\n\n\nw/l_1stIn\nwinner/loser’s number of first serves made\n\n\nw/l_1stWon\nwinner/loser’s number of first-serve points won\n\n\nw/l_2ndWon\nwinner/loser’s number of second-serve points won\n\n\nw/l_SvGms\nwinner/loser’s number of serve games\n\n\nw/l_bpSaved\nwinner/loser’s number of break points saved\n\n\nw/l_bpFaced\nwinner/loser’s number of break points faced\n\n\nwinner/loser_rank\nwinner/loser’s WTA rank, as of the tourney_date, or the most recent ranking date before the tourney_date\n\n\n\nNote that a full glossary of the features available for match data can be found here.", + "crumbs": [ + "Home", + "Tennis", + "WTA Grand Slam Matches" + ] }, { - "objectID": "wrestling/sumo_wrestingling_since_1957.html#questions", - "href": "wrestling/sumo_wrestingling_since_1957.html#questions", - "title": "Sumo wrestler characteristics", + "objectID": "tennis/wta-grand-slam-matches.html#questions", + "href": "tennis/wta-grand-slam-matches.html#questions", + "title": "WTA Grand Slam Matches", "section": "Questions", - "text": "Questions\n\nWhich place of origin has resulted in the most sumo wrestlers?\nWhich wrestlers train at Kataonami?\nWhich wrestlers have the highest win percentage?\nWhich wrestlers have had the most successful careers?" + "text": "Questions\n\nAfter performing the appropriate data wrangling, build a logistic regression model to predict whether or not the seed favorite wins the match based on the athlete information and recorded match statistics. Which variables, if any, are predictive of the match outcome? Describe their relationships.\nChoose an athlete of interest, e.g., Serena Williams. Create a new dataset where the columns describe the performance of that athlete in each particular match. Explore how the different match information and statistics correspond to whether or not the athlete is able to win the match.\nExplore the relationship between court surface and the various observed match statistics. This could be done by clustering matches based on the winner and loser statistics, and see how well aligned they are with the court surface type or Grand Slam event.", + "crumbs": [ + "Home", + "Tennis", + "WTA Grand Slam Matches" + ] }, { - "objectID": "wrestling/sumo_wrestingling_since_1957.html#references", - "href": "wrestling/sumo_wrestingling_since_1957.html#references", - "title": "Sumo wrestler characteristics", + "objectID": "tennis/wta-grand-slam-matches.html#references", + "href": "tennis/wta-grand-slam-matches.html#references", + "title": "WTA Grand Slam Matches", "section": "References", - "text": "References\nSumo Reference - Alexander Nitschke https://sumodb.sumogames.de\nThe Sumo Matchup Centuries In The Making - FiveThirtyEight https://fivethirtyeight.com/features/the-sumo-matchup-centuries-in-the-making/" + "text": "References\nData was accessed from Jeff Sackmann’s tennis GitHub repository.", + "crumbs": [ + "Home", + "Tennis", + "WTA Grand Slam Matches" + ] }, { - "objectID": "hockey/phf-shots.html", - "href": "hockey/phf-shots.html", - "title": "Premier Hockey Federation Shots", + "objectID": "running/boston_marathon_2023.html", + "href": "running/boston_marathon_2023.html", + "title": "2023 Boston Marathon runners", "section": "", - "text": "The Premier Hockey Federation (PHF) is a professional women’s hockey league in North America. Recently, more data has become available for people to explore and join the women’s hockey analytics community. This dataset lets you explore shots taken during the 2021-2022 PHF season." + "text": "The Boston Marathon is an annual road race that takes place in the City of Boston Massachusetts. Attracting thousands of runners of varying skill and experience, year after year participants from around the world gather to compete for world records, divisional records, and personal bests. Examining the results of these athletes yields an interesting look into the spread and distribution of the times of all runners who successfully completed the historic race.", + "crumbs": [ + "Home", + "Running", + "2023 Boston Marathon runners" + ] }, { - "objectID": "hockey/phf-shots.html#motivation", - "href": "hockey/phf-shots.html#motivation", - "title": "Premier Hockey Federation Shots", + "objectID": "running/boston_marathon_2023.html#motivation", + "href": "running/boston_marathon_2023.html#motivation", + "title": "2023 Boston Marathon runners", "section": "", - "text": "The Premier Hockey Federation (PHF) is a professional women’s hockey league in North America. Recently, more data has become available for people to explore and join the women’s hockey analytics community. This dataset lets you explore shots taken during the 2021-2022 PHF season." + "text": "The Boston Marathon is an annual road race that takes place in the City of Boston Massachusetts. Attracting thousands of runners of varying skill and experience, year after year participants from around the world gather to compete for world records, divisional records, and personal bests. Examining the results of these athletes yields an interesting look into the spread and distribution of the times of all runners who successfully completed the historic race.", + "crumbs": [ + "Home", + "Running", + "2023 Boston Marathon runners" + ] }, { - "objectID": "hockey/phf-shots.html#data", - "href": "hockey/phf-shots.html#data", - "title": "Premier Hockey Federation Shots", + "objectID": "running/boston_marathon_2023.html#data", + "href": "running/boston_marathon_2023.html#data", + "title": "2023 Boston Marathon runners", "section": "Data", - "text": "Data\nThis dataset contains information about 1,502 shots during the 2021-2022 PHF season.\nThe data was collected using the fastRhockey package in R.\n\nphf-shots-2021.csv\n\n\nVariable\nDescription\n\n\n\n\nplay_description\nString detailed description of event\n\n\nplay_type\nString denoting the outcome of the shot, either Goal, PP Goal (meaning power play goal), SH Goal, (meaning shorthanded goal), Shot (shot saved by goalie), or Shot BLK (meaning blocked by a non-goalie)\n\n\nperiod_id\nInteger value of the game period\n\n\ntime_remaining\nString display of time remaining in period in MM:SS format where MM and SS denotes minutes and seconds remaning respectively\n\n\nsec_from_start\nNumeric value of the seconds since the start of the game\n\n\nhome_team\nString name of the home team\n\n\naway_team\nString name of the away team\n\n\nhome_goals\nInteger value of the home team score after the event\n\n\naway_goals\nInteger value of the away team score after the event\n\n\nshooting_team\nString defining the team taking the shot\n\n\nplayer_name_1\nString name of the player taking the shot\n\n\nplayer_name_2\nString name of the secondary event player (blocker or goalie)\n\n\ngoalie_involved\nString name of the goalie involved in the shot attempt\n\n\nshot_result\nString denoting the outcome of the shot, either blocked (meaning blocked by a non-goalie), made (goal), or saved (shot on net that was saved by a goalie)\n\n\non_ice_situation\nString indicator for game strength: Even Strength or Power Play\n\n\nhome_score_total\nInteger value of the home team score at the end of the game\n\n\naway_score_total\nInteger value of the away team score at the end of the game" + "text": "Data\nThe data set contains 26598 rows and 15 columns. Each row represents a runner who completed the Boston Marathon in 2023\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nage_group\nage group of the runner\n\n\nplace_overall\nfinishing place of the runner out of all runners\n\n\nplace_gender\nfinishing place of runner among the same gender\n\n\nplace_division\nfinishing place of runner among runners of the same gender and age group\n\n\nname\nname of runner\n\n\ngender\ngender of runner\n\n\nteam\nteam the runner is affiliated with\n\n\nbib_number\nbib number of runner\n\n\nhalf_time\nhalf marathon time of runner\n\n\nfinish_net\nfinishing time timed from when they cross the starting gate\n\n\nfinish_gun\nfinishing time of runner timed from when the starter gun is fired\n\n\nage_group\nage group of the runner\n\n\nhalf_time_sec\nhalf marathon time in seconds\n\n\nfinish_net_sec\nnet finish in seconds\n\n\nfinish_gun_sec\ngun finish in seconds\n\n\n\n\nData file\n\nboston_marathon_2023.csv", + "crumbs": [ + "Home", + "Running", + "2023 Boston Marathon runners" + ] }, { - "objectID": "hockey/phf-shots.html#questions", - "href": "hockey/phf-shots.html#questions", - "title": "Premier Hockey Federation Shots", + "objectID": "running/boston_marathon_2023.html#questions", + "href": "running/boston_marathon_2023.html#questions", + "title": "2023 Boston Marathon runners", "section": "Questions", - "text": "Questions\n\nUsing the appropriate statistical test, assess whether or not shot_result is independent of the on_ice_situation.\nCount the number of shots taken by each team. Build a logistic regression model to predict the probability of the home team winning the game based on the number of attempted shots by each team." + "text": "Questions\n\nWhat summary statistics are relevant to examine for finishing time?\nWhich race times would be considered outliers?\nAre there extreme outliers?\nWhich runner, the top male or top female, was more remarkable in comparison to their respective fields?", + "crumbs": [ + "Home", + "Running", + "2023 Boston Marathon runners" + ] }, { - "objectID": "hockey/phf-shots.html#references", - "href": "hockey/phf-shots.html#references", - "title": "Premier Hockey Federation Shots", + "objectID": "running/boston_marathon_2023.html#references", + "href": "running/boston_marathon_2023.html#references", + "title": "2023 Boston Marathon runners", "section": "References", - "text": "References\nHowell B, Gilani S (2022). fastRhockey: Functions to Access Premier Hockey Federation and National Hockey League Play by Play Data. R package version 0.4.0, https://CRAN.R-project.org/package=fastRhockey." + "text": "References\nBoston Athletic Association\nhttps://www.baa.org", + "crumbs": [ + "Home", + "Running", + "2023 Boston Marathon runners" + ] }, { - "objectID": "baseball/verlander-pitches.html", - "href": "baseball/verlander-pitches.html", - "title": "Justin Verlander Pitches", + "objectID": "soccer/D3_wsoc2022.html", + "href": "soccer/D3_wsoc2022.html", + "title": "2022 Divison III Women’s Soccer Results", "section": "", - "text": "After nearly two full seasons due to injury, at the age of 39 Justin Verlander returned for the 2022 season to win the American League Cy Young award and World Series with the Houston Astros. Leading the league in a variety of statistics, Verlander dominated in his starts throughout the season. Pitch selection has played a key role into Verlander’s recent success with the Astros. Verlander throws four types of pitches (using MLB’s abbreviation): fastball (FF), slider (SL), curveball (CB), and changeup (CH). However, pitches are thrown in the context of an at-bat where the ball-strike count starts 0-0, and progresses until either the batter strikes out (reaches three strikes), is walked (reaches four balls), or is either hit-by-pitch or hits the ball in-play. As the count varies, pitchers often decide to favor certain pitches over others, e.g, with three balls (i.e., 3-X counts) the pitcher may favor throwing more accurate fastballs relative to out-of-the-zone offspeed pitches that are favored with two strikes (i.e., X-2 counts)." + "text": "Have you ever wondered if you know how many goals the home team scored in a soccer game then can you predict how many goals the opponent scored? Is there a relationship between this? With this data set on NCAA Division III women’s soccer data from the 2022 season you will be able to explore this question.", + "crumbs": [ + "Home", + "Soccer", + "2022 Divison III Women's Soccer Results" + ] }, { - "objectID": "baseball/verlander-pitches.html#motivation", - "href": "baseball/verlander-pitches.html#motivation", - "title": "Justin Verlander Pitches", + "objectID": "soccer/D3_wsoc2022.html#motivation", + "href": "soccer/D3_wsoc2022.html#motivation", + "title": "2022 Divison III Women’s Soccer Results", "section": "", - "text": "After nearly two full seasons due to injury, at the age of 39 Justin Verlander returned for the 2022 season to win the American League Cy Young award and World Series with the Houston Astros. Leading the league in a variety of statistics, Verlander dominated in his starts throughout the season. Pitch selection has played a key role into Verlander’s recent success with the Astros. Verlander throws four types of pitches (using MLB’s abbreviation): fastball (FF), slider (SL), curveball (CB), and changeup (CH). However, pitches are thrown in the context of an at-bat where the ball-strike count starts 0-0, and progresses until either the batter strikes out (reaches three strikes), is walked (reaches four balls), or is either hit-by-pitch or hits the ball in-play. As the count varies, pitchers often decide to favor certain pitches over others, e.g, with three balls (i.e., 3-X counts) the pitcher may favor throwing more accurate fastballs relative to out-of-the-zone offspeed pitches that are favored with two strikes (i.e., X-2 counts)." + "text": "Have you ever wondered if you know how many goals the home team scored in a soccer game then can you predict how many goals the opponent scored? Is there a relationship between this? With this data set on NCAA Division III women’s soccer data from the 2022 season you will be able to explore this question.", + "crumbs": [ + "Home", + "Soccer", + "2022 Divison III Women's Soccer Results" + ] }, { - "objectID": "baseball/verlander-pitches.html#data", - "href": "baseball/verlander-pitches.html#data", - "title": "Justin Verlander Pitches", + "objectID": "soccer/D3_wsoc2022.html#data", + "href": "soccer/D3_wsoc2022.html#data", + "title": "2022 Divison III Women’s Soccer Results", "section": "Data", - "text": "Data\nBelow are two datasets containing information about pitches thrown by Justin Verlander during the 2019 and 2022 seasons (with separate datasets for each season). Each dataset includes the pitch type, ball-strike count, release speed, and a description of what happened on the pitch.\nData courtesy of baseballsavant.mlb.com and accessed using the baseballr package.\n\nverlander-pitches-2019.csv, verlander-pitches-2022.csv\n\n\nVariable\nDescription\n\n\n\n\npitch_type\nThe type of pitch Verlander threw, either: fastball (FF), slider (SL), curveball (CU), or changeup (CH).\n\n\ncount\nThe ball-strike count when Verlander threw the pitch.\n\n\nrelease_speed\nPitch speed (MPH) at the time of release.\n\n\ndescription\nA brief text description of the result of the pitch, such as a ball, foul, called_strike, swinging_strike, etc." + "text": "Data\nThe data set consists of 3753 games among 431 teams in the 2022 Division III women’s soccer season. Each row represents one game in the season.\n\nD3_wsoc2022.csv\n\n\nVariable\nDescription\n\n\n\n\naway_team\nName away team\n\n\naway_score\nThe number of goals the away team scored in game\n\n\nhome_team\nName of home team\n\n\nhome_score\nThe number of goals the home team scored in game\n\n\ndate\nDate of game", + "crumbs": [ + "Home", + "Soccer", + "2022 Divison III Women's Soccer Results" + ] }, { - "objectID": "baseball/verlander-pitches.html#questions", - "href": "baseball/verlander-pitches.html#questions", - "title": "Justin Verlander Pitches", + "objectID": "soccer/D3_wsoc2022.html#questions", + "href": "soccer/D3_wsoc2022.html#questions", + "title": "2022 Divison III Women’s Soccer Results", "section": "Questions", - "text": "Questions\n\nUsing the appropriate statistical test, assess whether or not Justin Verlander’s pitch_type is independent of the count. Perform this analysis for both the 2019 and 2022 datasets. Are the conclusions similar or different?\nWhich combinations of pitch_type and count appear more than expected under the assumption of independence? Which combinations appear fewer than expected? Perform this analysis for both the 2019 and 2022 datasets. Are the conclusions similar or different?" + "text": "Questions\n\nDo you think the correlation value for Home Score vs. Away Score will be negative or positive? Explain your answer.\nFind the correlation value for Home Score vs. Away Score from the data set. Interpret the correlation value in context.\nPerform a test for association to see if the correlation value from above is statistically significant. Make sure to state the hypotheses clearly.\nDescribe the distribution of home scores or away scores. Would a normal (symmetric, bell-shaped) distribution be reasonable for scores?\nThere are a lot of games in the data set with many different scorelines that range from 0-0 all the way to 18-0. But there are very few games with high scores. Treat the scores as categories, but lump all of the larger scores into one category (e.g. 0, 1, 2, 3 or more). Create a two-way contingency table of home vs, away scores using these categories and test for an association.", + "crumbs": [ + "Home", + "Soccer", + "2022 Divison III Women's Soccer Results" + ] }, { - "objectID": "baseball/verlander-pitches.html#references", - "href": "baseball/verlander-pitches.html#references", - "title": "Justin Verlander Pitches", + "objectID": "soccer/D3_wsoc2022.html#references", + "href": "soccer/D3_wsoc2022.html#references", + "title": "2022 Divison III Women’s Soccer Results", "section": "References", - "text": "References\nPetti B, Gilani S (2022). baseballr: Acquiring and Analyzing Baseball Data. R package version 1.3.0, https://CRAN.R-project.org/package=baseballr." + "text": "References\nData web scraped from the D3soccer.com website. https://www.d3soccer.com/seasons/women/2022/schedule", + "crumbs": [ + "Home", + "Soccer", + "2022 Divison III Women's Soccer Results" + ] }, { - "objectID": "tennis/australian-open.html", - "href": "tennis/australian-open.html", - "title": "Australian Open Tennis (2008-2022)", + "objectID": "hockey/nhl_powerplay.html", + "href": "hockey/nhl_powerplay.html", + "title": "NHL Team Power Play Perfomance in 2022-2023", "section": "", - "text": "The Australian Open is an annual tennis tournament in Melbourne, Australia. We have data from the men’s tournament in the years 2008-2022, with variables for both the winner and loser of each match, along with information such as what hand they play with and their Association of Tennis Professionals (ATP) rank and characteristics of the players. Which characteristics might help predict the outcome of the matches?" + "text": "Certain infractions in a hockey game can lead to one or more players being forced to sit out for a period of time while their team has to skate with fewer payers. A common scenario that results is a 5 on 4 power play where one team has five skates (plus a goalie) and the penalized team has only four skaters (plus a goalie). The team with fewer skates is said to be shorthanded. Typical penalty times are for two minutes and the penalized player is allowed to return to the ice if the opponent scores a goal while on the power play. Rare, but more serious infractions, produce a major penalty (5 minutes) where the penalized player is not allowed to return to the ice when a goal is scored. A power play can also end before two minutes when the team on the power play commits a penalty which results in one of its players being sent off the ice to even the sides.\nThis dataset shows information for all 32 National Hockey League (NHL) teams based on their performance while on a 5 on 4 power play in the 2022-2023 regular season. Goals scored, shots on goals, and hits are all scaled to give a count of these events for every full two minutes of a power play.", + "crumbs": [ + "Home", + "Hockey", + "NHL Team Power Play Perfomance in 2022-2023" + ] }, { - "objectID": "tennis/australian-open.html#motivation", - "href": "tennis/australian-open.html#motivation", - "title": "Australian Open Tennis (2008-2022)", + "objectID": "hockey/nhl_powerplay.html#motivation", + "href": "hockey/nhl_powerplay.html#motivation", + "title": "NHL Team Power Play Perfomance in 2022-2023", "section": "", - "text": "The Australian Open is an annual tennis tournament in Melbourne, Australia. We have data from the men’s tournament in the years 2008-2022, with variables for both the winner and loser of each match, along with information such as what hand they play with and their Association of Tennis Professionals (ATP) rank and characteristics of the players. Which characteristics might help predict the outcome of the matches?" + "text": "Certain infractions in a hockey game can lead to one or more players being forced to sit out for a period of time while their team has to skate with fewer payers. A common scenario that results is a 5 on 4 power play where one team has five skates (plus a goalie) and the penalized team has only four skaters (plus a goalie). The team with fewer skates is said to be shorthanded. Typical penalty times are for two minutes and the penalized player is allowed to return to the ice if the opponent scores a goal while on the power play. Rare, but more serious infractions, produce a major penalty (5 minutes) where the penalized player is not allowed to return to the ice when a goal is scored. A power play can also end before two minutes when the team on the power play commits a penalty which results in one of its players being sent off the ice to even the sides.\nThis dataset shows information for all 32 National Hockey League (NHL) teams based on their performance while on a 5 on 4 power play in the 2022-2023 regular season. Goals scored, shots on goals, and hits are all scaled to give a count of these events for every full two minutes of a power play.", + "crumbs": [ + "Home", + "Hockey", + "NHL Team Power Play Perfomance in 2022-2023" + ] }, { - "objectID": "tennis/australian-open.html#data", - "href": "tennis/australian-open.html#data", - "title": "Australian Open Tennis (2008-2022)", + "objectID": "hockey/nhl_powerplay.html#data", + "href": "hockey/nhl_powerplay.html#data", + "title": "NHL Team Power Play Perfomance in 2022-2023", "section": "Data", - "text": "Data\nEach row of data gives 19 variables of information for one of the 1905 Men’s Australian Open matches played between 2008 and 2022.\n\nAustralianOpen.csv\n\n\nVariable\nDescription\n\n\n\n\nYear\nYear (2008-2022)\n\n\nWinner\nwinner of the match\n\n\nLoser\nloser of the match\n\n\nWRank\nwinning player’s rank\n\n\nLRank\nlosing player’s rank\n\n\npl1_flag\nwinning player’s country they choose to represent\n\n\npl1_year_pro\nwinning player’s first year as a professional\n\n\npl1_weight\nwinning player’s weight in kilograms\n\n\npl1_height\nwinning player’s height in centimeters\n\n\npl1_hand\nwinning player’s handedness that they play with\n\n\npl2_flag\nlosing player’s country they choose to represent\n\n\npl2_year_pro\nlosing player’s first year as a professional\n\n\npl2_weight\nlosing player’s weight in kilograms\n\n\npl2_height\nlosing player’s height in centimeters\n\n\npl2_hand\nlosing player’s handedness that they play with\n\n\nhigher_rank_won\nDid the higher rank win? Yes orNo\n\n\nrank_diff\ndifference in rank between the players\n\n\nrank_diff_cat\ncategroies for difference in rank between the players: Low (30 or less), Medium (31-75), High (more than 75)\n\n\nsame_handedness\nDo the players have the same handedness? Yes or No\n\n\n\n: AustralianOpenLater.csv has the same variables (with the addition of a Round variable), but only for 105 matches from the quarterfinals, semifinals, and finals each year." + "text": "Data\nEach row of data gives power play statistics for one of the 32 National Hockey League teams from the 2022-2023 regular season.\n\nNHL_PowerPlay2022.csv\n\n\nVariable\nDescription\n\n\n\n\nteam\nName of the team\n\n\ngoals_per_2_min\nHow many goals scored in 2 minute increments\n\n\nSOG_per_2_min\nHow many shots on goal were taken in a 2 minute increments\n\n\nhits_per_2_min\nAverage hits recorded for 2 minutes\n\n\nConference\nConference the team plays in (east vs west)\n\n\ngoal_differential\nPower play goals scored minus short-handed goals allowed\n\n\nIcetime\nTotal time spent on the power play (in minutes)", + "crumbs": [ + "Home", + "Hockey", + "NHL Team Power Play Perfomance in 2022-2023" + ] }, { - "objectID": "tennis/australian-open.html#questions", - "href": "tennis/australian-open.html#questions", - "title": "Australian Open Tennis (2008-2022)", + "objectID": "hockey/nhl_powerplay.html#questions", + "href": "hockey/nhl_powerplay.html#questions", + "title": "NHL Team Power Play Perfomance in 2022-2023", "section": "Questions", - "text": "Questions\n\nWhat proportion of time does the higher (ATP) rank player win?\nDoes the proportion of higher ranked wins change much when the players play with opposite hands (i.e. a right-handed player vs. a left-handed payer)\nConstruct a two-way table of higher_rank_won vs. rank_diff_cat and see if teh two variables appear to be related.\nInvestigate a logistic regression model to predict it the higher ranked player wins using the difference in ranks as the predictor.\nAre the results for any of these questions much different if we consider only matches in the later rounds for the tournament (quarterfinals, semifinals, and finals) in AustralianOpenLater.csv?" + "text": "Questions\n\nDescribe the distribution of goals-per-2min. Does any team appear to have unusually high or low values for this variable?\nCreate plot to look at the relationship between goals_per_2min and SOG_per_2min. Does it look like more shots tend to lead to more goals?\nCreate plot to look at the relationship between goals_per_2min and hits_per_2min. Does it look like more hits tend to lead to more goals?\nCreate a plot to compare the distribution of goal scoring rates while on the power play between the two conferences. Does there appear to be much difference between the two conferences?", + "crumbs": [ + "Home", + "Hockey", + "NHL Team Power Play Perfomance in 2022-2023" + ] }, { - "objectID": "tennis/australian-open.html#references", - "href": "tennis/australian-open.html#references", - "title": "Australian Open Tennis (2008-2022)", + "objectID": "hockey/nhl_powerplay.html#references", + "href": "hockey/nhl_powerplay.html#references", + "title": "NHL Team Power Play Perfomance in 2022-2023", "section": "References", - "text": "References\nThe data are a subset of the data found at Kaggle." + "text": "References\nThe data were obtained from the MoneyPuck website.", + "crumbs": [ + "Home", + "Hockey", + "NHL Team Power Play Perfomance in 2022-2023" + ] }, { - "objectID": "volleyball/volleyball_ncaa_team_stats.html", - "href": "volleyball/volleyball_ncaa_team_stats.html", - "title": "Team Statistics for Division I Women’s Volleyball", + "objectID": "hockey/phf-shots.html", + "href": "hockey/phf-shots.html", + "title": "Premier Hockey Federation Shots", "section": "", - "text": "Division 1 NCAA women’s volleyball is the highest level of collegiate competition for women’s volleyball in the United States. With well over 300 teams compete at the Division 1 level, there is fierce competition each year as only 64 teams make the post-season Volleyball Championship. The division demands high-level athletic performances, teamwork, and strategic game play from a variety of aspects. With each team having their own strengths and weaknesses, there is no exception of exciting matchups and and shocking results.\nIn volleyball, a kill is awarded to a player any time their attack is unreturnable by the opposition because it is the direct cause of the opponent not returning the ball. An assist is awarded when a set, pass, or dig to a teammate results in that teammate attacking the ball for a kill." + "text": "The Premier Hockey Federation (PHF) is a professional women’s hockey league in North America. Recently, more data has become available for people to explore and join the women’s hockey analytics community. This dataset lets you explore shots taken during the 2021-2022 PHF season.", + "crumbs": [ + "Home", + "Hockey", + "Premier Hockey Federation Shots" + ] }, { - "objectID": "volleyball/volleyball_ncaa_team_stats.html#motivation", - "href": "volleyball/volleyball_ncaa_team_stats.html#motivation", - "title": "Team Statistics for Division I Women’s Volleyball", + "objectID": "hockey/phf-shots.html#motivation", + "href": "hockey/phf-shots.html#motivation", + "title": "Premier Hockey Federation Shots", "section": "", - "text": "Division 1 NCAA women’s volleyball is the highest level of collegiate competition for women’s volleyball in the United States. With well over 300 teams compete at the Division 1 level, there is fierce competition each year as only 64 teams make the post-season Volleyball Championship. The division demands high-level athletic performances, teamwork, and strategic game play from a variety of aspects. With each team having their own strengths and weaknesses, there is no exception of exciting matchups and and shocking results.\nIn volleyball, a kill is awarded to a player any time their attack is unreturnable by the opposition because it is the direct cause of the opponent not returning the ball. An assist is awarded when a set, pass, or dig to a teammate results in that teammate attacking the ball for a kill." + "text": "The Premier Hockey Federation (PHF) is a professional women’s hockey league in North America. Recently, more data has become available for people to explore and join the women’s hockey analytics community. This dataset lets you explore shots taken during the 2021-2022 PHF season.", + "crumbs": [ + "Home", + "Hockey", + "Premier Hockey Federation Shots" + ] }, { - "objectID": "volleyball/volleyball_ncaa_team_stats.html#data", - "href": "volleyball/volleyball_ncaa_team_stats.html#data", - "title": "Team Statistics for Division I Women’s Volleyball", + "objectID": "hockey/phf-shots.html#data", + "href": "hockey/phf-shots.html#data", + "title": "Premier Hockey Federation Shots", "section": "Data", - "text": "Data\nThe data set contains 334 rows and 14 columns. Each row represents a team at the NCAA Division 1 level from the 2022-2023 season.\n\nvolleyball_ncaa_div1_2022_23.csv\n\n\nVariable\nDescription\n\n\n\n\nTeam\ncollege of the volleyball team\n\n\nConference\nconference to which the team belongs\n\n\nregion\nregion to which the team belongs\n\n\naces_per_set\nthe average amount of balls served that directly lead to a point not including errors by the opponent per set\n\n\nassists_per_set\nthe average amount of sets, passes, or digs to a teammate that directly result in a kill per set\n\n\nteam_attacks_per_set\nthe average amount of times the ball is sent to the other team’s court per set\n\n\nblocks_per_set\nthe average amount of times the ball is blocked from being hit on to the teams side per set\n\n\ndigs_per_set\naverage amount of times the ball is passed by a player successfully after an opponents attack per set\n\n\nhitting_pctg\ntotal team kills minus team hitting errors all divided by total attempts\n\n\nkills_per_set\naverage amount of hits that directly result in a point per set\n\n\nopp_hitting_pctg\nthe average hitting percentage of the teams opponent per set\n\n\nW\nthe amount of team wins for the season\n\n\nL\nthe amount of team losses for the season\n\n\nwin_loss_pctg\nthe amount of total wins divided by the total matches of the season" + "text": "Data\nThis dataset contains information about 1,502 shots during the 2021-2022 PHF season.\nThe data was collected using the fastRhockey package in R.\n\nphf-shots-2021.csv\n\n\nVariable\nDescription\n\n\n\n\nplay_description\nString detailed description of event\n\n\nplay_type\nString denoting the outcome of the shot, either Goal, PP Goal (meaning power play goal), SH Goal, (meaning shorthanded goal), Shot (shot saved by goalie), or Shot BLK (meaning blocked by a non-goalie)\n\n\nperiod_id\nInteger value of the game period\n\n\ntime_remaining\nString display of time remaining in period in MM:SS format where MM and SS denotes minutes and seconds remaning respectively\n\n\nsec_from_start\nNumeric value of the seconds since the start of the game\n\n\nhome_team\nString name of the home team\n\n\naway_team\nString name of the away team\n\n\nhome_goals\nInteger value of the home team score after the event\n\n\naway_goals\nInteger value of the away team score after the event\n\n\nshooting_team\nString defining the team taking the shot\n\n\nplayer_name_1\nString name of the player taking the shot\n\n\nplayer_name_2\nString name of the secondary event player (blocker or goalie)\n\n\ngoalie_involved\nString name of the goalie involved in the shot attempt\n\n\nshot_result\nString denoting the outcome of the shot, either blocked (meaning blocked by a non-goalie), made (goal), or saved (shot on net that was saved by a goalie)\n\n\non_ice_situation\nString indicator for game strength: Even Strength or Power Play\n\n\nhome_score_total\nInteger value of the home team score at the end of the game\n\n\naway_score_total\nInteger value of the away team score at the end of the game", + "crumbs": [ + "Home", + "Hockey", + "Premier Hockey Federation Shots" + ] }, { - "objectID": "volleyball/volleyball_ncaa_team_stats.html#questions", - "href": "volleyball/volleyball_ncaa_team_stats.html#questions", - "title": "Team Statistics for Division I Women’s Volleyball", + "objectID": "hockey/phf-shots.html#questions", + "href": "hockey/phf-shots.html#questions", + "title": "Premier Hockey Federation Shots", "section": "Questions", - "text": "Questions\n\nWhat variables are most highly correlated with win/loss percentage of a team?\nHow are assists and kills related?\nWhat model is best for predicting win/loss percentage?" + "text": "Questions\n\nUsing the appropriate statistical test, assess whether or not shot_result is independent of the on_ice_situation.\nCount the number of shots taken by each team. Build a logistic regression model to predict the probability of the home team winning the game based on the number of attempted shots by each team.", + "crumbs": [ + "Home", + "Hockey", + "Premier Hockey Federation Shots" + ] }, { - "objectID": "volleyball/volleyball_ncaa_team_stats.html#references", - "href": "volleyball/volleyball_ncaa_team_stats.html#references", - "title": "Team Statistics for Division I Women’s Volleyball", + "objectID": "hockey/phf-shots.html#references", + "href": "hockey/phf-shots.html#references", + "title": "Premier Hockey Federation Shots", "section": "References", - "text": "References\nNCAA - Women’s Volleyball\nhttps://www.ncaa.com/stats/volleyball-women/d1" + "text": "References\nHowell B, Gilani S (2022). fastRhockey: Functions to Access Premier Hockey Federation and National Hockey League Play by Play Data. R package version 0.4.0, https://CRAN.R-project.org/package=fastRhockey.", + "crumbs": [ + "Home", + "Hockey", + "Premier Hockey Federation Shots" + ] + }, + { + "objectID": "golf/pga_drive_putt.html", + "href": "golf/pga_drive_putt.html", + "title": "Statistics of PGA Tournaments During the 2022 Season", + "section": "", + "text": "A common expression among golfers is “Drive for show, putt for dough.” This implies that the long initial tee shots (drives) on each hole are impressive, but the real key to success is the final strokes rolling the ball along the green into the hole (putts). Do data support this adage? The dataset for this activity was obtained from the PGA statistics website. Cases include all golfers who made the cut in each of 19 PGA tournaments in 2022. The dataset includes variables for driving ability, putting ability, and measuring success in the tournament. The “driving” variables include average driving distance (avgDriveDist), driving accuracy percentage (drivePct), and strokes gained off the tee (driveSG). The “putting” variables are average putts per round (avgPuttsPerRound), one putt percentage (onePuttPct), and strokes gained putting (puttsSG). The variables to measure success are scoring average (avgScore), official money won (Money), and Fedex Cup points (Points).", + "crumbs": [ + "Home", + "Golf", + "Statistics of PGA Tournaments During the 2022 Season" + ] + }, + { + "objectID": "golf/pga_drive_putt.html#motivation", + "href": "golf/pga_drive_putt.html#motivation", + "title": "Statistics of PGA Tournaments During the 2022 Season", + "section": "", + "text": "A common expression among golfers is “Drive for show, putt for dough.” This implies that the long initial tee shots (drives) on each hole are impressive, but the real key to success is the final strokes rolling the ball along the green into the hole (putts). Do data support this adage? The dataset for this activity was obtained from the PGA statistics website. Cases include all golfers who made the cut in each of 19 PGA tournaments in 2022. The dataset includes variables for driving ability, putting ability, and measuring success in the tournament. The “driving” variables include average driving distance (avgDriveDist), driving accuracy percentage (drivePct), and strokes gained off the tee (driveSG). The “putting” variables are average putts per round (avgPuttsPerRound), one putt percentage (onePuttPct), and strokes gained putting (puttsSG). The variables to measure success are scoring average (avgScore), official money won (Money), and Fedex Cup points (Points).", + "crumbs": [ + "Home", + "Golf", + "Statistics of PGA Tournaments During the 2022 Season" + ] + }, + { + "objectID": "golf/pga_drive_putt.html#data", + "href": "golf/pga_drive_putt.html#data", + "title": "Statistics of PGA Tournaments During the 2022 Season", + "section": "Data", + "text": "Data\nEach row of data gives the measures for one golfer in one tournament. The dataset covers 19 PGA tournaments from the 2022 season with 1387 cases in all. Each tournament consists of four rounds of golf. Some golfers are eliminated after the first two (or sometimes three) rounds. Only players who competed in all four rounds (i.e. those that made the “cut”) are included in this dataset.\n\nPGA2022.csv\n\n\nVariable\nDescription\n\n\n\n\nplayerName\nName of the player\n\n\ncountry\nThe country where the player is from\n\n\navgDriveDist\nAverage driving distance (in yards)\n\n\ndrivePct\nPercentage of times a tee shot comes to rest in the fairway\n\n\ndriveSG\nStrokes gained off the tee measures player performance off the tee on all par 4s and par 5s of how much better or worse a player’s drive is than the PGA Tour average\n\n\navgPuttsPerRound\nAverage number of total putts per round\n\n\nonePuttPct\nPercentage of times it took one putt to get the ball into the hole\n\n\nputtsSG\nStrokes gained putting measures how many strokes a player gains or loses on the greens compared to the PGA Tour average\n\n\navgScore\nThe scoring average is the total strokes divided by the total rounds\n\n\nMoney\nThe official money is the prize money award to the Professional members\n\n\nPoints\nFedexCup Regular Season Points are awarded points by finished position for performance in each tournament\n\n\nTournament\nThe tournament where the PGA Tour is taking place", + "crumbs": [ + "Home", + "Golf", + "Statistics of PGA Tournaments During the 2022 Season" + ] + }, + { + "objectID": "golf/pga_drive_putt.html#questions", + "href": "golf/pga_drive_putt.html#questions", + "title": "Statistics of PGA Tournaments During the 2022 Season", + "section": "Questions", + "text": "Questions\n\nAre driving or putting statistics generally more effective for predicting success in a PGA tournament?\nWhich measure of success (avgScore, Money, or Points) appears to be easiest to predict with either driving or putting information?\nFor each measure of success, generate a scatterplot with one the putting or driving variables. Which of these plots suggest we might want to use a transformation of one of the variables to improve linearity?\nCan you find a transformation that improves the linearity of the relationship for one of the problematic cases in question #3?", + "crumbs": [ + "Home", + "Golf", + "Statistics of PGA Tournaments During the 2022 Season" + ] + }, + { + "objectID": "golf/pga_drive_putt.html#references", + "href": "golf/pga_drive_putt.html#references", + "title": "Statistics of PGA Tournaments During the 2022 Season", + "section": "References", + "text": "References\nThe data were obtained from the PGA statistics website.", + "crumbs": [ + "Home", + "Golf", + "Statistics of PGA Tournaments During the 2022 Season" + ] }, { "objectID": "basketball/wnba-shots.html", "href": "basketball/wnba-shots.html", "title": "Women’s National Basketball Association Shots", "section": "", - "text": "The Women’s National Basketball Association (WNBA) is the top professional women’s basketball league in the world. The league records every shot players take along with contextual information about the shot such as its location, a description of the shot type, as well as the outcome. With this dataset, you can predict the success of each shot attempt to compute the expected value of shot types and compare team decision making." + "text": "The Women’s National Basketball Association (WNBA) is the top professional women’s basketball league in the world. The league records every shot players take along with contextual information about the shot such as its location, a description of the shot type, as well as the outcome. With this dataset, you can predict the success of each shot attempt to compute the expected value of shot types and compare team decision making.", + "crumbs": [ + "Home", + "Basketball", + "Women's National Basketball Association Shots" + ] }, { "objectID": "basketball/wnba-shots.html#motivation", "href": "basketball/wnba-shots.html#motivation", "title": "Women’s National Basketball Association Shots", "section": "", - "text": "The Women’s National Basketball Association (WNBA) is the top professional women’s basketball league in the world. The league records every shot players take along with contextual information about the shot such as its location, a description of the shot type, as well as the outcome. With this dataset, you can predict the success of each shot attempt to compute the expected value of shot types and compare team decision making." + "text": "The Women’s National Basketball Association (WNBA) is the top professional women’s basketball league in the world. The league records every shot players take along with contextual information about the shot such as its location, a description of the shot type, as well as the outcome. With this dataset, you can predict the success of each shot attempt to compute the expected value of shot types and compare team decision making.", + "crumbs": [ + "Home", + "Basketball", + "Women's National Basketball Association Shots" + ] }, { "objectID": "basketball/wnba-shots.html#data", "href": "basketball/wnba-shots.html#data", "title": "Women’s National Basketball Association Shots", "section": "Data", - "text": "Data\nThis dataset contains information about 41,497 shots during the 2021-2022 WNBA season.\nThe data was collected using the wehoop package in R.\n\nwnba-shots-2021.csv.gz\n\n\nVariable\nDescription\n\n\n\n\ngame_id\nUnique integer ID for each WNBA game\n\n\ngame_play_number\nInteger indicating the recorded play number for the shot attempt, where 1 indicates the first play of the game\n\n\ndesc\nString detailed description of shot attempt\n\n\nshot_type\nString description of the shot type (e.g., dunk, layup, jump shot, etc.)\n\n\nmade_shot\nBoolean denoting if the shot was made (TRUE) or not (FALSE)\n\n\nshot_value\nNumeric value of the shot outcome (0 for shots that were not made, and a positive value for made shots)\n\n\ncoordinate_x\nHorizontal location in feet of shot attempt where the hoop would be located at 25 feet\n\n\ncoordinate_y\nVertical location in feet of shot attempt with respect to the target hoop (the hoop should be a little in front of 0 but the coordinate system is not exact)\n\n\nshooting_team\nString name of the team taking the shot\n\n\nhome_name\nString name of the home team\n\n\naway_name\nString name of the away team\n\n\nhome_score\nInteger value of the home team score after the shot\n\n\naway_score\nInteger value of the away team score after the shot\n\n\nqtr\nInteger denoting the quarter/period in the game\n\n\nquarter_seconds_remaining\nNumeric integer value for number of seconds remaining in quarter/period\n\n\ngame_seconds_remaining\nNumeric integer value for number of seconds remaining in game" + "text": "Data\nThis dataset contains information about 41,497 shots during the 2021-2022 WNBA season.\nThe data was collected using the wehoop package in R.\n\nwnba-shots-2021.csv.gz\n\n\nVariable\nDescription\n\n\n\n\ngame_id\nUnique integer ID for each WNBA game\n\n\ngame_play_number\nInteger indicating the recorded play number for the shot attempt, where 1 indicates the first play of the game\n\n\ndesc\nString detailed description of shot attempt\n\n\nshot_type\nString description of the shot type (e.g., dunk, layup, jump shot, etc.)\n\n\nmade_shot\nBoolean denoting if the shot was made (TRUE) or not (FALSE)\n\n\nshot_value\nNumeric value of the shot outcome (0 for shots that were not made, and a positive value for made shots)\n\n\ncoordinate_x\nHorizontal location in feet of shot attempt where the hoop would be located at 25 feet\n\n\ncoordinate_y\nVertical location in feet of shot attempt with respect to the target hoop (the hoop should be a little in front of 0 but the coordinate system is not exact)\n\n\nshooting_team\nString name of the team taking the shot\n\n\nhome_name\nString name of the home team\n\n\naway_name\nString name of the away team\n\n\nhome_score\nInteger value of the home team score after the shot\n\n\naway_score\nInteger value of the away team score after the shot\n\n\nqtr\nInteger denoting the quarter/period in the game\n\n\nquarter_seconds_remaining\nNumeric integer value for number of seconds remaining in quarter/period\n\n\ngame_seconds_remaining\nNumeric integer value for number of seconds remaining in game", + "crumbs": [ + "Home", + "Basketball", + "Women's National Basketball Association Shots" + ] }, { "objectID": "basketball/wnba-shots.html#questions", "href": "basketball/wnba-shots.html#questions", "title": "Women’s National Basketball Association Shots", "section": "Questions", - "text": "Questions\n\nBuild a classification model to predict the shot outcome based on the spatial x,y coordinates of the shot.\nCreate a visualization displaying the joint frequency of shot locations. Do there appear to be any clear modes of frequently taken shots? Create a conditional version of this display by shot outcome. Does the distribution shape vary by shot outcome? (You can also perform a similar analysis by team and shot type)." + "text": "Questions\n\nBuild a classification model to predict the shot outcome based on the spatial x,y coordinates of the shot.\nCreate a visualization displaying the joint frequency of shot locations. Do there appear to be any clear modes of frequently taken shots? Create a conditional version of this display by shot outcome. Does the distribution shape vary by shot outcome? (You can also perform a similar analysis by team and shot type).", + "crumbs": [ + "Home", + "Basketball", + "Women's National Basketball Association Shots" + ] }, { "objectID": "basketball/wnba-shots.html#references", "href": "basketball/wnba-shots.html#references", "title": "Women’s National Basketball Association Shots", "section": "References", - "text": "References\nGilani S, Hutchinson G (2022). wehoop: Access Women’s Basketball Play by Play Data. R package version 1.5.0, https://CRAN.R-project.org/package=wehoop." + "text": "References\nGilani S, Hutchinson G (2022). wehoop: Access Women’s Basketball Play by Play Data. R package version 1.5.0, https://CRAN.R-project.org/package=wehoop.", + "crumbs": [ + "Home", + "Basketball", + "Women's National Basketball Association Shots" + ] }, { - "objectID": "soccer/nwsl-team-stats.html", - "href": "soccer/nwsl-team-stats.html", - "title": "National Women’s Soccer League Team Statistics", + "objectID": "basketball/WNBA20Yrs.html", + "href": "basketball/WNBA20Yrs.html", + "title": "Women’s National Baskeball Assosiation game statistics", "section": "", - "text": "The National Women’s Soccer League (NWSL) is the top professional women’s soccer league in the United States. While a team’s record ultimately determines their ranking, goal differential (goals scored - goals conceded) is often a better indicator of a team’s ability. But what aspects of a team’s performance are related to their goal differential? The NWSL records a variety of statistics describing a team’s performance, such as the percentage of time they maintain possession, percentage of shots on target, etc. With this dataset, you can explore variation between teams as well as which statistics are relevant predictor variables of goal differential." + "text": "All data sets used in this worksheet are from the Wehoop Package.\nThe National Basketball Association (NBA) has dominated over the WNBA in popularity since the creation of the WNBA 50 years after that of the NBA. For this reason, there are way more data sets available for the NBA. Thus, there is a need for high quality WNBA data sets in order to run analysis on the players and teams. This data set can be used to demonstrate the process of cleaning, prepping, and determining quality of data.", + "crumbs": [ + "Home", + "Basketball", + "Women's National Baskeball Assosiation game statistics" + ] }, { - "objectID": "soccer/nwsl-team-stats.html#motivation", - "href": "soccer/nwsl-team-stats.html#motivation", - "title": "National Women’s Soccer League Team Statistics", + "objectID": "basketball/WNBA20Yrs.html#motivation", + "href": "basketball/WNBA20Yrs.html#motivation", + "title": "Women’s National Baskeball Assosiation game statistics", "section": "", - "text": "The National Women’s Soccer League (NWSL) is the top professional women’s soccer league in the United States. While a team’s record ultimately determines their ranking, goal differential (goals scored - goals conceded) is often a better indicator of a team’s ability. But what aspects of a team’s performance are related to their goal differential? The NWSL records a variety of statistics describing a team’s performance, such as the percentage of time they maintain possession, percentage of shots on target, etc. With this dataset, you can explore variation between teams as well as which statistics are relevant predictor variables of goal differential." + "text": "All data sets used in this worksheet are from the Wehoop Package.\nThe National Basketball Association (NBA) has dominated over the WNBA in popularity since the creation of the WNBA 50 years after that of the NBA. For this reason, there are way more data sets available for the NBA. Thus, there is a need for high quality WNBA data sets in order to run analysis on the players and teams. This data set can be used to demonstrate the process of cleaning, prepping, and determining quality of data.", + "crumbs": [ + "Home", + "Basketball", + "Women's National Baskeball Assosiation game statistics" + ] }, { - "objectID": "soccer/nwsl-team-stats.html#data", - "href": "soccer/nwsl-team-stats.html#data", - "title": "National Women’s Soccer League Team Statistics", + "objectID": "basketball/WNBA20Yrs.html#data", + "href": "basketball/WNBA20Yrs.html#data", + "title": "Women’s National Baskeball Assosiation game statistics", "section": "Data", - "text": "Data\nThis dataset contains statistics about the regular season performance for each NWSL team from 2016 to 2022 (excluding 2020 which was cancelled due to COVID).\nThe data was collected using the nwslR package in R.\n\nnwsl-team-stats.csv\n\n\nVariable\nDescription\n\n\n\n\nteam_name\nName of NWSL team\n\n\nseason\nRegular season year of team’s statistics\n\n\ngames_played\nNumber of games team played in season\n\n\ngoal_differential\nGoals scored - goals conceded\n\n\ngoals\nNumber of goals scores\n\n\ngoals_conceded\nNumber of goals conceded\n\n\ncross_accuracy\nPercent of crosses that were successful\n\n\ngoal_conversion_pct\nPercent of shots scored\n\n\npass_pct\nPass accuracy\n\n\npass_pct_opposition_half\nPass accuracy in opposition half\n\n\npossession_pct\nPercentage of overall ball possession the team had during the season\n\n\nshot_accuracy\nPercentage of shots on target\n\n\ntackle_success_pct\nPercent of successful tackles" + "text": "Data\nThe WNBA20Yrs data set contains 8920 rows and 9 columns. Each pair of rows represents a game played by two WNBA teams in one of the 2003 to 2022 regular seasons. The first row is for the away team and the second row is for the home team. The columns are as follows:\n\nWNBA20Yrs.csv\n\n\nVariable\nDescription\n\n\n\n\ngame_id\ngame id number\n\n\nseason\nseason number\n\n\nseason_type\nbinary predictor; 2 if regular season game; 3 if playoff game\n\n\ngame_date\ndate of the game\n\n\nteam_id\nteam id number\n\n\nteam_display_name\nfull team name (name and city)\n\n\nteam_winner\nBoolean; True if the team won the game\n\n\nopponent_team_id\nid number of the opponent\n\n\nteam_home_away\nWhere the game was played; either “home” or “away”", + "crumbs": [ + "Home", + "Basketball", + "Women's National Baskeball Assosiation game statistics" + ] }, { - "objectID": "soccer/nwsl-team-stats.html#questions", - "href": "soccer/nwsl-team-stats.html#questions", - "title": "National Women’s Soccer League Team Statistics", + "objectID": "basketball/WNBA20Yrs.html#questions", + "href": "basketball/WNBA20Yrs.html#questions", + "title": "Women’s National Baskeball Assosiation game statistics", "section": "Questions", - "text": "Questions\n\nBuild a linear regression model to predict goal differential as a function of the team’s statistics. Which variables, if any, are predictive of a team’s goal differential in a season? Describe their relationships.\nUsing principal component analysis, project the team seasons into a lower dimensional space using only the team statistics that do not include goals scored or conceded. Choose an appropriate number of principal components, and describe which statistics contribute to your selected number of components. Based on your results, which team statistics explain the most variation between team-seasons in the dataset? Which team statistics are independent of each other?" + "text": "Questions\nThe goal will be to create a worksheet for the students telling them we want to predict whether a team makes playoffs based on their record halfway through the season. First, they will need to clean the data based on prompts. We will ask them what they notice about total games played by each team across seasons. They should recognize that the total games recorded is inconsistent between teams and with online WNBA statistics. We will ask them why this is a problem and how the issue could be solved if we still wanted to run the original regression.\n\nCreate a table including a team’s mid season record, whether they would make playoffs based on that record (teams with the top 8 records make the playoffs), their record at the end of the season, and whether they made playoffs based on that record.\nWhat do you notice about the team IDs in this data set? Filter out the IDs we won’t be using and rename the teams so that the same IDs all have the most recent team name.\nWhat problem is there regarding number of games played by each team? What are some ways we could solve this problem?\nRun logistic Regression Models to predict whether a team makes playoffs based on their record at the halfway point of the season. If is significant? Interpret the results.", + "crumbs": [ + "Home", + "Basketball", + "Women's National Baskeball Assosiation game statistics" + ] }, { - "objectID": "soccer/nwsl-team-stats.html#references", - "href": "soccer/nwsl-team-stats.html#references", - "title": "National Women’s Soccer League Team Statistics", + "objectID": "basketball/WNBA20Yrs.html#references", + "href": "basketball/WNBA20Yrs.html#references", + "title": "Women’s National Baskeball Assosiation game statistics", + "section": "References", + "text": "References\nGilani S, Hutchinson G (2022). wehoop: Access Women’s Basketball Play by Play Data. R package version 1.5.0, https://CRAN.R-project.org/package=wehoop.", + "crumbs": [ + "Home", + "Basketball", + "Women's National Baskeball Assosiation game statistics" + ] + }, + { + "objectID": "esports/lol-buff-nerf.html", + "href": "esports/lol-buff-nerf.html", + "title": "League of Legends - Buffing and Nerfing", + "section": "", + "text": "League of Legends is a 5 v. 5 multiplayer online battle arena (MOBA) game developed by Riot Games. They collect data to evaluate the effect of every champion, adjusting and fine-tuning various aspects associated with each champion, to ensure fair and competitive gameplay. Through updates (patches) they can reduce the power of champions that are too strong (known as nerfing) or enhance the abilities of less successful champions (nerfing).", + "crumbs": [ + "Home", + "Esports", + "League of Legends - Buffing and Nerfing" + ] + }, + { + "objectID": "esports/lol-buff-nerf.html#motivation", + "href": "esports/lol-buff-nerf.html#motivation", + "title": "League of Legends - Buffing and Nerfing", + "section": "", + "text": "League of Legends is a 5 v. 5 multiplayer online battle arena (MOBA) game developed by Riot Games. They collect data to evaluate the effect of every champion, adjusting and fine-tuning various aspects associated with each champion, to ensure fair and competitive gameplay. Through updates (patches) they can reduce the power of champions that are too strong (known as nerfing) or enhance the abilities of less successful champions (nerfing).", + "crumbs": [ + "Home", + "Esports", + "League of Legends - Buffing and Nerfing" + ] + }, + { + "objectID": "esports/lol-buff-nerf.html#data", + "href": "esports/lol-buff-nerf.html#data", + "title": "League of Legends - Buffing and Nerfing", + "section": "Data", + "text": "Data\nEach row is one of the 162 League of Legends champions. The two datasets show play results (e.g. win rates) for two consecutive patches of the game (12.22 and 12.23).\n\nLOL_patch_12.22.csv, LOL_patch_12.23.csv\n\n\nVariable\nDescription\n\n\n\n\nName\nName of the champion\n\n\nRole\nRole of the champion in a game\n\n\nKDA\nAverage kills, deaths and assists associated with each champion\n\n\nWRate\nWin rates of each champion\n\n\nPickRate\nSPick rates of each champion\n\n\nRolePerc\nPercent of time the champion is used in the expected role\n\n\nBanPerc\nBan percentages associated with each champion", + "crumbs": [ + "Home", + "Esports", + "League of Legends - Buffing and Nerfing" + ] + }, + { + "objectID": "esports/lol-buff-nerf.html#questions", + "href": "esports/lol-buff-nerf.html#questions", + "title": "League of Legends - Buffing and Nerfing", + "section": "Questions", + "text": "Questions\n\nLook for outliers (e.g. in win rate) for the 12.22 patch to identify champions that might need to be buffed or nerfed.\nLook at results for the 12.23 patch to see if outliers identified from 12.22 might have been adjusted of if any different champions are now outliers.", + "crumbs": [ + "Home", + "Esports", + "League of Legends - Buffing and Nerfing" + ] + }, + { + "objectID": "esports/lol-buff-nerf.html#references", + "href": "esports/lol-buff-nerf.html#references", + "title": "League of Legends - Buffing and Nerfing", "section": "References", - "text": "References\nDror A (2023). nwslR: Compiles dataset for the National Women’s Soccer League (NWSL). R package version 0.0.0.9001." + "text": "References\nLol champion stats, 12.22 master, win rates. METAsrc. (n.d.). https://www.metasrc.com/5v5/12.22/stats?ranks=master\nLol champion stats, 12.23 master, win rates. METAsrc. (n.d.-b). https://www.metasrc.com/5v5/12.23/stats?ranks=master", + "crumbs": [ + "Home", + "Esports", + "League of Legends - Buffing and Nerfing" + ] + }, + { + "objectID": "baseball/verlander-pitches.html", + "href": "baseball/verlander-pitches.html", + "title": "Justin Verlander Pitches", + "section": "", + "text": "After nearly two full seasons due to injury, at the age of 39 Justin Verlander returned for the 2022 season to win the American League Cy Young award and World Series with the Houston Astros. Leading the league in a variety of statistics, Verlander dominated in his starts throughout the season. Pitch selection has played a key role into Verlander’s recent success with the Astros. Verlander throws four types of pitches (using MLB’s abbreviation): fastball (FF), slider (SL), curveball (CB), and changeup (CH). However, pitches are thrown in the context of an at-bat where the ball-strike count starts 0-0, and progresses until either the batter strikes out (reaches three strikes), is walked (reaches four balls), or is either hit-by-pitch or hits the ball in-play. As the count varies, pitchers often decide to favor certain pitches over others, e.g, with three balls (i.e., 3-X counts) the pitcher may favor throwing more accurate fastballs relative to out-of-the-zone offspeed pitches that are favored with two strikes (i.e., X-2 counts).", + "crumbs": [ + "Home", + "Baseball", + "Justin Verlander Pitches" + ] + }, + { + "objectID": "baseball/verlander-pitches.html#motivation", + "href": "baseball/verlander-pitches.html#motivation", + "title": "Justin Verlander Pitches", + "section": "", + "text": "After nearly two full seasons due to injury, at the age of 39 Justin Verlander returned for the 2022 season to win the American League Cy Young award and World Series with the Houston Astros. Leading the league in a variety of statistics, Verlander dominated in his starts throughout the season. Pitch selection has played a key role into Verlander’s recent success with the Astros. Verlander throws four types of pitches (using MLB’s abbreviation): fastball (FF), slider (SL), curveball (CB), and changeup (CH). However, pitches are thrown in the context of an at-bat where the ball-strike count starts 0-0, and progresses until either the batter strikes out (reaches three strikes), is walked (reaches four balls), or is either hit-by-pitch or hits the ball in-play. As the count varies, pitchers often decide to favor certain pitches over others, e.g, with three balls (i.e., 3-X counts) the pitcher may favor throwing more accurate fastballs relative to out-of-the-zone offspeed pitches that are favored with two strikes (i.e., X-2 counts).", + "crumbs": [ + "Home", + "Baseball", + "Justin Verlander Pitches" + ] + }, + { + "objectID": "baseball/verlander-pitches.html#data", + "href": "baseball/verlander-pitches.html#data", + "title": "Justin Verlander Pitches", + "section": "Data", + "text": "Data\nBelow are two datasets containing information about pitches thrown by Justin Verlander during the 2019 and 2022 seasons (with separate datasets for each season). Each dataset includes the pitch type, ball-strike count, release speed, and a description of what happened on the pitch.\nData courtesy of baseballsavant.mlb.com and accessed using the baseballr package.\n\nverlander-pitches-2019.csv, verlander-pitches-2022.csv\n\n\nVariable\nDescription\n\n\n\n\npitch_type\nThe type of pitch Verlander threw, either: fastball (FF), slider (SL), curveball (CU), or changeup (CH).\n\n\ncount\nThe ball-strike count when Verlander threw the pitch.\n\n\nrelease_speed\nPitch speed (MPH) at the time of release.\n\n\ndescription\nA brief text description of the result of the pitch, such as a ball, foul, called_strike, swinging_strike, etc.", + "crumbs": [ + "Home", + "Baseball", + "Justin Verlander Pitches" + ] + }, + { + "objectID": "baseball/verlander-pitches.html#questions", + "href": "baseball/verlander-pitches.html#questions", + "title": "Justin Verlander Pitches", + "section": "Questions", + "text": "Questions\n\nUsing the appropriate statistical test, assess whether or not Justin Verlander’s pitch_type is independent of the count. Perform this analysis for both the 2019 and 2022 datasets. Are the conclusions similar or different?\nWhich combinations of pitch_type and count appear more than expected under the assumption of independence? Which combinations appear fewer than expected? Perform this analysis for both the 2019 and 2022 datasets. Are the conclusions similar or different?", + "crumbs": [ + "Home", + "Baseball", + "Justin Verlander Pitches" + ] + }, + { + "objectID": "baseball/verlander-pitches.html#references", + "href": "baseball/verlander-pitches.html#references", + "title": "Justin Verlander Pitches", + "section": "References", + "text": "References\nPetti B, Gilani S (2022). baseballr: Acquiring and Analyzing Baseball Data. R package version 1.3.0, https://CRAN.R-project.org/package=baseballr.", + "crumbs": [ + "Home", + "Baseball", + "Justin Verlander Pitches" + ] }, { "objectID": "football/nfl-draft-combine.html", "href": "football/nfl-draft-combine.html", "title": "NFL Draft Combine", "section": "", - "text": "The data set has statistics with variables for player draft information and how they performed in events at the NFL Scouting Combine. The variables for draft information are position and Round. The variables for the NFL Scouting Combine events are forty, vertical, bench_reps, broad_jump, and shuttle. Some entries are NA if a player was not drafted or did not prticipate in some events." + "text": "The data set has statistics with variables for player draft information and how they performed in events at the NFL Scouting Combine. The variables for draft information are position and Round. The variables for the NFL Scouting Combine events are forty, vertical, bench_reps, broad_jump, and shuttle. Some entries are NA if a player was not drafted or did not prticipate in some events.", + "crumbs": [ + "Home", + "Football", + "NFL Draft Combine" + ] }, { "objectID": "football/nfl-draft-combine.html#motivation", "href": "football/nfl-draft-combine.html#motivation", "title": "NFL Draft Combine", "section": "", - "text": "The data set has statistics with variables for player draft information and how they performed in events at the NFL Scouting Combine. The variables for draft information are position and Round. The variables for the NFL Scouting Combine events are forty, vertical, bench_reps, broad_jump, and shuttle. Some entries are NA if a player was not drafted or did not prticipate in some events." + "text": "The data set has statistics with variables for player draft information and how they performed in events at the NFL Scouting Combine. The variables for draft information are position and Round. The variables for the NFL Scouting Combine events are forty, vertical, bench_reps, broad_jump, and shuttle. Some entries are NA if a player was not drafted or did not prticipate in some events.", + "crumbs": [ + "Home", + "Football", + "NFL Draft Combine" + ] }, { "objectID": "football/nfl-draft-combine.html#data", "href": "football/nfl-draft-combine.html#data", "title": "NFL Draft Combine", "section": "Data", - "text": "Data\nEach row of data gives gives information on position, draft round, and event scores for one of 6128 players at the NFL Scouting Combine between 2004 and 2023.\n\n\n\nVariable\nDescription\n\n\n\n\nposition\nPlaying position of the player\n\n\nRound\nRound player was drafted in\n\n\nforty\n40-yard dash time\n\n\nvertical\nVertical jump height (inches)\n\n\nbench_reps\n225 bench press reps\n\n\nbroad_jump\nBroad jump distances (inches)\n\n\nshuttle\n20-yard shuttle time\n\n\n\nnfl_combine.csv" + "text": "Data\nEach row of data gives gives information on position, draft round, and event scores for one of 6128 players at the NFL Scouting Combine between 2004 and 2023.\n\n\n\nVariable\nDescription\n\n\n\n\nposition\nPlaying position of the player\n\n\nRound\nRound player was drafted in\n\n\nforty\n40-yard dash time\n\n\nvertical\nVertical jump height (inches)\n\n\nbench_reps\n225 bench press reps\n\n\nbroad_jump\nBroad jump distances (inches)\n\n\nshuttle\n20-yard shuttle time\n\n\n\nnfl_combine.csv", + "crumbs": [ + "Home", + "Football", + "NFL Draft Combine" + ] }, { "objectID": "football/nfl-draft-combine.html#questions", "href": "football/nfl-draft-combine.html#questions", "title": "NFL Draft Combine", "section": "Questions", - "text": "Questions\n\nHow is player draft position related to any of the scores at NFL Combine events?\nHow does the distribution of event scores differ between various football positions?" + "text": "Questions\n\nHow is player draft position related to any of the scores at NFL Combine events?\nHow does the distribution of event scores differ between various football positions?", + "crumbs": [ + "Home", + "Football", + "NFL Draft Combine" + ] }, { "objectID": "football/nfl-draft-combine.html#references", "href": "football/nfl-draft-combine.html#references", "title": "NFL Draft Combine", "section": "References", - "text": "References\nThe data is complied from Sports Reference StatHead Football." + "text": "References\nThe data is complied from Sports Reference StatHead Football.", + "crumbs": [ + "Home", + "Football", + "NFL Draft Combine" + ] }, { - "objectID": "motor_sports/NASCAR_driver_ratings.html#data", - "href": "motor_sports/NASCAR_driver_ratings.html#data", - "title": "NASCAR Data Description", + "objectID": "by-statsds-topic.html", + "href": "by-statsds-topic.html", + "title": "Datasets By Topic", + "section": "", + "text": "2022 Divison III Women’s Soccer Results\n\n\n\n\n\n\nCorrelation\n\n\nChi-square test for association\n\n\n\nDivision III women’s soccer teams game results from 2022 season\n\n\n\n\n\nHope Donoghue, Robin Lock\n\n\n\n\n\n\n\n\n\n\n\n\n2023 Boston Marathon runners\n\n\n\n\n\n\nSummary statistics\n\n\nOutliers\n\n\nz-scores\n\n\n\nThe data set looks at the 2023 Boston Marathon results.\n\n\n\n\n\nJun 27, 2023\n\n\nJack Fay, A.J. Dykstra, and Ivan Ramler\n\n\n\n\n\n\n\n\n\n\n\n\nAustralian Open Tennis (2008-2022)\n\n\n\n\n\n\nproportions\n\n\ntwo-way tables\n\n\n\nResults from matches at the Men’s Australian Open Tennis Tournament between 2008 and 2022.\n\n\n\n\n\nAug 3, 2023\n\n\nTrent Meyer, Robin Lock\n\n\n\n\n\n\n\n\n\n\n\n\nBatting Statistics for Division I Softball\n\n\n\n\n\n\nmerging\n\n\njoining\n\n\ndata wrangling\n\n\n\nThese data sets contain batting statistics for Division I softball in the 2021 and 2022 seasons.\n\n\n\n\n\nMay 23, 2023\n\n\nSarah Weaver and Ivan Ramler\n\n\n\n\n\n\n\n\n\n\n\n\nJustin Verlander Pitches\n\n\n\n\n\n\ncategorical data\n\n\nchi-squared test\n\n\n\nInformation about pitches thrown by Justin Verlander in the 2019 and 2022 MLB seasons\n\n\n\n\n\nMar 18, 2023\n\n\nRon Yurko\n\n\n\n\n\n\n\n\n\n\n\n\nLacrosse PLL vs. NLL\n\n\n\n\n\n\nDifference in means hypothesis tests\n\n\nDifference in means confidence intervals\n\n\n\nThe data set looks at both lacrosse leagues, their respective teams, and their shot and goal amounts.\n\n\n\n\n\nJun 22, 2023\n\n\nAJ Dykstra, Jack Cowan, and Ivan Ramler\n\n\n\n\n\n\n\n\n\n\n\n\nLake Placid Women’s Ironman Triathlon\n\n\n\n\n\n\nHistograms\n\n\nScatter Plots\n\n\nCorrelation\n\n\nLinear Regression\n\n\n\nThe data set looks at female participatants in the triathlon Ironman.\n\n\n\n\n\nJun 17, 2023\n\n\nMichael Schuckers, Matthew Abel, and A.J. Dykstra\n\n\n\n\n\n\n\n\n\n\n\n\nLeague of Legends - Buffing and Nerfing\n\n\n\n\n\n\noutlier detection\n\n\nfive number summary\n\n\nz-scores\n\n\n\nGame play statistics for League of Legends champions in two different patches. Can detecting outliers indicate champions that should be enhanced (buffed) or scaled back (nerfed)?\n\n\n\n\n\nJul 1, 2023\n\n\nGeorge Charalambous, Ivan Ramler, and A.J. Dykstra\n\n\n\n\n\n\n\n\n\n\n\n\nNASCAR Data Description\n\n\n\n\n\n\ndata visualization\n\n\ndata wrangling\n\n\nlinear regression\n\n\ntransformations in regression\n\n\nmodel assumptions\n\n\nmodel building\n\n\n\nNASCAR Driver Ratings and statistics from 2007-2022\n\n\n\n\n\nJul 6, 2023\n\n\nAlyssa Bigness, Jack Fay, and Ivan Ramler\n\n\n\n\n\n\n\n\n\n\n\n\nNFL Draft Combine\n\n\n\n\n\n\nExploratory Data Analysis\n\n\nLinear regression\n\n\n\nNFL Scouting Combine Event scores from 2004 - 2023\n\n\n\n\n\nAug 3, 2023\n\n\nShane Hauk, Michael Schuckers, Robin Lock\n\n\n\n\n\n\n\n\n\n\n\n\nNHL Team Power Play Perfomance in 2022-2023\n\n\n\n\n\n\nexploratory data Analysis\n\n\n\nMeasures for National Hockey League teams from all 5 on 4 power plays in the 2022-2023 season.\n\n\n\n\n\nJul 27, 2023\n\n\nAbby Hustler, Robin Lock\n\n\n\n\n\n\n\n\n\n\n\n\nNHL Team Statistics\n\n\n\n\n\n\nlinear regression\n\n\ncorrelation\n\n\n\nTeam-level statistics from the 2022-2023 NHL season\n\n\n\n\n\nDec 15, 2023\n\n\nTaylor Lum, Robin Lock\n\n\n\n\n\n\n\n\n\n\n\n\nNational Basketball Association Player Statistics\n\n\n\n\n\n\nclustering\n\n\nprincipal component analysis\n\n\n\nNBA player statistics from the 2021-2022 regular season\n\n\n\n\n\nMar 25, 2023\n\n\nRon Yurko\n\n\n\n\n\n\n\n\n\n\n\n\nNational Football League Team Statistics\n\n\n\n\n\n\nlinear regression\n\n\nprincipal component analysis\n\n\nmissing data\n\n\nvariable selection\n\n\nregularization\n\n\n\nNFL team statistics from 1999 to 2022\n\n\n\n\n\nMay 24, 2023\n\n\nRon Yurko\n\n\n\n\n\n\n\n\n\n\n\n\nNational Hockey League Shots\n\n\n\n\n\n\nclassification\n\n\nlogistic regression\n\n\ngeneralized additive models\n\n\nmultinomial logistic regression\n\n\nnaive bayes classifier\n\n\ndensity estimation\n\n\n\nInformation about shots during the 2021-2022 NHL season\n\n\n\n\n\nMar 23, 2023\n\n\nRon Yurko\n\n\n\n\n\n\n\n\n\n\n\n\nNational Women’s Soccer League Team Statistics\n\n\n\n\n\n\nlinear regression\n\n\nprincipal component analysis\n\n\n\nNWSL team statistics from 2016 to 2022\n\n\n\n\n\nMar 18, 2023\n\n\nRon Yurko\n\n\n\n\n\n\n\n\n\n\n\n\nPremier Hockey Federation Shots\n\n\n\n\n\n\ncategorical data\n\n\nchi-squared test\n\n\nlogistic regression\n\n\n\nInformation about shots during the 2021-2022 PHF season\n\n\n\n\n\nMar 23, 2023\n\n\nRon Yurko\n\n\n\n\n\n\n\n\n\n\n\n\nSoccer Heights\n\n\n\n\n\n\nANOVA for means\n\n\nPairwise comparisons\n\n\n\nHeights for a sample of 100 soccer players by position\n\n\n\n\n\nAug 3, 2023\n\n\nRoderick Mpofu, Robin Lock\n\n\n\n\n\n\n\n\n\n\n\n\nStatistics of PGA Tournaments During the 2022 Season\n\n\n\n\n\n\ncorrelation\n\n\nlinear regression\n\n\n\nMeasures of Putting, Driving, and Success for individual golfers in 19 professional tournaments in 2022\n\n\n\n\n\nMay 19, 2023\n\n\nAlysssa Bigness\n\n\n\n\n\n\n\n\n\n\n\n\nSumo wrestler characteristics\n\n\n\n\n\n\ndata wrangling\n\n\ndata visualization\n\n\n\nSumo wrestling data 1957-2023.\n\n\n\n\n\nJun 29, 2023\n\n\nJack Fay, A.J. Dykstra, and Ivan Ramler\n\n\n\n\n\n\n\n\n\n\n\n\nTeam Statistics for Division I Women’s Volleyball\n\n\n\n\n\n\nmulticollinearity\n\n\nmultiple linear regression\n\n\ncorrelations\n\n\nsummary statistics\n\n\n\nThese data sets contain team statistics for Division I women’s volleyball in the 2022 and 2023 seasons.\n\n\n\n\n\nJul 24, 2023\n\n\nJack Fay, A.J. Dykstra, and Ivan Ramler\n\n\n\n\n\n\n\n\n\n\n\n\nTeam statistics for NCAA Division 1 Men’s and Women’s Lacrosse\n\n\n\n\n\n\ncorrelation\n\n\nindicator variables\n\n\nlinear regression\n\n\nside-by-side boxplots\n\n\n\nThese data sets contain team statistics for NCAA Division I women’s and men’s lacrosse in the 2022 and 2023 seasons.\n\n\n\n\n\nJul 24, 2023\n\n\nJack Fay, A.J. Dykstra, and Ivan Ramler\n\n\n\n\n\n\n\n\n\n\n\n\nWTA Grand Slam Matches\n\n\n\n\n\n\nlogistic regression\n\n\ndata wrangling\n\n\ncategorical data\n\n\nchi-squared test\n\n\nclustering\n\n\n\nWTA Grand Slam Matches from 2018 to 2022\n\n\n\n\n\nMay 24, 2023\n\n\nRon Yurko\n\n\n\n\n\n\n\n\n\n\n\n\nWomen’s National Baskeball Assosiation game statistics\n\n\n\n\n\n\nData cleaning\n\n\nMissing Data\n\n\nTables\n\n\nLogistic Regression\n\n\n\nThis data set contains team game statistics for 16 WNBA teams and for 20 seasons.\n\n\n\n\n\nDec 15, 2023\n\n\nKristen Varin\n\n\n\n\n\n\n\n\n\n\n\n\nWomen’s National Basketball Association Shots\n\n\n\n\n\n\nclassification\n\n\nlogistic regression\n\n\ngeneralized additive models\n\n\nmultinomial logistic regression\n\n\nnaive bayes classifier\n\n\ndensity estimation\n\n\n\nInformation about shots during the 2021-2022 WNBA season\n\n\n\n\n\nMar 25, 2023\n\n\nRon Yurko\n\n\n\n\n\n\nNo matching items", + "crumbs": [ + "Home", + "Datasets By Topic" + ] + }, + { + "objectID": "football/nfl-team-statistics.html", + "href": "football/nfl-team-statistics.html", + "title": "National Football League Team Statistics", + "section": "", + "text": "The National Football League (NFL) is the top professional American football league in the world. While a team’s record ultimately determines whether or not they make the playoffs, their score differential (points for - points against) is often a better indicator of a team’s ability. But what aspects of a team’s performance are related to their point differential? Is passing more important than rushing? What about offense in comparison to defense? The NFL records a variety of statistics, and the public NFL analytics community have developed advanced metrics such as expected points added (EPA) that provide deeper insight into a team’s performance. With this dataset of statistics dating back to 1999, you can explore variation between teams since as well as which types of statistics are relevant predictor variables of record and point differential.", + "crumbs": [ + "Home", + "Football", + "National Football League Team Statistics" + ] + }, + { + "objectID": "football/nfl-team-statistics.html#motivation", + "href": "football/nfl-team-statistics.html#motivation", + "title": "National Football League Team Statistics", + "section": "", + "text": "The National Football League (NFL) is the top professional American football league in the world. While a team’s record ultimately determines whether or not they make the playoffs, their score differential (points for - points against) is often a better indicator of a team’s ability. But what aspects of a team’s performance are related to their point differential? Is passing more important than rushing? What about offense in comparison to defense? The NFL records a variety of statistics, and the public NFL analytics community have developed advanced metrics such as expected points added (EPA) that provide deeper insight into a team’s performance. With this dataset of statistics dating back to 1999, you can explore variation between teams since as well as which types of statistics are relevant predictor variables of record and point differential.", + "crumbs": [ + "Home", + "Football", + "National Football League Team Statistics" + ] + }, + { + "objectID": "football/nfl-team-statistics.html#data", + "href": "football/nfl-team-statistics.html#data", + "title": "National Football League Team Statistics", "section": "Data", - "text": "Data\nThe data comes from the NASCAR website and shows the season statistics from 2007-2022. Each row displays the metrics of a racer for that specific year. The data frame contains 1111 rows of observations and 20 variables.\n\nnascar_driver_statistics.csv\n\n\nVariable\nDescription\n\n\n\n\nWins\nThe sum of the driver’s victories\n\n\nAvgStart\nThe sum of the driver’s starting positions divided by the number of races\n\n\nAvgMidRace\nThe sum of the driver’s mid race positions divided by the number of races\n\n\nAvgFinish\nThe sum of the driver’s finishing positions divided by the number of races\n\n\nAvgPos\nThe sum of the driver’s position each lap divided by the number of laps\n\n\nPassDiff\nThe sum of green flag passes minus green times passed\n\n\nGreenFlagPasses\nNumber of green flag passes performed by the driver\n\n\nGreenFlagPassed\nNumber of times driver is passed during green flag\n\n\nQualityPasses\nNumber of passes in the top 15 while under green flag conditions by driver\n\n\nPercentQualityPasses\nThe sum of quality passes divided by green flag passes\n\n\nNumFastestLaps\nNumber of where the driver had the fastest speed on the lap\n\n\nLapsInTop15\nNumber of laps completed while running in a top 15 position\n\n\nPercentLapsInTop15\nThe sum of the laps run in the top 15 divided by total laps completed\n\n\nLapsLed\nThe sum of the laps led in a race\n\n\nPercentLapsLed\nThe sum of the laps led in the race\n\n\nTotalLaps\nThe sum of the laps completed by a driver that year\n\n\nDriverRating\nFormula combining wins, finish, top15-finish, average running position while on lead lap, average speed under green, fastest lap, led most laps, and lead lap finish with a maximum rating of 150 points" + "text": "Data\nThis dataset contains statistics about the regular season performance for each NFL team from 1999 to 2022 team. The data was collected using the nflreadr package in R.\nEach row in the dataset corresponds to a single NFL team in a single regular season. There are a total of 765 team-seasons, with 56 total columns. The column names are organized below by the type of information they contain, with the first set of columns being self-explanatory:\n\nnfl-team-statistics.csv\n\n\nVariable\nDescription\n\n\n\n\nseason\nRegular season year of team’s statistics\n\n\nteam\nNFL team three letter abbreviation\n\n\n\nThere are also columns with season level outcomes:\n\n\n\nVariable\nDescription\n\n\n\n\npoints_score\nTotal number of points scored by the team\n\n\npoints_allowed\nTotal number of points allowed by the team\n\n\nwins\nNumber of games the team won\n\n\nlosses\nNumber of games the team lost\n\n\nties\nNumber of games the team tied\n\n\nscore_differential\npoints scored - points allowed\n\n\n\nThere are also several columns corresponding to offensive and defensive summaries of the team’s performance in the season separated by play type (either pass or run):\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\noffense/defense_completion_percentage\nPassing completion percentage either for (offense) or against (defense)\n\n\noffense/defense_total_yards_gained_pass/run\nTotal number of yards gained (offense) or allowed (defense) by play type (pass or run)\n\n\noffense/defense_ave_yards_gained_pass/run\nAverage number of yards gained (offense) or allowed (defense) per play by play type (pass or run)\n\n\noffense/defense_total_air_yards\nTotal number of air yards gained (offense) or allowed (defense), where air yards correspond to perpendicular yards traveled from the line of scrimmage to location of catch for passing plays\n\n\noffense/defense_ave_air_yards\nAverage number of air yards gained (offense) or allowed (defense) per passing play\n\n\noffense/defense_total_yac\nTotal number of yards after catch gained (offense) or allowed (defense)\n\n\noffense/defense_ave_yac\nAverage number of yards after catch gained (offense) or allowed (defense) per passing play\n\n\noffense/defense_n_plays_pass/run\nTotal number of plays by the team (offense) or against (defense) by play type (pass or run)\n\n\noffense/defense_n_interceptions\nTotal number of interceptions thrown (offense) or caught (defense)\n\n\noffense/defense/n_fumbles_lost_pass/run\nTotal number of fumbles lost (offense) or forced (defense) by play type (pass or run)\n\n\noffense/defense_total_epa_pass/run\nTotal expected points added (offense) or allowed (defense) by play type (pass or run)\n\n\noffense/defense_ave_epa_pass/run\nAverage expected points added (offense) or allowed (defense) per play by play type (pass or run)\n\n\noffense/defense_total_wpa_pass/run\nTotal win probability added (offense) or allowed (defense) by play type (pass or run)\n\n\noffense/defense_ave_wpa_pass/run\nAverage win probability added (offense) or allowed (defense) per play by play type (pass or run)\n\n\noffense/defense_total_epa_pass/run\nTotal expected points added (offense) or allowed (defense) by play type (pass or run)\n\n\noffense/defense_success_rate_pass/run\nProportion of plays with positive expected points added (offense) or allowed (defense) by play type (pass or run)\n\n\n\nThe EPA variables are advanced NFL statistics, conveying how much value a team is adding over the average team in a given situation. It’s on a points scale instead of the typically used yards, because not all yards are created equal in American football (10 yard gain on 3rd and 15 is much less valuable than a 2 yard gain on 4th and 1). For offensive stats the higher the EPA the better, but for defensive stats the lower (more negative) the EPA the better. The WPA variables are similar except they are measuring play value in terms of win probability.", + "crumbs": [ + "Home", + "Football", + "National Football League Team Statistics" + ] }, { - "objectID": "motor_sports/NASCAR_driver_ratings.html#questions", - "href": "motor_sports/NASCAR_driver_ratings.html#questions", - "title": "NASCAR Data Description", + "objectID": "football/nfl-team-statistics.html#questions", + "href": "football/nfl-team-statistics.html#questions", + "title": "National Football League Team Statistics", "section": "Questions", - "text": "Questions\n\nWhat is the best way to model the relationship between average finish and driver rating? Are the linear model assumptions met?\nWhich predictors best predict driver rating?" + "text": "Questions\n\nBuild a linear regression model to predict score differential as a function of the team’s statistics. Which variables, if any, are predictive of score differential in a season? Are certain types of variables more predictive? (e.g., passing versus rushing,) Describe their relationships.\nUsing principal component analysis, project the team seasons into a lower dimensional space using only the team statistics that do not include points or record. Choose an appropriate number of principal components, and describe which statistics contribute to your selected number of components. Based on your results, which team statistics explain the most variation between team-seasons in the dataset? Which team statistics are independent of each other?", + "crumbs": [ + "Home", + "Football", + "National Football League Team Statistics" + ] }, { - "objectID": "motor_sports/NASCAR_driver_ratings.html#references", - "href": "motor_sports/NASCAR_driver_ratings.html#references", - "title": "NASCAR Data Description", + "objectID": "football/nfl-team-statistics.html#references", + "href": "football/nfl-team-statistics.html#references", + "title": "National Football League Team Statistics", "section": "References", - "text": "References\nhttps://www.nascar.com/stats/" + "text": "References\nHo T, Carl S (2022). nflreadr: Download ‘nflverse’ Data. R package version 1.3.1, https://CRAN.R-project.org/package=nflreadr.", + "crumbs": [ + "Home", + "Football", + "National Football League Team Statistics" + ] }, { - "objectID": "golf/pga_drive_putt.html", - "href": "golf/pga_drive_putt.html", - "title": "Statistics of PGA Tournaments During the 2022 Season", + "objectID": "index.html", + "href": "index.html", + "title": "SCORE Sports Data Repository", "section": "", - "text": "A common expression among golfers is “Drive for show, putt for dough.” This implies that the long initial tee shots (drives) on each hole are impressive, but the real key to success is the final strokes rolling the ball along the green into the hole (putts). Do data support this adage? The dataset for this activity was obtained from the PGA statistics website. Cases include all golfers who made the cut in each of 19 PGA tournaments in 2022. The dataset includes variables for driving ability, putting ability, and measuring success in the tournament. The “driving” variables include average driving distance (avgDriveDist), driving accuracy percentage (drivePct), and strokes gained off the tee (driveSG). The “putting” variables are average putts per round (avgPuttsPerRound), one putt percentage (onePuttPct), and strokes gained putting (puttsSG). The variables to measure success are scoring average (avgScore), official money won (Money), and Fedex Cup points (Points)." + "text": "The SCORE Network Sports Data Repository curates interesting datasets across a variety of sports for use in statistics and data science education. Each dataset has the following properties:\n\nA sports question of interest, with context motivating why the dataset is relevant and interesting to explore.\nA statistics / data science topic which the dataset can be used to help teach.\nExample questions that instructors can use to help build lessons, handouts, and SCORE modules.\n\nDatasets are organized by sport along the left, but you can also browse by statistics and data science topic.\nThis repository is heavily inspired by the CMU S&DS Data Repository.\nThe development of the SCORE with Data network is funded by the National Science Foundation (award 2142705)." }, { - "objectID": "golf/pga_drive_putt.html#motivation", - "href": "golf/pga_drive_putt.html#motivation", - "title": "Statistics of PGA Tournaments During the 2022 Season", + "objectID": "softball/softball_d1_2021-2022.html", + "href": "softball/softball_d1_2021-2022.html", + "title": "Batting Statistics for Division I Softball", "section": "", - "text": "A common expression among golfers is “Drive for show, putt for dough.” This implies that the long initial tee shots (drives) on each hole are impressive, but the real key to success is the final strokes rolling the ball along the green into the hole (putts). Do data support this adage? The dataset for this activity was obtained from the PGA statistics website. Cases include all golfers who made the cut in each of 19 PGA tournaments in 2022. The dataset includes variables for driving ability, putting ability, and measuring success in the tournament. The “driving” variables include average driving distance (avgDriveDist), driving accuracy percentage (drivePct), and strokes gained off the tee (driveSG). The “putting” variables are average putts per round (avgPuttsPerRound), one putt percentage (onePuttPct), and strokes gained putting (puttsSG). The variables to measure success are scoring average (avgScore), official money won (Money), and Fedex Cup points (Points)." + "text": "Data for a particular sport is often stored across numerous locations. For example, in NCAA Division I Softball, batting statistics for each season are typically stored in separate tables. (See for example the statistics hosted by https://d1softball.com/)\nSuppose we are interested in tracking the statistics of players across multiple seasons. A common way to prep the data to do this is to use join statements to merge each season’s data into one table with one row per player (and columns associated with their different statistics for each season).", + "crumbs": [ + "Home", + "Softball", + "Batting Statistics for Division I Softball" + ] }, { - "objectID": "golf/pga_drive_putt.html#data", - "href": "golf/pga_drive_putt.html#data", - "title": "Statistics of PGA Tournaments During the 2022 Season", + "objectID": "softball/softball_d1_2021-2022.html#motivation", + "href": "softball/softball_d1_2021-2022.html#motivation", + "title": "Batting Statistics for Division I Softball", + "section": "", + "text": "Data for a particular sport is often stored across numerous locations. For example, in NCAA Division I Softball, batting statistics for each season are typically stored in separate tables. (See for example the statistics hosted by https://d1softball.com/)\nSuppose we are interested in tracking the statistics of players across multiple seasons. A common way to prep the data to do this is to use join statements to merge each season’s data into one table with one row per player (and columns associated with their different statistics for each season).", + "crumbs": [ + "Home", + "Softball", + "Batting Statistics for Division I Softball" + ] + }, + { + "objectID": "softball/softball_d1_2021-2022.html#data", + "href": "softball/softball_d1_2021-2022.html#data", + "title": "Batting Statistics for Division I Softball", "section": "Data", - "text": "Data\nEach row of data gives the measures for one golfer in one tournament. The dataset covers 19 PGA tournaments from the 2022 season with 1387 cases in all. Each tournament consists of four rounds of golf. Some golfers are eliminated after the first two (or sometimes three) rounds. Only players who competed in all four rounds (i.e. those that made the “cut”) are included in this dataset.\n\nPGA2022.csv\n\n\nVariable\nDescription\n\n\n\n\nplayerName\nName of the player\n\n\ncountry\nThe country where the player is from\n\n\navgDriveDist\nAverage driving distance (in yards)\n\n\ndrivePct\nPercentage of times a tee shot comes to rest in the fairway\n\n\ndriveSG\nStrokes gained off the tee measures player performance off the tee on all par 4s and par 5s of how much better or worse a player’s drive is than the PGA Tour average\n\n\navgPuttsPerRound\nAverage number of total putts per round\n\n\nonePuttPct\nPercentage of times it took one putt to get the ball into the hole\n\n\nputtsSG\nStrokes gained putting measures how many strokes a player gains or loses on the greens compared to the PGA Tour average\n\n\navgScore\nThe scoring average is the total strokes divided by the total rounds\n\n\nMoney\nThe official money is the prize money award to the Professional members\n\n\nPoints\nFedexCup Regular Season Points are awarded points by finished position for performance in each tournament\n\n\nTournament\nThe tournament where the PGA Tour is taking place" + "text": "Data\nThere are four data tables associated with this page.\nTwo of them, batting2021 and batting2022, contain statistics for all Division 1 Softball player who played in the 2021 and/or 2022 season.\nThe other two, batting2021_subset and batting2022_subset, represent non-random samples taken the full data and can be used to manually illustrate how to merge the tables.\nThe 2021 data set has 2131 rows with 22 columns. The 2022 data set has 2275 rows with the same 22 columns. Each row represents a Division 1 Softball player who played in the 2021 and/or 2022 season.\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nPlayer\nName of the softball player\n\n\nTeam\nName of the team (school) for each player\n\n\nBA\nBatting Average - The ratio of a player’s total base hits to their total number of at-bats, indicating their ability to make successful hits.\n\n\nOBP\nOn-Base Percentage - The percentage of times a player reaches base, either through a hit, walk, or hit-by-pitch, out of their total plate appearances.\n\n\nSLG\nSlugging Percentage - The measure of a player’s power by calculating the total number of bases they accumulate per at-bat.\n\n\nOPS\nOn-Base Plus Slugging - The sum of a player’s on-base percentage and slugging percentage, providing a comprehensive measure of their offensive contribution.\n\n\nGP\nGames Played - The total number of games in which a player has participated.\n\n\nPA\nPlate Appearances - The total number of times a player has come up to bat, including at-bats, walks, and hit-by-pitches.\n\n\nAB\nAt-Bats - The number of times a player has officially faced a pitcher and had an opportunity to hit.\n\n\nR\nRuns Scored - The number of times a player has crossed home plate and scored a run.\n\n\nH\nHits - The total number of successful hits made by a player.\n\n\n2B\nDoubles - The number of hits resulting in the batter reaching second base.\n\n\n3B\nTriples - The number of hits resulting in the batter reaching third base.\n\n\nHR\nHome Runs - The number of hits resulting in the batter scoring a run by hitting the ball out of the park.\n\n\nRBI\nRuns Batted In - The number of runs a player has driven in with a hit or sacrifice.\n\n\nHBP\nHit by Pitch - The number of times a player has been struck by a pitched ball and awarded first base.\n\n\nBB\nWalks - The number of times a player has received a base on balls (four balls) and been awarded first base.\n\n\nK\nStrikeouts - The number of times a player has been called out after accumulating three strikes.\n\n\nSB\nStolen Bases - The number of times a player successfully advances to the next base without a hit, during a pitch while the ball is in play.\n\n\nCS\nCaught Stealing - The number of times a player is thrown out while attempting to steal a base.\n\n\n\n\nThe data tables are available for individual download as csv files or bundled together into either a SQLite or DuckDB database.\n\nbatting2021.csv\n\n\nbatting2022.csv\n\n\nbatting2021_subset.csv\n\n\nbatting2022_subset.csv\n\n\nsoftball_batting.sqlite\n\n\nsoftball_batting.duckdb", + "crumbs": [ + "Home", + "Softball", + "Batting Statistics for Division I Softball" + ] }, { - "objectID": "golf/pga_drive_putt.html#questions", - "href": "golf/pga_drive_putt.html#questions", - "title": "Statistics of PGA Tournaments During the 2022 Season", + "objectID": "softball/softball_d1_2021-2022.html#questions", + "href": "softball/softball_d1_2021-2022.html#questions", + "title": "Batting Statistics for Division I Softball", "section": "Questions", - "text": "Questions\n\nAre driving or putting statistics generally more effective for predicting success in a PGA tournament?\nWhich measure of success (avgScore, Money, or Points) appears to be easiest to predict with either driving or putting information?\nFor each measure of success, generate a scatterplot with one the putting or driving variables. Which of these plots suggest we might want to use a transformation of one of the variables to improve linearity?\nCan you find a transformation that improves the linearity of the relationship for one of the problematic cases in question #3?" + "text": "Questions\n\nWith multiple seasons of data, you can investigate the different ways to join the the 2021 and 2022 statistics. (e.g., comparing the results of an inner join vs a full join) This can be done manually using subsets of data (to illustrate the joins with simple examples) or scaled up using the full data\nWith a single season/table, you can practice different data wrangling techniques. e.g., finding the league leaders in different categories", + "crumbs": [ + "Home", + "Softball", + "Batting Statistics for Division I Softball" + ] }, { - "objectID": "golf/pga_drive_putt.html#references", - "href": "golf/pga_drive_putt.html#references", - "title": "Statistics of PGA Tournaments During the 2022 Season", + "objectID": "softball/softball_d1_2021-2022.html#references", + "href": "softball/softball_d1_2021-2022.html#references", + "title": "Batting Statistics for Division I Softball", "section": "References", - "text": "References\nThe data were obtained from the PGA statistics website." + "text": "References\nStatistics. D1Softball. (2019, October 24). https://d1softball.com/statistics/", + "crumbs": [ + "Home", + "Softball", + "Batting Statistics for Division I Softball" + ] }, { - "objectID": "data-sources.html", - "href": "data-sources.html", - "title": "Data Sources", - "section": "", - "text": "Here are some resources for finding sports datasets:\n\nCRAN Task View for Sports Analytics - catalog of R packages published on the Comprehensive R Archive Network (CRAN) organized by sport (curated by Benjamin S. Baumer, Quang Nguyen, and Gregory J. Matthews)\nSportsDataverse initiative - collection of software tools for accessing publicly available sports data (organized by Saiem Gilani)\nKaggle\nTidyTuesday - GitHub repository of datasets updated weekly with example code for accessing data (featuring a variety of sports examples)\nFiveThirtyEight - GitHub repository of datasets used in FiveThirtyEight articles" - }, - { - "objectID": "lacrosse/lacrosse_pll_vs_nll.html", - "href": "lacrosse/lacrosse_pll_vs_nll.html", - "title": "Lacrosse PLL vs. NLL", + "objectID": "triathlon/triathlon_lakeplacid_women_ironman.html", + "href": "triathlon/triathlon_lakeplacid_women_ironman.html", + "title": "Lake Placid Women’s Ironman Triathlon", "section": "", - "text": "This data set examines the goals and shots in two prominent lacrosse leagues: the Premier Lacrosse League (PLL) and the National Lacrosse League (NLL).\nThe PLL and NLL are highly regarded professional lacrosse leagues that feature top-tier athletes from around the world. The PLL is played in an indoor, smaller field while the NLL plays on an outdoor field with traditional dimensions. This data set allows for an analysis of goal-scoring within these leagues." + "text": "The motivation for this data analysis is to explore the relationships between swim times, bike times, and run times (in minutes) in order to gain insights into the performance patterns of the athletes. By analyzing these relationships, we can understand the interplay between different segments of the race and potentially identify areas of improvement for athletes.", + "crumbs": [ + "Home", + "Triathlon", + "Lake Placid Women's Ironman Triathlon" + ] }, { - "objectID": "lacrosse/lacrosse_pll_vs_nll.html#motivation", - "href": "lacrosse/lacrosse_pll_vs_nll.html#motivation", - "title": "Lacrosse PLL vs. NLL", + "objectID": "triathlon/triathlon_lakeplacid_women_ironman.html#motivation", + "href": "triathlon/triathlon_lakeplacid_women_ironman.html#motivation", + "title": "Lake Placid Women’s Ironman Triathlon", "section": "", - "text": "This data set examines the goals and shots in two prominent lacrosse leagues: the Premier Lacrosse League (PLL) and the National Lacrosse League (NLL).\nThe PLL and NLL are highly regarded professional lacrosse leagues that feature top-tier athletes from around the world. The PLL is played in an indoor, smaller field while the NLL plays on an outdoor field with traditional dimensions. This data set allows for an analysis of goal-scoring within these leagues." + "text": "The motivation for this data analysis is to explore the relationships between swim times, bike times, and run times (in minutes) in order to gain insights into the performance patterns of the athletes. By analyzing these relationships, we can understand the interplay between different segments of the race and potentially identify areas of improvement for athletes.", + "crumbs": [ + "Home", + "Triathlon", + "Lake Placid Women's Ironman Triathlon" + ] }, { - "objectID": "lacrosse/lacrosse_pll_vs_nll.html#data", - "href": "lacrosse/lacrosse_pll_vs_nll.html#data", - "title": "Lacrosse PLL vs. NLL", + "objectID": "triathlon/triathlon_lakeplacid_women_ironman.html#data", + "href": "triathlon/triathlon_lakeplacid_women_ironman.html#data", + "title": "Lake Placid Women’s Ironman Triathlon", "section": "Data", - "text": "Data\nThe data set has 162 rows with 9 columns. Each row represents a single lacrosse match either in the Premier Lacrosse League or the National Lacrosse League for the 2021 - 2022 season. NLL seasons typically run from December to May while PLL seasons typically run from June to September.\n\nlacrosse_pll_nll_2021-2022.csv\n\n\nVariable\nDescription\n\n\n\n\nLeague\nThe Premier Lacrosse league and the National Lacrosse League\n\n\nAway_team\nThe traveling team\n\n\nHome_team\nThe hosting team\n\n\nAway_shots\nHow many shots the Away_team had on net\n\n\nHome_shots\nHow many shots the Home_team had on net\n\n\nAway_goals\nHow many goals the Away_team had on net\n\n\nHome_goals\nHow many goals the Home_team had on net\n\n\ngoals_per_game\nThe total amount of goals scored each game\n\n\ngoals_per_48\nThe average amount of goals for the first 48 minutes of a game" + "text": "Data\nThe main data set has 489 rows with 17 columns. Each row represents a female who has participated in the Ironman in 2022. Additionally, a subset of 2022 female Canadian participants is available and has 64 rows.\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nBib\nregistration number of each runner used for identification\n\n\nName\nThe participant’s name\n\n\nCountry\nWhat country the participant is from\n\n\nGender\nThe participant’s gender\n\n\nDivision\nThe age range or membership a runner is\n\n\nDivision.Rank\nWithin the divisions, the place each runner has obtained over all races\n\n\nOverall.Time\nThe total time it took to complete the Ironman in minutes\n\n\nOverall.Rank\nThe runner’s finishing place for that particular triathlon\n\n\nSwim.Time\nThe time in minutes it took to complete the swimming portion\n\n\nSwim.Rank\nThe place the runner finished for the swim portion\n\n\nBike.Time\nThe time in minutes it took to complete the biking portion\n\n\nBike.Rank\nThe place the runner finished for the bike portion\n\n\nRun.Time\nThe time in minutes it took to complete the running portion\n\n\nRun.Rank\nThe place the runner finished for the running portion\n\n\nFinish.Status\nStates whether someone completed the Ironman successfully\n\n\nLocation\nWhere the Ironman takes place\n\n\nYear\nThey year when the mentioned participant ran\n\n\n\n\n\n\n\nironman_lake_placid_female_2022.csv\n\n\nironman_lake_placid_female_2022_canadian.csv", + "crumbs": [ + "Home", + "Triathlon", + "Lake Placid Women's Ironman Triathlon" + ] }, { - "objectID": "lacrosse/lacrosse_pll_vs_nll.html#questions", - "href": "lacrosse/lacrosse_pll_vs_nll.html#questions", - "title": "Lacrosse PLL vs. NLL", + "objectID": "triathlon/triathlon_lakeplacid_women_ironman.html#questions", + "href": "triathlon/triathlon_lakeplacid_women_ironman.html#questions", + "title": "Lake Placid Women’s Ironman Triathlon", "section": "Questions", - "text": "Questions\n\nDo these data provide evidence that, on average, goals scored in the PLL and NLL differ? Include all details of the appropriate hypothesis test.\nConstruct and interpret a 95% confidence interval in average goals for NLL and PLL lacrosse games per 48 minutes." + "text": "Questions\n\nWith the different times and rank variables, you can explore the relationships between them through things like correlation coefficients and linear regression.\nOther things like finding the best variables at predicting a runner’s overall rank in a race and then even a division rank would be easy to model using this data set.", + "crumbs": [ + "Home", + "Triathlon", + "Lake Placid Women's Ironman Triathlon" + ] }, { - "objectID": "lacrosse/lacrosse_pll_vs_nll.html#references", - "href": "lacrosse/lacrosse_pll_vs_nll.html#references", - "title": "Lacrosse PLL vs. NLL", + "objectID": "triathlon/triathlon_lakeplacid_women_ironman.html#score-modules", + "href": "triathlon/triathlon_lakeplacid_women_ironman.html#score-modules", + "title": "Lake Placid Women’s Ironman Triathlon", + "section": "SCORE Modules", + "text": "SCORE Modules\nThe following SCORE modules are associated with these data.\n\nIronman Triathlons - Correlations", + "crumbs": [ + "Home", + "Triathlon", + "Lake Placid Women's Ironman Triathlon" + ] + }, + { + "objectID": "triathlon/triathlon_lakeplacid_women_ironman.html#references", + "href": "triathlon/triathlon_lakeplacid_women_ironman.html#references", + "title": "Lake Placid Women’s Ironman Triathlon", "section": "References", - "text": "References\nPremier Lacrosse League stats. Premier Lacrosse League Stats. (n.d.). https://stats.premierlacrosseleague.com/\nPlayer stats. NLL. (2023, January 26). https://www.nll.com/stats/all-player-stats/" + "text": "References\nIronman Lake Placid Results and articles. CoachCox. (n.d.). https://www.coachcox.co.uk/imstats/series/13/", + "crumbs": [ + "Home", + "Triathlon", + "Lake Placid Women's Ironman Triathlon" + ] }, { - "objectID": "running/boston_marathon_2023.html", - "href": "running/boston_marathon_2023.html", - "title": "2023 Boston Marathon runners", + "objectID": "basketball/nba-player-stats.html", + "href": "basketball/nba-player-stats.html", + "title": "National Basketball Association Player Statistics", "section": "", - "text": "The Boston Marathon is an annual road race that takes place in the City of Boston Massachusetts. Attracting thousands of runners of varying skill and experience, year after year participants from around the world gather to compete for world records, divisional records, and personal bests. Examining the results of these athletes yields an interesting look into the spread and distribution of the times of all runners who successfully completed the historic race." + "text": "The National Basketball Association (NBA) is the top men’s professional basketball league in the world. While players have predefined positions, the sport is becoming increasingly positionless - with centers attempting more three point shots and guards driving the ball inside to dunk. With this dataset, you can explore clustering NBA players based on various types of statistics and compare your players labels to the predefined positions.", + "crumbs": [ + "Home", + "Basketball", + "National Basketball Association Player Statistics" + ] }, { - "objectID": "running/boston_marathon_2023.html#motivation", - "href": "running/boston_marathon_2023.html#motivation", - "title": "2023 Boston Marathon runners", + "objectID": "basketball/nba-player-stats.html#motivation", + "href": "basketball/nba-player-stats.html#motivation", + "title": "National Basketball Association Player Statistics", "section": "", - "text": "The Boston Marathon is an annual road race that takes place in the City of Boston Massachusetts. Attracting thousands of runners of varying skill and experience, year after year participants from around the world gather to compete for world records, divisional records, and personal bests. Examining the results of these athletes yields an interesting look into the spread and distribution of the times of all runners who successfully completed the historic race." + "text": "The National Basketball Association (NBA) is the top men’s professional basketball league in the world. While players have predefined positions, the sport is becoming increasingly positionless - with centers attempting more three point shots and guards driving the ball inside to dunk. With this dataset, you can explore clustering NBA players based on various types of statistics and compare your players labels to the predefined positions.", + "crumbs": [ + "Home", + "Basketball", + "National Basketball Association Player Statistics" + ] }, { - "objectID": "running/boston_marathon_2023.html#data", - "href": "running/boston_marathon_2023.html#data", - "title": "2023 Boston Marathon runners", + "objectID": "basketball/nba-player-stats.html#data", + "href": "basketball/nba-player-stats.html#data", + "title": "National Basketball Association Player Statistics", "section": "Data", - "text": "Data\nThe data set contains 26598 rows and 15 columns. Each row represents a runner who completed the Boston Marathon in 2023\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nage_group\nage group of the runner\n\n\nplace_overall\nfinishing place of the runner out of all runners\n\n\nplace_gender\nfinishing place of runner among the same gender\n\n\nplace_division\nfinishing place of runner among runners of the same gender and age group\n\n\nname\nname of runner\n\n\ngender\ngender of runner\n\n\nteam\nteam the runner is affiliated with\n\n\nbib_number\nbib number of runner\n\n\nhalf_time\nhalf marathon time of runner\n\n\nfinish_net\nfinishing time timed from when they cross the starting gate\n\n\nfinish_gun\nfinishing time of runner timed from when the starter gun is fired\n\n\nage_group\nage group of the runner\n\n\nhalf_time_sec\nhalf marathon time in seconds\n\n\nfinish_net_sec\nnet finish in seconds\n\n\nfinish_gun_sec\ngun finish in seconds\n\n\n\n\nData file\n\nboston_marathon_2023.csv" + "text": "Data\nThis dataset contains statistics about 812 player-team stints for during the 2021-2022 NBA regular season. For players that played for \\(T\\) teams during the season (due to trade), there are \\(T+1\\) rows with one row for their performance with each of the \\(T\\) teams and another row indicating their total performance (where tm = TOT) across the full season regardless of team. The counting stats are reported on a per 100 team possessions scale, to normalize for playing time differences.\nThe data was collected using the ballr package in R., which gathers data from basketball-reference.com.\n\nnba-player-stats.csv\n\n\nVariable\nDescription\n\n\n\n\nplayer\nName of player\n\n\npos\nPlayer’s designated position\n\n\nage\nPlayer’s age on February 1st of the season\n\n\ntm\nName of team\n\n\ng\nNumber of games\n\n\ngs\nNumber of games started\n\n\nmp\nNumber of minutes played\n\n\nfg\nField goals per 100 team possessions\n\n\nfga\nField goal attempts per 100 team possessions\n\n\nfgpercent\nField goal percentage\n\n\nx3p\n3 point field goals per 100 team possessions\n\n\nx3pa\n3 point field goal attempts per 100 team possessions\n\n\nx3ppercent\n3 point field goal percentage\n\n\nx2p\n2 point field goals per 100 team possessions\n\n\nx2pa\n2 point field goal attempts per 100 team possessions\n\n\nx2ppercent\n2 point field goal percentage\n\n\nft\nFree throws per 100 team possessions\n\n\nfta\nFree throw attempts per 100 team possessions\n\n\nftpercent\nFree throw percentage\n\n\norb\nOffensive rebounds per 100 team possessions\n\n\ndrb\nDefensive rebounds per 100 team possessions\n\n\ntrb\nTotal rebounds per 100 team possessions\n\n\nast\nAssists per 100 team possessions\n\n\nstl\nSteals per 100 team possessions\n\n\nblk\nBlocks per 100 team possessions\n\n\ntov\nTurnovers per 100 team possessions\n\n\npf\nPersonal fouls per 100 team possessions\n\n\npts\nPoints per 100 team possessions\n\n\nortg\nOffensive Rating - an estimate of points produced per 100 possessions scale\n\n\ndrtg\nDefensive Rating - an estimate of points allowed per 100 possessions scale", + "crumbs": [ + "Home", + "Basketball", + "National Basketball Association Player Statistics" + ] }, { - "objectID": "running/boston_marathon_2023.html#questions", - "href": "running/boston_marathon_2023.html#questions", - "title": "2023 Boston Marathon runners", + "objectID": "basketball/nba-player-stats.html#questions", + "href": "basketball/nba-player-stats.html#questions", + "title": "National Basketball Association Player Statistics", "section": "Questions", - "text": "Questions\n\nWhat summary statistics are relevant to examine for finishing time?\nWhich race times would be considered outliers?\nAre there extreme outliers?\nWhich runner, the top male or top female, was more remarkable in comparison to their respective fields?" + "text": "Questions\n\nApply a clustering technique using the player statistics to create new labels for the types of NBA players, and compare to their known position labels. Compare and contrast your data-defined roles to the traditional basketball positions.\nUsing principal component analysis, project the players into a lower dimensional space. Choose an appropriate number of principal components, and describe which statistics contribute to your selected number of components. Based on your results, which player statistics explain the most variation between players in the dataset? Which stats are independent of each other?", + "crumbs": [ + "Home", + "Basketball", + "National Basketball Association Player Statistics" + ] }, { - "objectID": "running/boston_marathon_2023.html#references", - "href": "running/boston_marathon_2023.html#references", - "title": "2023 Boston Marathon runners", + "objectID": "basketball/nba-player-stats.html#references", + "href": "basketball/nba-player-stats.html#references", + "title": "National Basketball Association Player Statistics", "section": "References", - "text": "References\nBoston Athletic Association\nhttps://www.baa.org" + "text": "References\nElmore R (2020). ballr: Access to Current and Historical Basketball Data. R package version 0.2.6.", + "crumbs": [ + "Home", + "Basketball", + "National Basketball Association Player Statistics" + ] }, { - "objectID": "lacrosse/lacrosse_ncaa_div1.html", - "href": "lacrosse/lacrosse_ncaa_div1.html", - "title": "Team statistics for NCAA Division 1 Men’s and Women’s Lacrosse", + "objectID": "volleyball/volleyball_ncaa_team_stats.html", + "href": "volleyball/volleyball_ncaa_team_stats.html", + "title": "Team Statistics for Division I Women’s Volleyball", "section": "", - "text": "Over the years, lacrosse has experienced remarkable growth, with increased popularity and participation propelling the sport to new heights. This surge has led to a rise in skilled athletes competing in high-level leagues, notably NCAA Division I Men’s Lacrosse. Within this heightened competition, faceoffs are known to be a vital aspect of the game. Due to their important impact, players train to specifically specialize in them, as winning a faceoff often grants their teams crucial opportunities for offensive possession which can profoundly impact the course of the game." + "text": "Division 1 NCAA women’s volleyball is the highest level of collegiate competition for women’s volleyball in the United States. With well over 300 teams compete at the Division 1 level, there is fierce competition each year as only 64 teams make the post-season Volleyball Championship. The division demands high-level athletic performances, teamwork, and strategic game play from a variety of aspects. With each team having their own strengths and weaknesses, there is no exception of exciting matchups and and shocking results.\nIn volleyball, a kill is awarded to a player any time their attack is unreturnable by the opposition because it is the direct cause of the opponent not returning the ball. An assist is awarded when a set, pass, or dig to a teammate results in that teammate attacking the ball for a kill.", + "crumbs": [ + "Home", + "Volleyball", + "Team Statistics for Division I Women's Volleyball" + ] }, { - "objectID": "lacrosse/lacrosse_ncaa_div1.html#motivation", - "href": "lacrosse/lacrosse_ncaa_div1.html#motivation", - "title": "Team statistics for NCAA Division 1 Men’s and Women’s Lacrosse", + "objectID": "volleyball/volleyball_ncaa_team_stats.html#motivation", + "href": "volleyball/volleyball_ncaa_team_stats.html#motivation", + "title": "Team Statistics for Division I Women’s Volleyball", "section": "", - "text": "Over the years, lacrosse has experienced remarkable growth, with increased popularity and participation propelling the sport to new heights. This surge has led to a rise in skilled athletes competing in high-level leagues, notably NCAA Division I Men’s Lacrosse. Within this heightened competition, faceoffs are known to be a vital aspect of the game. Due to their important impact, players train to specifically specialize in them, as winning a faceoff often grants their teams crucial opportunities for offensive possession which can profoundly impact the course of the game." + "text": "Division 1 NCAA women’s volleyball is the highest level of collegiate competition for women’s volleyball in the United States. With well over 300 teams compete at the Division 1 level, there is fierce competition each year as only 64 teams make the post-season Volleyball Championship. The division demands high-level athletic performances, teamwork, and strategic game play from a variety of aspects. With each team having their own strengths and weaknesses, there is no exception of exciting matchups and and shocking results.\nIn volleyball, a kill is awarded to a player any time their attack is unreturnable by the opposition because it is the direct cause of the opponent not returning the ball. An assist is awarded when a set, pass, or dig to a teammate results in that teammate attacking the ball for a kill.", + "crumbs": [ + "Home", + "Volleyball", + "Team Statistics for Division I Women's Volleyball" + ] }, { - "objectID": "lacrosse/lacrosse_ncaa_div1.html#data", - "href": "lacrosse/lacrosse_ncaa_div1.html#data", - "title": "Team statistics for NCAA Division 1 Men’s and Women’s Lacrosse", + "objectID": "volleyball/volleyball_ncaa_team_stats.html#data", + "href": "volleyball/volleyball_ncaa_team_stats.html#data", + "title": "Team Statistics for Division I Women’s Volleyball", "section": "Data", - "text": "Data\n\nThere are three data sets: 2022-2023 statistics for Men’s teams, 2022-2023 statsitics for Women’s teams, and a dataset combining the Men’s and Women’s statisitcs (that only includes the variables available in each of the other two).\n\nlacrosse_men_ncaa_div1_2022_2023.csv\n\n\nThis data set contains 72 rows and 22 columns. Each row represents a men’s lacrosse team at the NCAA Division 1 level from the 2022-2023 season.\n\nlacrosse_women_ncaa_div1_2022_2023.csv\n\n\nVariable\nDescription\n\n\n\n\nTeam\ncollege of the team\n\n\navg_assists\naverage assists to goals per game\n\n\navg_caused_turnovers\naverage turnovers forced by the team per game\n\n\nclearing_pctg\npercentage of successful attempts to earn an offensive opportunity after gaining the ball in the teams own half\n\n\ntotal_faceoffs\ntotal faceoffs taken by a team for the season\n\n\nfaceoff_wins\ntotal faceoff wins by a team for the season\n\n\nfaceoff_win_pct\nproportion of total faceoff wins out of total faceoffs. Equivalent to the draw percentage in women’s lacrosse.\n\n\navg_goals\naverage goals per game\n\n\navg_goals_allowed\naverage goals allowed by the team per game\n\n\navg_ground_balls\naverage loose balls picked up by the team per game\n\n\nman_down_defense_pctg\nproportion of times a team stops the opponent from scoring while man down due to a penalty\n\n\nman_up_offense_pctg\nproportion of times the offense scores out of total opportunities while man up\n\n\navg_scoring_margin\naverage margin of goals per game\n\n\nopp_clear_pctg\nopponents clearing percentage averaged by game\n\n\navg_points\naverage team points per game\n\n\navg_saves\naverage saves per game\n\n\nshot_pctg\nproportion of shots that go in out of total shots\n\n\navg_turnovers\naverage turnovers that are directly the fault of a player per game\n\n\nW\ntotal wins by the team\n\n\nL\ntotal losses by the team\n\n\nwin_loss_pctg\nproportion of games won out of total games\n\n\n\nThis data set contains 120 rows and 18 columns. Each row represents a women’s lacrosse team at the NCAA Division 1 level from the 2022-2023 season.\n\n\n\n\n\n\n\nVariable Name\nDescription\n\n\n\n\nTeam\nThe name of the women’s NCAA lacrosse team.\n\n\ndraw_pctg\nDraw percentage, the proportion of draw controls won by the team in a game. Equivalent to the Faceoff percentage in Men’s lacrosse.\n\n\nturnovers_per_game\nAverage number of turnovers committed by the team per game.\n\n\nsog_per_game\nShots on goal (SOG) per game, the average number of shots on goal taken by the team.\n\n\nassists_per_game\nAverage number of assists made by the team per game.\n\n\ncaused_turnovers_per_game\nThe average number of caused turnovers by the team per game.\n\n\nclearing_pctg\nClearing percentage, the rate at which the team successfully clears the ball from defense to offense.\n\n\ndraw_controls_per_game\nThe average number of draw controls won by the team per game.\n\n\nfouls_per_game\nAverage number of fouls committed by the team per game.\n\n\nfree_position_pctg\nFree position percentage, the rate of successful free position shots taken by the team.\n\n\nground_balls_per_game\nThe average number of ground balls scooped by the team per game.\n\n\npoints_per_game\nAverage number of total points (goals + assists) scored by the team per game.\n\n\nsave_pctg\nSave percentage, the proportion of shots on goal saved by the team’s goalkeeper.\n\n\ngoals_allowed_per_game\nAverage number of goals allowed by the team’s defense per game.\n\n\ngoals_per_game\nAverage number of goals scored by the team per game.\n\n\nshots_per_game\nAverage number of total shots (on goal + off target) taken by the team per game.\n\n\nshot_pctg\nShooting percentage, the proportion of shots on goal that result in a goal.\n\n\nwin_pctg\nThe team’s winning percentage, calculated as the number of wins divided by total games played.\n\n\n\n\nNote: The table provides a brief description of each variable, but more context and understanding might be required to interpret and analyze these statistics effectively in the context of women’s NCAA lacrosse.\n\nlacrosse_men_and_women_ncaa_div1_2022_2023.csv\n\n\nThis data set contains 192 rows and 6 columns. Each row represents a lacrosse team at the NCAA Division 1 level from the 2022-2023 season. Teams come from both the men’s and womens division.\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nTeam\ncollege of the team\n\n\nfaceoff_win_pct\nthe proportion of faceoffs win out of total faceoffs taken\n\n\nshots_per_game\naverage amount of shots per game\n\n\nshot_pctg\nproportion of shots that are goals out of total shots\n\n\nwin_loss_percentage\namount of wins out of total games played\n\n\nDivision\nthe division the team is in (Mens or Womens)" + "text": "Data\nThe data set contains 334 rows and 14 columns. Each row represents a team at the NCAA Division 1 level from the 2022-2023 season.\n\nvolleyball_ncaa_div1_2022_23.csv\n\n\nVariable\nDescription\n\n\n\n\nTeam\ncollege of the volleyball team\n\n\nConference\nconference to which the team belongs\n\n\nregion\nregion to which the team belongs\n\n\naces_per_set\nthe average amount of balls served that directly lead to a point not including errors by the opponent per set\n\n\nassists_per_set\nthe average amount of sets, passes, or digs to a teammate that directly result in a kill per set\n\n\nteam_attacks_per_set\nthe average amount of times the ball is sent to the other team’s court per set\n\n\nblocks_per_set\nthe average amount of times the ball is blocked from being hit on to the teams side per set\n\n\ndigs_per_set\naverage amount of times the ball is passed by a player successfully after an opponents attack per set\n\n\nhitting_pctg\ntotal team kills minus team hitting errors all divided by total attempts\n\n\nkills_per_set\naverage amount of hits that directly result in a point per set\n\n\nopp_hitting_pctg\nthe average hitting percentage of the teams opponent per set\n\n\nW\nthe amount of team wins for the season\n\n\nL\nthe amount of team losses for the season\n\n\nwin_loss_pctg\nthe amount of total wins divided by the total matches of the season", + "crumbs": [ + "Home", + "Volleyball", + "Team Statistics for Division I Women's Volleyball" + ] }, { - "objectID": "lacrosse/lacrosse_ncaa_div1.html#questions", - "href": "lacrosse/lacrosse_ncaa_div1.html#questions", - "title": "Team statistics for NCAA Division 1 Men’s and Women’s Lacrosse", + "objectID": "volleyball/volleyball_ncaa_team_stats.html#questions", + "href": "volleyball/volleyball_ncaa_team_stats.html#questions", + "title": "Team Statistics for Division I Women’s Volleyball", "section": "Questions", - "text": "Questions\nUsing either Women’s or Men’s\n\nWhich teams have better faceoff win proportions than the average?\nWhich teams have worse faceoff win proportions than the average?\nWhat sort of impact does faceoff percentage have on the success of the team?\n\nUsing the data with both Women’s and Men’s\n\nIn terms of a team’s win percentage, are faceoffs more important for men or women?\nWhat factors could make faceoffs more important for men or women?\nHow does faceoff win percentage affect winning percentage?" + "text": "Questions\n\nWhat variables are most highly correlated with win/loss percentage of a team?\nHow are assists and kills related?\nWhat model is best for predicting win/loss percentage?", + "crumbs": [ + "Home", + "Volleyball", + "Team Statistics for Division I Women's Volleyball" + ] }, { - "objectID": "lacrosse/lacrosse_ncaa_div1.html#references", - "href": "lacrosse/lacrosse_ncaa_div1.html#references", - "title": "Team statistics for NCAA Division 1 Men’s and Women’s Lacrosse", + "objectID": "volleyball/volleyball_ncaa_team_stats.html#references", + "href": "volleyball/volleyball_ncaa_team_stats.html#references", + "title": "Team Statistics for Division I Women’s Volleyball", "section": "References", - "text": "References\nNCAA Website: Women’s and Men’s Lacrosse Division I\nhttp://stats.ncaa.org/rankings/change_sport_year_div" - }, - { - "objectID": "index.html", - "href": "index.html", - "title": "SCORE Sports Data Repository", - "section": "", - "text": "The SCORE Network Sports Data Repository curates interesting datasets across a variety of sports for use in statistics and data science education. Each dataset has the following properties:\n\nA sports question of interest, with context motivating why the dataset is relevant and interesting to explore.\nA statistics / data science topic which the dataset can be used to help teach.\nExample questions that instructors can use to help build lessons, handouts, and SCORE modules.\n\nDatasets are organized by sport along the left, but you can also browse by statistics and data science topic.\nThis repository is heavily inspired by the CMU S&DS Data Repository.\nThe development of the SCORE with Data network is funded by the National Science Foundation (award 2142705)." + "text": "References\nNCAA - Women’s Volleyball\nhttps://www.ncaa.com/stats/volleyball-women/d1", + "crumbs": [ + "Home", + "Volleyball", + "Team Statistics for Division I Women's Volleyball" + ] }, { - "objectID": "softball/softball_d1_2021-2022.html", - "href": "softball/softball_d1_2021-2022.html", - "title": "Batting Statistics for Division I Softball", + "objectID": "hockey/nhl_2223.html", + "href": "hockey/nhl_2223.html", + "title": "NHL Team Statistics", "section": "", - "text": "Data for a particular sport is often stored across numerous locations. For example, in NCAA Division I Softball, batting statistics for each season are typically stored in separate tables. (See for example the statistics hosted by https://d1softball.com/)\nSuppose we are interested in tracking the statistics of players across multiple seasons. A common way to prep the data to do this is to use join statements to merge each season’s data into one table with one row per player (and columns associated with their different statistics for each season)." + "text": "In 2005 the National Hockey League added shootouts to regular season games. In the event that regulation time ends in a tie and no goals are scored in overtime, the game goes to a 3-player shootout. This data set can be used to explore the relationship between various team-level statistics.\nRegulation time in an NHL game consists of three 20 minute periods. If regulation time ends and the score is tied (both teams have the same number of goals), the game goes into a 5 minute 3-on-3 overtime, where the next goal wins. If the game is still not resolved after overtime, it goes into a 3 round shootout, where players take a 1-on-1 breakaway against the goalie. If still tied after three shootout rounds, shootout rounds continue until one team is victorious. A team gets 2 points for a win (whether it’s regulation, overtime, or shootout), and the losing team gets 0 points for a regulation loss, or 1 point for an overtime/shootout loss.", + "crumbs": [ + "Home", + "Hockey", + "NHL Team Statistics" + ] }, { - "objectID": "softball/softball_d1_2021-2022.html#motivation", - "href": "softball/softball_d1_2021-2022.html#motivation", - "title": "Batting Statistics for Division I Softball", + "objectID": "hockey/nhl_2223.html#motivation", + "href": "hockey/nhl_2223.html#motivation", + "title": "NHL Team Statistics", "section": "", - "text": "Data for a particular sport is often stored across numerous locations. For example, in NCAA Division I Softball, batting statistics for each season are typically stored in separate tables. (See for example the statistics hosted by https://d1softball.com/)\nSuppose we are interested in tracking the statistics of players across multiple seasons. A common way to prep the data to do this is to use join statements to merge each season’s data into one table with one row per player (and columns associated with their different statistics for each season)." + "text": "In 2005 the National Hockey League added shootouts to regular season games. In the event that regulation time ends in a tie and no goals are scored in overtime, the game goes to a 3-player shootout. This data set can be used to explore the relationship between various team-level statistics.\nRegulation time in an NHL game consists of three 20 minute periods. If regulation time ends and the score is tied (both teams have the same number of goals), the game goes into a 5 minute 3-on-3 overtime, where the next goal wins. If the game is still not resolved after overtime, it goes into a 3 round shootout, where players take a 1-on-1 breakaway against the goalie. If still tied after three shootout rounds, shootout rounds continue until one team is victorious. A team gets 2 points for a win (whether it’s regulation, overtime, or shootout), and the losing team gets 0 points for a regulation loss, or 1 point for an overtime/shootout loss.", + "crumbs": [ + "Home", + "Hockey", + "NHL Team Statistics" + ] }, { - "objectID": "softball/softball_d1_2021-2022.html#data", - "href": "softball/softball_d1_2021-2022.html#data", - "title": "Batting Statistics for Division I Softball", + "objectID": "hockey/nhl_2223.html#data", + "href": "hockey/nhl_2223.html#data", + "title": "NHL Team Statistics", "section": "Data", - "text": "Data\nThere are four data tables associated with this page.\nTwo of them, batting2021 and batting2022, contain statistics for all Division 1 Softball player who played in the 2021 and/or 2022 season.\nThe other two, batting2021_subset and batting2022_subset, represent non-random samples taken the full data and can be used to manually illustrate how to merge the tables.\nThe 2021 data set has 2131 rows with 22 columns. The 2022 data set has 2275 rows with the same 22 columns. Each row represents a Division 1 Softball player who played in the 2021 and/or 2022 season.\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nPlayer\nName of the softball player\n\n\nTeam\nName of the team (school) for each player\n\n\nBA\nBatting Average - The ratio of a player’s total base hits to their total number of at-bats, indicating their ability to make successful hits.\n\n\nOBP\nOn-Base Percentage - The percentage of times a player reaches base, either through a hit, walk, or hit-by-pitch, out of their total plate appearances.\n\n\nSLG\nSlugging Percentage - The measure of a player’s power by calculating the total number of bases they accumulate per at-bat.\n\n\nOPS\nOn-Base Plus Slugging - The sum of a player’s on-base percentage and slugging percentage, providing a comprehensive measure of their offensive contribution.\n\n\nGP\nGames Played - The total number of games in which a player has participated.\n\n\nPA\nPlate Appearances - The total number of times a player has come up to bat, including at-bats, walks, and hit-by-pitches.\n\n\nAB\nAt-Bats - The number of times a player has officially faced a pitcher and had an opportunity to hit.\n\n\nR\nRuns Scored - The number of times a player has crossed home plate and scored a run.\n\n\nH\nHits - The total number of successful hits made by a player.\n\n\n2B\nDoubles - The number of hits resulting in the batter reaching second base.\n\n\n3B\nTriples - The number of hits resulting in the batter reaching third base.\n\n\nHR\nHome Runs - The number of hits resulting in the batter scoring a run by hitting the ball out of the park.\n\n\nRBI\nRuns Batted In - The number of runs a player has driven in with a hit or sacrifice.\n\n\nHBP\nHit by Pitch - The number of times a player has been struck by a pitched ball and awarded first base.\n\n\nBB\nWalks - The number of times a player has received a base on balls (four balls) and been awarded first base.\n\n\nK\nStrikeouts - The number of times a player has been called out after accumulating three strikes.\n\n\nSB\nStolen Bases - The number of times a player successfully advances to the next base without a hit, during a pitch while the ball is in play.\n\n\nCS\nCaught Stealing - The number of times a player is thrown out while attempting to steal a base.\n\n\n\n\nThe data tables are available for individual download as csv files or bundled together into either a SQLite or DuckDB database.\n\nbatting2021.csv\n\n\nbatting2022.csv\n\n\nbatting2021_subset.csv\n\n\nbatting2022_subset.csv\n\n\nsoftball_batting.sqlite\n\n\nsoftball_batting.duckdb" + "text": "Data\nThe data set contains the final standings of the 2022-2023 NHL Season with team level statistics.\n\nnhl_2223.csv\n\n\nVariable\nDescription\n\n\n\n\nTeam\nNHL team abbreviation\n\n\nGP\nTotal games played\n\n\nSOG\nNumber of games that went to shootout\n\n\nW\nWins\n\n\nL\nLosses\n\n\nOTL\nOvertime losses\n\n\nPTS\nPoints\n\n\nReg_PTS\nPoints won during regulation time or overtime (2 points per game)\n\n\nPPG\nPoints won per game in regulation time or overtime (# of non-shootout points / # of non-shootout games)\n\n\nSO_PTS\nPoints won from shootout (1 extra point per shootout)\n\n\nPct_SO\nProportion of possible points from shootout won (# of shootout points / # of shootout games)\n\n\nRW\nNumber of regulation wins\n\n\nROW\nNumber of regulation and overtime wins\n\n\nSOW\nNumber of shootout wins\n\n\nSOL\nNumber of shootout losses\n\n\nGF\nTotal goals scored\n\n\nGA\nTotal goals against\n\n\nDIFF\nGoal differential (GF-GA)", + "crumbs": [ + "Home", + "Hockey", + "NHL Team Statistics" + ] }, { - "objectID": "softball/softball_d1_2021-2022.html#questions", - "href": "softball/softball_d1_2021-2022.html#questions", - "title": "Batting Statistics for Division I Softball", + "objectID": "hockey/nhl_2223.html#questions", + "href": "hockey/nhl_2223.html#questions", + "title": "NHL Team Statistics", "section": "Questions", - "text": "Questions\n\nWith multiple seasons of data, you can investigate the different ways to join the the 2021 and 2022 statistics. (e.g., comparing the results of an inner join vs a full join) This can be done manually using subsets of data (to illustrate the joins with simple examples) or scaled up using the full data\nWith a single season/table, you can practice different data wrangling techniques. e.g., finding the league leaders in different categories" + "text": "Questions\n\nPlot GA versus GF. Describe the trend. Do teams that score more goals tend to allow more or less goals against?\nIs offensive ability (‘GF’) or defensive ability (‘GA’) a better predictor of overall performance of a team (as measured by ‘PTS’)?\nIs there a relationship between the ability to win shootouts (‘Pct_SO’) the ability to win games before shootouts (‘PPG’)?\nIs there a relationship between the total number of goals a team scored during the season (GF) and the proportion of shootout points they won (Pct_SO)?", + "crumbs": [ + "Home", + "Hockey", + "NHL Team Statistics" + ] }, { - "objectID": "softball/softball_d1_2021-2022.html#references", - "href": "softball/softball_d1_2021-2022.html#references", - "title": "Batting Statistics for Division I Softball", + "objectID": "hockey/nhl_2223.html#references", + "href": "hockey/nhl_2223.html#references", + "title": "NHL Team Statistics", "section": "References", - "text": "References\nStatistics. D1Softball. (2019, October 24). https://d1softball.com/statistics/" + "text": "References\nNHL Standings 2022-23. ESPN (n.d.). https://www.espn.com/nhl/standings/_/season/2023/group/league", + "crumbs": [ + "Home", + "Hockey", + "NHL Team Statistics" + ] }, { - "objectID": "triathlon/triathlon_lakeplacid_women_ironman.html", - "href": "triathlon/triathlon_lakeplacid_women_ironman.html", - "title": "Lake Placid Women’s Ironman Triathlon", + "objectID": "hockey/nhl-shots.html", + "href": "hockey/nhl-shots.html", + "title": "National Hockey League Shots", "section": "", - "text": "The motivation for this data analysis is to explore the relationships between swim times, bike times, and run times (in minutes) in order to gain insights into the performance patterns of the athletes. By analyzing these relationships, we can understand the interplay between different segments of the race and potentially identify areas of improvement for athletes." + "text": "The National Hockey League (NHL) is the top professional men’s hockey league in the world. The league records every shot players take along with contextual information about the shot such as its location, the player’s distance and angle to the goal when attempting the shot, as well as the outcome (blocked, missed, or goal). Using this information, the hockey analytics community have developed measures of shot quality known as expected goals. With this dataset, you can create your own expected goals model to predict the shot outcome given relevant features.", + "crumbs": [ + "Home", + "Hockey", + "National Hockey League Shots" + ] }, { - "objectID": "triathlon/triathlon_lakeplacid_women_ironman.html#motivation", - "href": "triathlon/triathlon_lakeplacid_women_ironman.html#motivation", - "title": "Lake Placid Women’s Ironman Triathlon", + "objectID": "hockey/nhl-shots.html#motivation", + "href": "hockey/nhl-shots.html#motivation", + "title": "National Hockey League Shots", "section": "", - "text": "The motivation for this data analysis is to explore the relationships between swim times, bike times, and run times (in minutes) in order to gain insights into the performance patterns of the athletes. By analyzing these relationships, we can understand the interplay between different segments of the race and potentially identify areas of improvement for athletes." + "text": "The National Hockey League (NHL) is the top professional men’s hockey league in the world. The league records every shot players take along with contextual information about the shot such as its location, the player’s distance and angle to the goal when attempting the shot, as well as the outcome (blocked, missed, or goal). Using this information, the hockey analytics community have developed measures of shot quality known as expected goals. With this dataset, you can create your own expected goals model to predict the shot outcome given relevant features.", + "crumbs": [ + "Home", + "Hockey", + "National Hockey League Shots" + ] }, { - "objectID": "triathlon/triathlon_lakeplacid_women_ironman.html#data", - "href": "triathlon/triathlon_lakeplacid_women_ironman.html#data", - "title": "Lake Placid Women’s Ironman Triathlon", + "objectID": "hockey/nhl-shots.html#data", + "href": "hockey/nhl-shots.html#data", + "title": "National Hockey League Shots", "section": "Data", - "text": "Data\nThe main data set has 489 rows with 17 columns. Each row represents a female who has participated in the Ironman in 2022. Additionally, a subset of 2022 female Canadian participants is available and has 64 rows.\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nBib\nregistration number of each runner used for identification\n\n\nName\nThe participant’s name\n\n\nCountry\nWhat country the participant is from\n\n\nGender\nThe participant’s gender\n\n\nDivision\nThe age range or membership a runner is\n\n\nDivision.Rank\nWithin the divisions, the place each runner has obtained over all races\n\n\nOverall.Time\nThe total time it took to complete the Ironman in minutes\n\n\nOverall.Rank\nThe runner’s finishing place for that particular triathlon\n\n\nSwim.Time\nThe time in minutes it took to complete the swimming portion\n\n\nSwim.Rank\nThe place the runner finished for the swim portion\n\n\nBike.Time\nThe time in minutes it took to complete the biking portion\n\n\nBike.Rank\nThe place the runner finished for the bike portion\n\n\nRun.Time\nThe time in minutes it took to complete the running portion\n\n\nRun.Rank\nThe place the runner finished for the running portion\n\n\nFinish.Status\nStates whether someone completed the Ironman successfully\n\n\nLocation\nWhere the Ironman takes place\n\n\nYear\nThey year when the mentioned participant ran\n\n\n\n\n\n\n\nironman_lake_placid_female_2022.csv\n\n\nironman_lake_placid_female_2022_canadian.csv" + "text": "Data\nThis dataset contains information about 160,573 shots during the 2021-2022 NHL season.\nThe data was collected using the hockeyR package in R.\n\nnhl-shots-2021.csv.gz\n\n\nVariable\nDescription\n\n\n\n\ngame_id\nUnique integer identifier for game shot took place in\n\n\ndescription\nString detailed description of shot event\n\n\nshot_outcome\nString denoting the outcome of the shot, either BLOCKED_SHOT (meaning blocked by a non-goalie), GOAL, MISSED_SHOT (shot that missed the net), or SHOT (shot on net that was saved by a goalie)\n\n\nperiod\nInteger value of the game period\n\n\nperiod_seconds_remaining\nNumeric value of the seconds remaining in the period\n\n\ngame_seconds_remaining\nNumeric value of the seconds remaining in the game; negative for overtime periods\n\n\nhome_score\nInteger value of the home team score after the event\n\n\naway_score\nInteger value of the away team score after the event\n\n\nhome_name\nString name of the home team\n\n\naway_name\nString name of the away team\n\n\nevent_team\nString defining the team taking the shot\n\n\nevent_goalie_name\nString name of goalie (if in net)\n\n\nempty_net\nBoolean indicating if the shot was during an empty net situation, TRUE if so but FALSE or NA if not\n\n\nevent_player_1_name\nString name of the primary event player\n\n\nevent_player_1_type\nString indicator for the role of event_player_1 (typically the shooter)\n\n\nevent_player_2_name\nString name of the secondary event player\n\n\nevent_player_2_type\nString indicator for the role of event_player_2 (blocker, assist, or goalie)\n\n\nstrength_code\nString indicator for game strength: EV (Even), SH (Shorthanded), or PP (Power Play)\n\n\nx_fixed\nNumeric transformed x-coordinate of event in feet, where the home team always shoots to the right, away team to the left\n\n\ny_fixed\nNumeric transformed y-coordinate of event in feet, where the home team always shoots to the right, away team to the left\n\n\nshot_distance\nNumeric distance (in feet) to center of net for unblocked shot events\n\n\nshot_angle\nNumeric angle (in degrees) to center of net for unlocked shot events", + "crumbs": [ + "Home", + "Hockey", + "National Hockey League Shots" + ] }, { - "objectID": "triathlon/triathlon_lakeplacid_women_ironman.html#questions", - "href": "triathlon/triathlon_lakeplacid_women_ironman.html#questions", - "title": "Lake Placid Women’s Ironman Triathlon", + "objectID": "hockey/nhl-shots.html#questions", + "href": "hockey/nhl-shots.html#questions", + "title": "National Hockey League Shots", "section": "Questions", - "text": "Questions\n\nWith the different times and rank variables, you can explore the relationships between them through things like correlation coefficients and linear regression.\nOther things like finding the best variables at predicting a runner’s overall rank in a race and then even a division rank would be easy to model using this data set." + "text": "Questions\n\nBuild a logistic regression model to predict whether or not the shot will result in a goal based on the shot distance and angle.\nBuild a classification model to predict the outcome based on the spatial x,y coordinates of the shot.\nCreate a visualization displaying the joint frequency of shot locations. Do there appear to be any clear modes of frequently taken shots? Create a conditional version of this display by shot outcome. Does the distribution shape vary by shot outcome? (You can also perform a similar analysis by team).", + "crumbs": [ + "Home", + "Hockey", + "National Hockey League Shots" + ] }, { - "objectID": "triathlon/triathlon_lakeplacid_women_ironman.html#score-modules", - "href": "triathlon/triathlon_lakeplacid_women_ironman.html#score-modules", - "title": "Lake Placid Women’s Ironman Triathlon", - "section": "SCORE Modules", - "text": "SCORE Modules\nThe following SCORE modules are associated with these data.\n\nIronman Triathlons - Correlations" - }, - { - "objectID": "triathlon/triathlon_lakeplacid_women_ironman.html#references", - "href": "triathlon/triathlon_lakeplacid_women_ironman.html#references", - "title": "Lake Placid Women’s Ironman Triathlon", + "objectID": "hockey/nhl-shots.html#references", + "href": "hockey/nhl-shots.html#references", + "title": "National Hockey League Shots", "section": "References", - "text": "References\nIronman Lake Placid Results and articles. CoachCox. (n.d.). https://www.coachcox.co.uk/imstats/series/13/" + "text": "References\nMorse D (2023). hockeyR: Collect and Clean Hockey Stats. R package version 1.3.1, https://github.com/danmorse314/hockeyR.", + "crumbs": [ + "Home", + "Hockey", + "National Hockey League Shots" + ] }, { - "objectID": "football/nfl-team-statistics.html", - "href": "football/nfl-team-statistics.html", - "title": "National Football League Team Statistics", + "objectID": "soccer/nwsl-team-stats.html", + "href": "soccer/nwsl-team-stats.html", + "title": "National Women’s Soccer League Team Statistics", "section": "", - "text": "The National Football League (NFL) is the top professional American football league in the world. While a team’s record ultimately determines whether or not they make the playoffs, their score differential (points for - points against) is often a better indicator of a team’s ability. But what aspects of a team’s performance are related to their point differential? Is passing more important than rushing? What about offense in comparison to defense? The NFL records a variety of statistics, and the public NFL analytics community have developed advanced metrics such as expected points added (EPA) that provide deeper insight into a team’s performance. With this dataset of statistics dating back to 1999, you can explore variation between teams since as well as which types of statistics are relevant predictor variables of record and point differential." + "text": "The National Women’s Soccer League (NWSL) is the top professional women’s soccer league in the United States. While a team’s record ultimately determines their ranking, goal differential (goals scored - goals conceded) is often a better indicator of a team’s ability. But what aspects of a team’s performance are related to their goal differential? The NWSL records a variety of statistics describing a team’s performance, such as the percentage of time they maintain possession, percentage of shots on target, etc. With this dataset, you can explore variation between teams as well as which statistics are relevant predictor variables of goal differential.", + "crumbs": [ + "Home", + "Soccer", + "National Women’s Soccer League Team Statistics" + ] }, { - "objectID": "football/nfl-team-statistics.html#motivation", - "href": "football/nfl-team-statistics.html#motivation", - "title": "National Football League Team Statistics", + "objectID": "soccer/nwsl-team-stats.html#motivation", + "href": "soccer/nwsl-team-stats.html#motivation", + "title": "National Women’s Soccer League Team Statistics", "section": "", - "text": "The National Football League (NFL) is the top professional American football league in the world. While a team’s record ultimately determines whether or not they make the playoffs, their score differential (points for - points against) is often a better indicator of a team’s ability. But what aspects of a team’s performance are related to their point differential? Is passing more important than rushing? What about offense in comparison to defense? The NFL records a variety of statistics, and the public NFL analytics community have developed advanced metrics such as expected points added (EPA) that provide deeper insight into a team’s performance. With this dataset of statistics dating back to 1999, you can explore variation between teams since as well as which types of statistics are relevant predictor variables of record and point differential." + "text": "The National Women’s Soccer League (NWSL) is the top professional women’s soccer league in the United States. While a team’s record ultimately determines their ranking, goal differential (goals scored - goals conceded) is often a better indicator of a team’s ability. But what aspects of a team’s performance are related to their goal differential? The NWSL records a variety of statistics describing a team’s performance, such as the percentage of time they maintain possession, percentage of shots on target, etc. With this dataset, you can explore variation between teams as well as which statistics are relevant predictor variables of goal differential.", + "crumbs": [ + "Home", + "Soccer", + "National Women’s Soccer League Team Statistics" + ] }, { - "objectID": "football/nfl-team-statistics.html#data", - "href": "football/nfl-team-statistics.html#data", - "title": "National Football League Team Statistics", + "objectID": "soccer/nwsl-team-stats.html#data", + "href": "soccer/nwsl-team-stats.html#data", + "title": "National Women’s Soccer League Team Statistics", "section": "Data", - "text": "Data\nThis dataset contains statistics about the regular season performance for each NFL team from 1999 to 2022 team. The data was collected using the nflreadr package in R.\nEach row in the dataset corresponds to a single NFL team in a single regular season. There are a total of 765 team-seasons, with 56 total columns. The column names are organized below by the type of information they contain, with the first set of columns being self-explanatory:\n\nnfl-team-statistics.csv\n\n\nVariable\nDescription\n\n\n\n\nseason\nRegular season year of team’s statistics\n\n\nteam\nNFL team three letter abbreviation\n\n\n\nThere are also columns with season level outcomes:\n\n\n\nVariable\nDescription\n\n\n\n\npoints_score\nTotal number of points scored by the team\n\n\npoints_allowed\nTotal number of points allowed by the team\n\n\nwins\nNumber of games the team won\n\n\nlosses\nNumber of games the team lost\n\n\nties\nNumber of games the team tied\n\n\nscore_differential\npoints scored - points allowed\n\n\n\nThere are also several columns corresponding to offensive and defensive summaries of the team’s performance in the season separated by play type (either pass or run):\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\noffense/defense_completion_percentage\nPassing completion percentage either for (offense) or against (defense)\n\n\noffense/defense_total_yards_gained_pass/run\nTotal number of yards gained (offense) or allowed (defense) by play type (pass or run)\n\n\noffense/defense_ave_yards_gained_pass/run\nAverage number of yards gained (offense) or allowed (defense) per play by play type (pass or run)\n\n\noffense/defense_total_air_yards\nTotal number of air yards gained (offense) or allowed (defense), where air yards correspond to perpendicular yards traveled from the line of scrimmage to location of catch for passing plays\n\n\noffense/defense_ave_air_yards\nAverage number of air yards gained (offense) or allowed (defense) per passing play\n\n\noffense/defense_total_yac\nTotal number of yards after catch gained (offense) or allowed (defense)\n\n\noffense/defense_ave_yac\nAverage number of yards after catch gained (offense) or allowed (defense) per passing play\n\n\noffense/defense_n_plays_pass/run\nTotal number of plays by the team (offense) or against (defense) by play type (pass or run)\n\n\noffense/defense_n_interceptions\nTotal number of interceptions thrown (offense) or caught (defense)\n\n\noffense/defense/n_fumbles_lost_pass/run\nTotal number of fumbles lost (offense) or forced (defense) by play type (pass or run)\n\n\noffense/defense_total_epa_pass/run\nTotal expected points added (offense) or allowed (defense) by play type (pass or run)\n\n\noffense/defense_ave_epa_pass/run\nAverage expected points added (offense) or allowed (defense) per play by play type (pass or run)\n\n\noffense/defense_total_wpa_pass/run\nTotal win probability added (offense) or allowed (defense) by play type (pass or run)\n\n\noffense/defense_ave_wpa_pass/run\nAverage win probability added (offense) or allowed (defense) per play by play type (pass or run)\n\n\noffense/defense_total_epa_pass/run\nTotal expected points added (offense) or allowed (defense) by play type (pass or run)\n\n\noffense/defense_success_rate_pass/run\nProportion of plays with positive expected points added (offense) or allowed (defense) by play type (pass or run)\n\n\n\nThe EPA variables are advanced NFL statistics, conveying how much value a team is adding over the average team in a given situation. It’s on a points scale instead of the typically used yards, because not all yards are created equal in American football (10 yard gain on 3rd and 15 is much less valuable than a 2 yard gain on 4th and 1). For offensive stats the higher the EPA the better, but for defensive stats the lower (more negative) the EPA the better. The WPA variables are similar except they are measuring play value in terms of win probability." + "text": "Data\nThis dataset contains statistics about the regular season performance for each NWSL team from 2016 to 2022 (excluding 2020 which was cancelled due to COVID).\nThe data was collected using the nwslR package in R.\n\nnwsl-team-stats.csv\n\n\nVariable\nDescription\n\n\n\n\nteam_name\nName of NWSL team\n\n\nseason\nRegular season year of team’s statistics\n\n\ngames_played\nNumber of games team played in season\n\n\ngoal_differential\nGoals scored - goals conceded\n\n\ngoals\nNumber of goals scores\n\n\ngoals_conceded\nNumber of goals conceded\n\n\ncross_accuracy\nPercent of crosses that were successful\n\n\ngoal_conversion_pct\nPercent of shots scored\n\n\npass_pct\nPass accuracy\n\n\npass_pct_opposition_half\nPass accuracy in opposition half\n\n\npossession_pct\nPercentage of overall ball possession the team had during the season\n\n\nshot_accuracy\nPercentage of shots on target\n\n\ntackle_success_pct\nPercent of successful tackles", + "crumbs": [ + "Home", + "Soccer", + "National Women’s Soccer League Team Statistics" + ] }, { - "objectID": "football/nfl-team-statistics.html#questions", - "href": "football/nfl-team-statistics.html#questions", - "title": "National Football League Team Statistics", + "objectID": "soccer/nwsl-team-stats.html#questions", + "href": "soccer/nwsl-team-stats.html#questions", + "title": "National Women’s Soccer League Team Statistics", "section": "Questions", - "text": "Questions\n\nBuild a linear regression model to predict score differential as a function of the team’s statistics. Which variables, if any, are predictive of score differential in a season? Are certain types of variables more predictive? (e.g., passing versus rushing,) Describe their relationships.\nUsing principal component analysis, project the team seasons into a lower dimensional space using only the team statistics that do not include points or record. Choose an appropriate number of principal components, and describe which statistics contribute to your selected number of components. Based on your results, which team statistics explain the most variation between team-seasons in the dataset? Which team statistics are independent of each other?" + "text": "Questions\n\nBuild a linear regression model to predict goal differential as a function of the team’s statistics. Which variables, if any, are predictive of a team’s goal differential in a season? Describe their relationships.\nUsing principal component analysis, project the team seasons into a lower dimensional space using only the team statistics that do not include goals scored or conceded. Choose an appropriate number of principal components, and describe which statistics contribute to your selected number of components. Based on your results, which team statistics explain the most variation between team-seasons in the dataset? Which team statistics are independent of each other?", + "crumbs": [ + "Home", + "Soccer", + "National Women’s Soccer League Team Statistics" + ] }, { - "objectID": "football/nfl-team-statistics.html#references", - "href": "football/nfl-team-statistics.html#references", - "title": "National Football League Team Statistics", + "objectID": "soccer/nwsl-team-stats.html#references", + "href": "soccer/nwsl-team-stats.html#references", + "title": "National Women’s Soccer League Team Statistics", "section": "References", - "text": "References\nHo T, Carl S (2022). nflreadr: Download ‘nflverse’ Data. R package version 1.3.1, https://CRAN.R-project.org/package=nflreadr." + "text": "References\nDror A (2023). nwslR: Compiles dataset for the National Women’s Soccer League (NWSL). R package version 0.0.0.9001.", + "crumbs": [ + "Home", + "Soccer", + "National Women’s Soccer League Team Statistics" + ] }, { "objectID": "soccer/soccer-heights.html", "href": "soccer/soccer-heights.html", "title": "Soccer Heights", "section": "", - "text": "A researcher wants to investigate whether there are differences among the mean height for different positions (Goalkeeper, Defender, Midfield, Attack, or Missing) of soccer players. Goalkeepers are typically thought to be taller than other positions to make up in wingspan to cover the goal better. The dataset contains information on each player including their name, position, preferred foot, and height. For this analysis, only the position and height of the player will be used. A sample of 20 players was taken from each position for this analysis." + "text": "A researcher wants to investigate whether there are differences among the mean height for different positions (Goalkeeper, Defender, Midfield, Attack, or Missing) of soccer players. Goalkeepers are typically thought to be taller than other positions to make up in wingspan to cover the goal better. The dataset contains information on each player including their name, position, preferred foot, and height. For this analysis, only the position and height of the player will be used. A sample of 20 players was taken from each position for this analysis.", + "crumbs": [ + "Home", + "Soccer", + "Soccer Heights" + ] }, { "objectID": "soccer/soccer-heights.html#motivation", "href": "soccer/soccer-heights.html#motivation", "title": "Soccer Heights", "section": "", - "text": "A researcher wants to investigate whether there are differences among the mean height for different positions (Goalkeeper, Defender, Midfield, Attack, or Missing) of soccer players. Goalkeepers are typically thought to be taller than other positions to make up in wingspan to cover the goal better. The dataset contains information on each player including their name, position, preferred foot, and height. For this analysis, only the position and height of the player will be used. A sample of 20 players was taken from each position for this analysis." + "text": "A researcher wants to investigate whether there are differences among the mean height for different positions (Goalkeeper, Defender, Midfield, Attack, or Missing) of soccer players. Goalkeepers are typically thought to be taller than other positions to make up in wingspan to cover the goal better. The dataset contains information on each player including their name, position, preferred foot, and height. For this analysis, only the position and height of the player will be used. A sample of 20 players was taken from each position for this analysis.", + "crumbs": [ + "Home", + "Soccer", + "Soccer Heights" + ] }, { "objectID": "soccer/soccer-heights.html#data", "href": "soccer/soccer-heights.html#data", "title": "Soccer Heights", "section": "Data", - "text": "Data\nEach row of data gives gives information for one of 100 soccer players in the sample with 20 players at each of five positions for a total sample size of 100.\n\n\n\nVariable\nDescription\n\n\n\n\nname\nName of the player\n\n\nposition\nPlayer’s current position\n\n\nfoot\nPlayer’s preferred foot\n\n\nheight_in_cm\nPlayer’s height (in cm)\n\n\n\nsoccer_heights_sample.csv" + "text": "Data\nEach row of data gives gives information for one of 100 soccer players in the sample with 20 players at each of five positions for a total sample size of 100.\n\n\n\nVariable\nDescription\n\n\n\n\nname\nName of the player\n\n\nposition\nPlayer’s current position\n\n\nfoot\nPlayer’s preferred foot\n\n\nheight_in_cm\nPlayer’s height (in cm)\n\n\n\nsoccer_heights_sample.csv", + "crumbs": [ + "Home", + "Soccer", + "Soccer Heights" + ] }, { "objectID": "soccer/soccer-heights.html#questions", "href": "soccer/soccer-heights.html#questions", "title": "Soccer Heights", "section": "Questions", - "text": "Questions\n\nWhat are the mean heights for each position?\nTest to see if there is evidence of a discernible difference in mean height among the five positions.\nWhich pairs of positions appear to have a discernible difference in mean height?" + "text": "Questions\n\nWhat are the mean heights for each position?\nTest to see if there is evidence of a discernible difference in mean height among the five positions.\nWhich pairs of positions appear to have a discernible difference in mean height?", + "crumbs": [ + "Home", + "Soccer", + "Soccer Heights" + ] }, { "objectID": "soccer/soccer-heights.html#references", "href": "soccer/soccer-heights.html#references", "title": "Soccer Heights", "section": "References", - "text": "References\nThe data was subsetted from the data.world website" - }, - { - "objectID": "basketball/nba-player-stats.html", - "href": "basketball/nba-player-stats.html", - "title": "National Basketball Association Player Statistics", - "section": "", - "text": "The National Basketball Association (NBA) is the top men’s professional basketball league in the world. While players have predefined positions, the sport is becoming increasingly positionless - with centers attempting more three point shots and guards driving the ball inside to dunk. With this dataset, you can explore clustering NBA players based on various types of statistics and compare your players labels to the predefined positions." - }, - { - "objectID": "basketball/nba-player-stats.html#motivation", - "href": "basketball/nba-player-stats.html#motivation", - "title": "National Basketball Association Player Statistics", - "section": "", - "text": "The National Basketball Association (NBA) is the top men’s professional basketball league in the world. While players have predefined positions, the sport is becoming increasingly positionless - with centers attempting more three point shots and guards driving the ball inside to dunk. With this dataset, you can explore clustering NBA players based on various types of statistics and compare your players labels to the predefined positions." - }, - { - "objectID": "basketball/nba-player-stats.html#data", - "href": "basketball/nba-player-stats.html#data", - "title": "National Basketball Association Player Statistics", - "section": "Data", - "text": "Data\nThis dataset contains statistics about 812 player-team stints for during the 2021-2022 NBA regular season. For players that played for \\(T\\) teams during the season (due to trade), there are \\(T+1\\) rows with one row for their performance with each of the \\(T\\) teams and another row indicating their total performance (where tm = TOT) across the full season regardless of team. The counting stats are reported on a per 100 team possessions scale, to normalize for playing time differences.\nThe data was collected using the ballr package in R., which gathers data from basketball-reference.com.\n\nnba-player-stats.csv\n\n\nVariable\nDescription\n\n\n\n\nplayer\nName of player\n\n\npos\nPlayer’s designated position\n\n\nage\nPlayer’s age on February 1st of the season\n\n\ntm\nName of team\n\n\ng\nNumber of games\n\n\ngs\nNumber of games started\n\n\nmp\nNumber of minutes played\n\n\nfg\nField goals per 100 team possessions\n\n\nfga\nField goal attempts per 100 team possessions\n\n\nfgpercent\nField goal percentage\n\n\nx3p\n3 point field goals per 100 team possessions\n\n\nx3pa\n3 point field goal attempts per 100 team possessions\n\n\nx3ppercent\n3 point field goal percentage\n\n\nx2p\n2 point field goals per 100 team possessions\n\n\nx2pa\n2 point field goal attempts per 100 team possessions\n\n\nx2ppercent\n2 point field goal percentage\n\n\nft\nFree throws per 100 team possessions\n\n\nfta\nFree throw attempts per 100 team possessions\n\n\nftpercent\nFree throw percentage\n\n\norb\nOffensive rebounds per 100 team possessions\n\n\ndrb\nDefensive rebounds per 100 team possessions\n\n\ntrb\nTotal rebounds per 100 team possessions\n\n\nast\nAssists per 100 team possessions\n\n\nstl\nSteals per 100 team possessions\n\n\nblk\nBlocks per 100 team possessions\n\n\ntov\nTurnovers per 100 team possessions\n\n\npf\nPersonal fouls per 100 team possessions\n\n\npts\nPoints per 100 team possessions\n\n\nortg\nOffensive Rating - an estimate of points produced per 100 possessions scale\n\n\ndrtg\nDefensive Rating - an estimate of points allowed per 100 possessions scale" - }, - { - "objectID": "basketball/nba-player-stats.html#questions", - "href": "basketball/nba-player-stats.html#questions", - "title": "National Basketball Association Player Statistics", - "section": "Questions", - "text": "Questions\n\nApply a clustering technique using the player statistics to create new labels for the types of NBA players, and compare to their known position labels. Compare and contrast your data-defined roles to the traditional basketball positions.\nUsing principal component analysis, project the players into a lower dimensional space. Choose an appropriate number of principal components, and describe which statistics contribute to your selected number of components. Based on your results, which player statistics explain the most variation between players in the dataset? Which stats are independent of each other?" - }, - { - "objectID": "basketball/nba-player-stats.html#references", - "href": "basketball/nba-player-stats.html#references", - "title": "National Basketball Association Player Statistics", - "section": "References", - "text": "References\nElmore R (2020). ballr: Access to Current and Historical Basketball Data. R package version 0.2.6." + "text": "References\nThe data was subsetted from the data.world website", + "crumbs": [ + "Home", + "Soccer", + "Soccer Heights" + ] }, { - "objectID": "tennis/wta-grand-slam-matches.html", - "href": "tennis/wta-grand-slam-matches.html", - "title": "WTA Grand Slam Matches", + "objectID": "tennis/australian-open.html", + "href": "tennis/australian-open.html", + "title": "Australian Open Tennis (2008-2022)", "section": "", - "text": "The Women’s Tennis Associate (WTA) organizes the top women’s professional tennis tour in the world. Throughout the year, there are four major tournaments yielding the most ranking points, prize money, and fame. These are known as the Grand Slam tournaments, consisting of (in order): Australian Open, French Open (aka Roland Garros), Wimbledon, and the US Open. With this dataset of information about winners and losers in WTA Grand Slam matches from 2018 to 2022, you’ll be able to explore statistics collected during matches and information about the athletes to predict match outcomes." + "text": "The Australian Open is an annual tennis tournament in Melbourne, Australia. We have data from the men’s tournament in the years 2008-2022, with variables for both the winner and loser of each match, along with information such as what hand they play with and their Association of Tennis Professionals (ATP) rank and characteristics of the players. Which characteristics might help predict the outcome of the matches?", + "crumbs": [ + "Home", + "Tennis", + "Australian Open Tennis (2008-2022)" + ] }, { - "objectID": "tennis/wta-grand-slam-matches.html#motivation", - "href": "tennis/wta-grand-slam-matches.html#motivation", - "title": "WTA Grand Slam Matches", + "objectID": "tennis/australian-open.html#motivation", + "href": "tennis/australian-open.html#motivation", + "title": "Australian Open Tennis (2008-2022)", "section": "", - "text": "The Women’s Tennis Associate (WTA) organizes the top women’s professional tennis tour in the world. Throughout the year, there are four major tournaments yielding the most ranking points, prize money, and fame. These are known as the Grand Slam tournaments, consisting of (in order): Australian Open, French Open (aka Roland Garros), Wimbledon, and the US Open. With this dataset of information about winners and losers in WTA Grand Slam matches from 2018 to 2022, you’ll be able to explore statistics collected during matches and information about the athletes to predict match outcomes." + "text": "The Australian Open is an annual tennis tournament in Melbourne, Australia. We have data from the men’s tournament in the years 2008-2022, with variables for both the winner and loser of each match, along with information such as what hand they play with and their Association of Tennis Professionals (ATP) rank and characteristics of the players. Which characteristics might help predict the outcome of the matches?", + "crumbs": [ + "Home", + "Tennis", + "Australian Open Tennis (2008-2022)" + ] }, { - "objectID": "tennis/wta-grand-slam-matches.html#data", - "href": "tennis/wta-grand-slam-matches.html#data", - "title": "WTA Grand Slam Matches", + "objectID": "tennis/australian-open.html#data", + "href": "tennis/australian-open.html#data", + "title": "Australian Open Tennis (2008-2022)", "section": "Data", - "text": "Data\nThis dataset contains all WTA matches between 2018 and 2022, courtesy of Jeff Sackmann’s famous tennis repository.\nThere are 2,413 rows in this dataset where each row corresponds to a single WTA Grand Slam match. Each row has 38 columns with general information about the matches, as well as columns describing the winner and loser of the matches:\n\nwta-grand-slam-matches-2018to2022.csv\n\n\nVariable\nDescription\n\n\n\n\ntourney_name\nname of the Grand Slam Tournament (French Open is recorded as ROLAND GARROS)\n\n\nsurface\ntype of court surface\n\n\ntourney_date\neight digits, YYYYMMDD, usually the Monday of the tournament week\n\n\nwinner/loser_seed\nseed of winning/losing player\n\n\nwinner/loser_name\nName of the winning/losing player\n\n\nwinner/loser_hand\nR = right, L = left, U = unknown. For ambidextrous players, this is their serving hand\n\n\nwinner/loser_ht\nheight in centimeters, where available\n\n\nwinner/loser_ioc\nthree-character country code\n\n\nwinner/loser_age\nage, in years, as of the tourney_date\n\n\nscore\nfinal match score\n\n\nround\ntournament round\n\n\nminutes\nmatch length in minutes\n\n\nw/l_ace\nwinner/loser’s number of aces\n\n\nw/l_df\nwinner/loser’s number of doubles faults\n\n\nw/l_svpt\nwinner/loser’s number of serve points\n\n\nw/l_1stIn\nwinner/loser’s number of first serves made\n\n\nw/l_1stWon\nwinner/loser’s number of first-serve points won\n\n\nw/l_2ndWon\nwinner/loser’s number of second-serve points won\n\n\nw/l_SvGms\nwinner/loser’s number of serve games\n\n\nw/l_bpSaved\nwinner/loser’s number of break points saved\n\n\nw/l_bpFaced\nwinner/loser’s number of break points faced\n\n\nwinner/loser_rank\nwinner/loser’s WTA rank, as of the tourney_date, or the most recent ranking date before the tourney_date\n\n\n\nNote that a full glossary of the features available for match data can be found here." + "text": "Data\nEach row of data gives 19 variables of information for one of the 1905 Men’s Australian Open matches played between 2008 and 2022.\n\nAustralianOpen.csv\n\n\nVariable\nDescription\n\n\n\n\nYear\nYear (2008-2022)\n\n\nWinner\nwinner of the match\n\n\nLoser\nloser of the match\n\n\nWRank\nwinning player’s rank\n\n\nLRank\nlosing player’s rank\n\n\npl1_flag\nwinning player’s country they choose to represent\n\n\npl1_year_pro\nwinning player’s first year as a professional\n\n\npl1_weight\nwinning player’s weight in kilograms\n\n\npl1_height\nwinning player’s height in centimeters\n\n\npl1_hand\nwinning player’s handedness that they play with\n\n\npl2_flag\nlosing player’s country they choose to represent\n\n\npl2_year_pro\nlosing player’s first year as a professional\n\n\npl2_weight\nlosing player’s weight in kilograms\n\n\npl2_height\nlosing player’s height in centimeters\n\n\npl2_hand\nlosing player’s handedness that they play with\n\n\nhigher_rank_won\nDid the higher rank win? Yes orNo\n\n\nrank_diff\ndifference in rank between the players\n\n\nrank_diff_cat\ncategroies for difference in rank between the players: Low (30 or less), Medium (31-75), High (more than 75)\n\n\nsame_handedness\nDo the players have the same handedness? Yes or No\n\n\n\n: AustralianOpenLater.csv has the same variables (with the addition of a Round variable), but only for 105 matches from the quarterfinals, semifinals, and finals each year.", + "crumbs": [ + "Home", + "Tennis", + "Australian Open Tennis (2008-2022)" + ] }, { - "objectID": "tennis/wta-grand-slam-matches.html#questions", - "href": "tennis/wta-grand-slam-matches.html#questions", - "title": "WTA Grand Slam Matches", + "objectID": "tennis/australian-open.html#questions", + "href": "tennis/australian-open.html#questions", + "title": "Australian Open Tennis (2008-2022)", "section": "Questions", - "text": "Questions\n\nAfter performing the appropriate data wrangling, build a logistic regression model to predict whether or not the seed favorite wins the match based on the athlete information and recorded match statistics. Which variables, if any, are predictive of the match outcome? Describe their relationships.\nChoose an athlete of interest, e.g., Serena Williams. Create a new dataset where the columns describe the performance of that athlete in each particular match. Explore how the different match information and statistics correspond to whether or not the athlete is able to win the match.\nExplore the relationship between court surface and the various observed match statistics. This could be done by clustering matches based on the winner and loser statistics, and see how well aligned they are with the court surface type or Grand Slam event." + "text": "Questions\n\nWhat proportion of time does the higher (ATP) rank player win?\nDoes the proportion of higher ranked wins change much when the players play with opposite hands (i.e. a right-handed player vs. a left-handed payer)\nConstruct a two-way table of higher_rank_won vs. rank_diff_cat and see if teh two variables appear to be related.\nInvestigate a logistic regression model to predict it the higher ranked player wins using the difference in ranks as the predictor.\nAre the results for any of these questions much different if we consider only matches in the later rounds for the tournament (quarterfinals, semifinals, and finals) in AustralianOpenLater.csv?", + "crumbs": [ + "Home", + "Tennis", + "Australian Open Tennis (2008-2022)" + ] }, { - "objectID": "tennis/wta-grand-slam-matches.html#references", - "href": "tennis/wta-grand-slam-matches.html#references", - "title": "WTA Grand Slam Matches", + "objectID": "tennis/australian-open.html#references", + "href": "tennis/australian-open.html#references", + "title": "Australian Open Tennis (2008-2022)", "section": "References", - "text": "References\nData was accessed from Jeff Sackmann’s tennis GitHub repository." + "text": "References\nThe data are a subset of the data found at Kaggle.", + "crumbs": [ + "Home", + "Tennis", + "Australian Open Tennis (2008-2022)" + ] }, { "objectID": "submit-data.html", "href": "submit-data.html", "title": "Submit a Dataset", "section": "", - "text": "The goal of this repository is to provide a collection of relevant and engaging sports datasets to be used in class, handouts, assignments, projects, and SCORE modules. We are always looking for new datasets and welcome public submissions, but these datasets will need to meet the following requirements:" + "text": "The goal of this repository is to provide a collection of relevant and engaging sports datasets to be used in class, handouts, assignments, projects, and SCORE modules. We are always looking for new datasets and welcome public submissions, but these datasets will need to meet the following requirements:", + "crumbs": [ + "Home", + "Submit a Dataset" + ] }, { "objectID": "submit-data.html#two-ways-to-submit-a-dataset", "href": "submit-data.html#two-ways-to-submit-a-dataset", "title": "Submit a Dataset", "section": "Two ways to submit a dataset", - "text": "Two ways to submit a dataset\nThere are two ways to contribute a dataset:\n\nSubmit a dataset with the necessary information using this Google form. This is the recommended option for contributors who are not experienced with quarto or Rmarkdown files. The form will describe what information is necessary for us to create the appropriate page for your submitted dataset.\nFill out a quarto template file with the dataset information, and then submit either via email or directly with a GitHub pull request.\n\n\nThe rest of this page includes information about submitting a dataset using the quarto template file." + "text": "Two ways to submit a dataset\nThere are two ways to contribute a dataset:\n\nSubmit a dataset with the necessary information using this Google form. This is the recommended option for contributors who are not experienced with quarto or Rmarkdown files. The form will describe what information is necessary for us to create the appropriate page for your submitted dataset.\nFill out a quarto template file with the dataset information, and then submit either via email or directly with a GitHub pull request.\n\n\nThe rest of this page includes information about submitting a dataset using the quarto template file.", + "crumbs": [ + "Home", + "Submit a Dataset" + ] }, { "objectID": "submit-data.html#quarto-template-file-submission-steps", "href": "submit-data.html#quarto-template-file-submission-steps", "title": "Submit a Dataset", "section": "Quarto template file submission steps", - "text": "Quarto template file submission steps\nThis data repository is built using quarto and rendered into a website. You can get the template file in two ways:\n\nCopy the _dataset-template.qmd file from our GitHub repository and save it on your computer. Once you’re done, you can email us the file and the data (see below for email address).\nFork our GitHub repository into your own GitHub account and edit it like any other Git repository. Once you’re done, you can submit a pull request.\n\n\nDataset template\nThe dataset template is a Quarto file. The template asks for:\n\nBasic metadata to tag the dataset, such as the statistics & data science topic, a short title, and your name. (All submissions are credited with the name of the submitter.)\nA description of the sports question to motivate analysis with the dataset.\nA description of the dataset with documentation about each variable. The file should make it clear what a single row of the dataset represents, and clearly describe each of the variables (including units of measure when relevant).\nReferences to the original source of the data. If the source is a code package, then include an appropriate citation with a link to the package. If the source is an academic paper and the data is deposited in a third-party repository (e.g., Figshare), then include references to both the paper and the archived dataset. When available, include the DOIs of the references.\n\nThe template is meant to prevent a common problem with course datasets: they get passed down from instructor to instructor, and eventually all information about the original source is lost. The dataset may be presented to students without context or important details (like units), and the instructor may not be able to find the original data or references to answer questions about the data.\n\n\nSubmitting template file\nOnce you’ve filled in the template, you can either:\n\nEmail the template file, the data, and any code for preparing the data to the repository editor (currently Ron Yurko: ryurko AT stat DOT cmu DOT edu).\nSubmit a pull request to our repository on GitHub. Save the Qmd with a meaningful name and place it in its relevant sports directory (e.g., baseball/ or tennis/). Place the data files in data/. Place any code to prepare the data in the _prep/ folder, inside another folder with a name matching the submitted dataset. Once you’ve committed all of the necessary files (without anything extraneous) then you can submit a pull request." + "text": "Quarto template file submission steps\nThis data repository is built using quarto and rendered into a website. You can get the template file in two ways:\n\nCopy the _dataset-template.qmd file from our GitHub repository and save it on your computer. Once you’re done, you can email us the file and the data (see below for email address).\nFork our GitHub repository into your own GitHub account and edit it like any other Git repository. Once you’re done, you can submit a pull request.\n\n\nDataset template\nThe dataset template is a Quarto file. The template asks for:\n\nBasic metadata to tag the dataset, such as the statistics & data science topic, a short title, and your name. (All submissions are credited with the name of the submitter.)\nA description of the sports question to motivate analysis with the dataset.\nA description of the dataset with documentation about each variable. The file should make it clear what a single row of the dataset represents, and clearly describe each of the variables (including units of measure when relevant).\nReferences to the original source of the data. If the source is a code package, then include an appropriate citation with a link to the package. If the source is an academic paper and the data is deposited in a third-party repository (e.g., Figshare), then include references to both the paper and the archived dataset. When available, include the DOIs of the references.\n\nThe template is meant to prevent a common problem with course datasets: they get passed down from instructor to instructor, and eventually all information about the original source is lost. The dataset may be presented to students without context or important details (like units), and the instructor may not be able to find the original data or references to answer questions about the data.\n\n\nSubmitting template file\nOnce you’ve filled in the template, you can either:\n\nEmail the template file, the data, and any code for preparing the data to the repository editor (currently Ron Yurko: ryurko AT stat DOT cmu DOT edu).\nSubmit a pull request to our repository on GitHub. Save the Qmd with a meaningful name and place it in its relevant sports directory (e.g., baseball/ or tennis/). Place the data files in data/. Place any code to prepare the data in the _prep/ folder, inside another folder with a name matching the submitted dataset. Once you’ve committed all of the necessary files (without anything extraneous) then you can submit a pull request.", + "crumbs": [ + "Home", + "Submit a Dataset" + ] }, { - "objectID": "hockey/nhl_powerplay.html", - "href": "hockey/nhl_powerplay.html", - "title": "NHL Team Power Play Perfomance in 2022-2023", + "objectID": "wrestling/sumo_wrestingling_since_1957.html", + "href": "wrestling/sumo_wrestingling_since_1957.html", + "title": "Sumo wrestler characteristics", "section": "", - "text": "Certain infractions in a hockey game can lead to one or more players being forced to sit out for a period of time while their team has to skate with fewer payers. A common scenario that results is a 5 on 4 power play where one team has five skates (plus a goalie) and the penalized team has only four skaters (plus a goalie). The team with fewer skates is said to be shorthanded. Typical penalty times are for two minutes and the penalized player is allowed to return to the ice if the opponent scores a goal while on the power play. Rare, but more serious infractions, produce a major penalty (5 minutes) where the penalized player is not allowed to return to the ice when a goal is scored. A power play can also end before two minutes when the team on the power play commits a penalty which results in one of its players being sent off the ice to even the sides.\nThis dataset shows information for all 32 National Hockey League (NHL) teams based on their performance while on a 5 on 4 power play in the 2022-2023 regular season. Goals scored, shots on goals, and hits are all scaled to give a count of these events for every full two minutes of a power play." + "text": "The Banzuke is a ranking system used in Japanese sumo wrestling to determine the order and matchups for tournaments. In the Makuuchi Banzuke division, which is the top division, the history can be traced back to the 18th century. The Banzuke lists the wrestlers’ ranks, names, stables, and more in a hierarchical order based on their performance and records in previous tournaments. Over the years, the Makuuchi Banzuke has evolved, incorporating changes in the number of ranks and divisions. Today, it plays a crucial role in organizing and structuring sumo tournaments, reflecting the wrestlers’ skill, rank, and accomplishments in the sport.", + "crumbs": [ + "Home", + "Wrestling", + "Sumo wrestler characteristics" + ] }, { - "objectID": "hockey/nhl_powerplay.html#motivation", - "href": "hockey/nhl_powerplay.html#motivation", - "title": "NHL Team Power Play Perfomance in 2022-2023", + "objectID": "wrestling/sumo_wrestingling_since_1957.html#motivation", + "href": "wrestling/sumo_wrestingling_since_1957.html#motivation", + "title": "Sumo wrestler characteristics", "section": "", - "text": "Certain infractions in a hockey game can lead to one or more players being forced to sit out for a period of time while their team has to skate with fewer payers. A common scenario that results is a 5 on 4 power play where one team has five skates (plus a goalie) and the penalized team has only four skaters (plus a goalie). The team with fewer skates is said to be shorthanded. Typical penalty times are for two minutes and the penalized player is allowed to return to the ice if the opponent scores a goal while on the power play. Rare, but more serious infractions, produce a major penalty (5 minutes) where the penalized player is not allowed to return to the ice when a goal is scored. A power play can also end before two minutes when the team on the power play commits a penalty which results in one of its players being sent off the ice to even the sides.\nThis dataset shows information for all 32 National Hockey League (NHL) teams based on their performance while on a 5 on 4 power play in the 2022-2023 regular season. Goals scored, shots on goals, and hits are all scaled to give a count of these events for every full two minutes of a power play." + "text": "The Banzuke is a ranking system used in Japanese sumo wrestling to determine the order and matchups for tournaments. In the Makuuchi Banzuke division, which is the top division, the history can be traced back to the 18th century. The Banzuke lists the wrestlers’ ranks, names, stables, and more in a hierarchical order based on their performance and records in previous tournaments. Over the years, the Makuuchi Banzuke has evolved, incorporating changes in the number of ranks and divisions. Today, it plays a crucial role in organizing and structuring sumo tournaments, reflecting the wrestlers’ skill, rank, and accomplishments in the sport.", + "crumbs": [ + "Home", + "Wrestling", + "Sumo wrestler characteristics" + ] }, { - "objectID": "hockey/nhl_powerplay.html#data", - "href": "hockey/nhl_powerplay.html#data", - "title": "NHL Team Power Play Perfomance in 2022-2023", + "objectID": "wrestling/sumo_wrestingling_since_1957.html#data", + "href": "wrestling/sumo_wrestingling_since_1957.html#data", + "title": "Sumo wrestler characteristics", "section": "Data", - "text": "Data\nEach row of data gives power play statistics for one of the 32 National Hockey League teams from the 2022-2023 regular season.\n\nNHL_PowerPlay2022.csv\n\n\nVariable\nDescription\n\n\n\n\nteam\nName of the team\n\n\ngoals_per_2_min\nHow many goals scored in 2 minute increments\n\n\nSOG_per_2_min\nHow many shots on goal were taken in a 2 minute increments\n\n\nhits_per_2_min\nAverage hits recorded for 2 minutes\n\n\nConference\nConference the team plays in (east vs west)\n\n\ngoal_differential\nPower play goals scored minus short-handed goals allowed\n\n\nIcetime\nTotal time spent on the power play (in minutes)" + "text": "Data\nThe data set has 15760 rows with 10 columns. Each row represents an appearance of a sumo wrestler in the Makuuchi Banzuke, Japan’s most elite sumo wrestling division. The data dates from 1957 through mid 2023 and is comprised of multiple Bashos (tournaments) each year. An addition to the standard columns of wrestler names and records, the data contains many other metrics pertaining to each wrestlers birth place and physical stature.\n\nsumo_since_1957.csv\n\n\nVariable\nDescription\n\n\n\n\nRank\nRank of the wrestler in the Basho\n\n\nRikishi\nThe ring name of a the sumo wrestler\n\n\nHeya\nThe establishment where a wrestler trains\n\n\nShusshin\nThe birthplace, or place of origin of the wrestler\n\n\nBirth Date\nThe birth date of the wrestler\n\n\nheight_cm\nThe height of the wrestler at the time of the event in centimeters\n\n\nweight_kg\nThe weight of the wrestler at the time of the even in kilograms\n\n\nwins\nAmount of wins in the specific Basho\n\n\nlosses\nAmount of losses in the specific Basho\n\n\nties\nAmount of ties in the specific Basho", + "crumbs": [ + "Home", + "Wrestling", + "Sumo wrestler characteristics" + ] }, { - "objectID": "hockey/nhl_powerplay.html#questions", - "href": "hockey/nhl_powerplay.html#questions", - "title": "NHL Team Power Play Perfomance in 2022-2023", + "objectID": "wrestling/sumo_wrestingling_since_1957.html#questions", + "href": "wrestling/sumo_wrestingling_since_1957.html#questions", + "title": "Sumo wrestler characteristics", "section": "Questions", - "text": "Questions\n\nDescribe the distribution of goals-per-2min. Does any team appear to have unusually high or low values for this variable?\nCreate plot to look at the relationship between goals_per_2min and SOG_per_2min. Does it look like more shots tend to lead to more goals?\nCreate plot to look at the relationship between goals_per_2min and hits_per_2min. Does it look like more hits tend to lead to more goals?\nCreate a plot to compare the distribution of goal scoring rates while on the power play between the two conferences. Does there appear to be much difference between the two conferences?" + "text": "Questions\n\nWhich place of origin has resulted in the most sumo wrestlers?\nWhich wrestlers train at Kataonami?\nWhich wrestlers have the highest win percentage?\nWhich wrestlers have had the most successful careers?", + "crumbs": [ + "Home", + "Wrestling", + "Sumo wrestler characteristics" + ] }, { - "objectID": "hockey/nhl_powerplay.html#references", - "href": "hockey/nhl_powerplay.html#references", - "title": "NHL Team Power Play Perfomance in 2022-2023", + "objectID": "wrestling/sumo_wrestingling_since_1957.html#references", + "href": "wrestling/sumo_wrestingling_since_1957.html#references", + "title": "Sumo wrestler characteristics", "section": "References", - "text": "References\nThe data were obtained from the MoneyPuck website." + "text": "References\nSumo Reference - Alexander Nitschke https://sumodb.sumogames.de\nThe Sumo Matchup Centuries In The Making - FiveThirtyEight https://fivethirtyeight.com/features/the-sumo-matchup-centuries-in-the-making/", + "crumbs": [ + "Home", + "Wrestling", + "Sumo wrestler characteristics" + ] }, { - "objectID": "hockey/nhl-shots.html", - "href": "hockey/nhl-shots.html", - "title": "National Hockey League Shots", + "objectID": "lacrosse/lacrosse_pll_vs_nll.html", + "href": "lacrosse/lacrosse_pll_vs_nll.html", + "title": "Lacrosse PLL vs. NLL", "section": "", - "text": "The National Hockey League (NHL) is the top professional men’s hockey league in the world. The league records every shot players take along with contextual information about the shot such as its location, the player’s distance and angle to the goal when attempting the shot, as well as the outcome (blocked, missed, or goal). Using this information, the hockey analytics community have developed measures of shot quality known as expected goals. With this dataset, you can create your own expected goals model to predict the shot outcome given relevant features." + "text": "This data set examines the goals and shots in two prominent lacrosse leagues: the Premier Lacrosse League (PLL) and the National Lacrosse League (NLL).\nThe PLL and NLL are highly regarded professional lacrosse leagues that feature top-tier athletes from around the world. The PLL is played in an indoor, smaller field while the NLL plays on an outdoor field with traditional dimensions. This data set allows for an analysis of goal-scoring within these leagues.", + "crumbs": [ + "Home", + "Lacrosse", + "Lacrosse PLL vs. NLL" + ] }, { - "objectID": "hockey/nhl-shots.html#motivation", - "href": "hockey/nhl-shots.html#motivation", - "title": "National Hockey League Shots", + "objectID": "lacrosse/lacrosse_pll_vs_nll.html#motivation", + "href": "lacrosse/lacrosse_pll_vs_nll.html#motivation", + "title": "Lacrosse PLL vs. NLL", "section": "", - "text": "The National Hockey League (NHL) is the top professional men’s hockey league in the world. The league records every shot players take along with contextual information about the shot such as its location, the player’s distance and angle to the goal when attempting the shot, as well as the outcome (blocked, missed, or goal). Using this information, the hockey analytics community have developed measures of shot quality known as expected goals. With this dataset, you can create your own expected goals model to predict the shot outcome given relevant features." + "text": "This data set examines the goals and shots in two prominent lacrosse leagues: the Premier Lacrosse League (PLL) and the National Lacrosse League (NLL).\nThe PLL and NLL are highly regarded professional lacrosse leagues that feature top-tier athletes from around the world. The PLL is played in an indoor, smaller field while the NLL plays on an outdoor field with traditional dimensions. This data set allows for an analysis of goal-scoring within these leagues.", + "crumbs": [ + "Home", + "Lacrosse", + "Lacrosse PLL vs. NLL" + ] }, { - "objectID": "hockey/nhl-shots.html#data", - "href": "hockey/nhl-shots.html#data", - "title": "National Hockey League Shots", + "objectID": "lacrosse/lacrosse_pll_vs_nll.html#data", + "href": "lacrosse/lacrosse_pll_vs_nll.html#data", + "title": "Lacrosse PLL vs. NLL", "section": "Data", - "text": "Data\nThis dataset contains information about 160,573 shots during the 2021-2022 NHL season.\nThe data was collected using the hockeyR package in R.\n\nnhl-shots-2021.csv.gz\n\n\nVariable\nDescription\n\n\n\n\ngame_id\nUnique integer identifier for game shot took place in\n\n\ndescription\nString detailed description of shot event\n\n\nshot_outcome\nString denoting the outcome of the shot, either BLOCKED_SHOT (meaning blocked by a non-goalie), GOAL, MISSED_SHOT (shot that missed the net), or SHOT (shot on net that was saved by a goalie)\n\n\nperiod\nInteger value of the game period\n\n\nperiod_seconds_remaining\nNumeric value of the seconds remaining in the period\n\n\ngame_seconds_remaining\nNumeric value of the seconds remaining in the game; negative for overtime periods\n\n\nhome_score\nInteger value of the home team score after the event\n\n\naway_score\nInteger value of the away team score after the event\n\n\nhome_name\nString name of the home team\n\n\naway_name\nString name of the away team\n\n\nevent_team\nString defining the team taking the shot\n\n\nevent_goalie_name\nString name of goalie (if in net)\n\n\nempty_net\nBoolean indicating if the shot was during an empty net situation, TRUE if so but FALSE or NA if not\n\n\nevent_player_1_name\nString name of the primary event player\n\n\nevent_player_1_type\nString indicator for the role of event_player_1 (typically the shooter)\n\n\nevent_player_2_name\nString name of the secondary event player\n\n\nevent_player_2_type\nString indicator for the role of event_player_2 (blocker, assist, or goalie)\n\n\nstrength_code\nString indicator for game strength: EV (Even), SH (Shorthanded), or PP (Power Play)\n\n\nx_fixed\nNumeric transformed x-coordinate of event in feet, where the home team always shoots to the right, away team to the left\n\n\ny_fixed\nNumeric transformed y-coordinate of event in feet, where the home team always shoots to the right, away team to the left\n\n\nshot_distance\nNumeric distance (in feet) to center of net for unblocked shot events\n\n\nshot_angle\nNumeric angle (in degrees) to center of net for unlocked shot events" + "text": "Data\nThe data set has 162 rows with 9 columns. Each row represents a single lacrosse match either in the Premier Lacrosse League or the National Lacrosse League for the 2021 - 2022 season. NLL seasons typically run from December to May while PLL seasons typically run from June to September.\n\nlacrosse_pll_nll_2021-2022.csv\n\n\nVariable\nDescription\n\n\n\n\nLeague\nThe Premier Lacrosse league and the National Lacrosse League\n\n\nAway_team\nThe traveling team\n\n\nHome_team\nThe hosting team\n\n\nAway_shots\nHow many shots the Away_team had on net\n\n\nHome_shots\nHow many shots the Home_team had on net\n\n\nAway_goals\nHow many goals the Away_team had on net\n\n\nHome_goals\nHow many goals the Home_team had on net\n\n\ngoals_per_game\nThe total amount of goals scored each game\n\n\ngoals_per_48\nThe average amount of goals for the first 48 minutes of a game", + "crumbs": [ + "Home", + "Lacrosse", + "Lacrosse PLL vs. NLL" + ] }, { - "objectID": "hockey/nhl-shots.html#questions", - "href": "hockey/nhl-shots.html#questions", - "title": "National Hockey League Shots", + "objectID": "lacrosse/lacrosse_pll_vs_nll.html#questions", + "href": "lacrosse/lacrosse_pll_vs_nll.html#questions", + "title": "Lacrosse PLL vs. NLL", "section": "Questions", - "text": "Questions\n\nBuild a logistic regression model to predict whether or not the shot will result in a goal based on the shot distance and angle.\nBuild a classification model to predict the outcome based on the spatial x,y coordinates of the shot.\nCreate a visualization displaying the joint frequency of shot locations. Do there appear to be any clear modes of frequently taken shots? Create a conditional version of this display by shot outcome. Does the distribution shape vary by shot outcome? (You can also perform a similar analysis by team)." + "text": "Questions\n\nDo these data provide evidence that, on average, goals scored in the PLL and NLL differ? Include all details of the appropriate hypothesis test.\nConstruct and interpret a 95% confidence interval in average goals for NLL and PLL lacrosse games per 48 minutes.", + "crumbs": [ + "Home", + "Lacrosse", + "Lacrosse PLL vs. NLL" + ] }, { - "objectID": "hockey/nhl-shots.html#references", - "href": "hockey/nhl-shots.html#references", - "title": "National Hockey League Shots", + "objectID": "lacrosse/lacrosse_pll_vs_nll.html#references", + "href": "lacrosse/lacrosse_pll_vs_nll.html#references", + "title": "Lacrosse PLL vs. NLL", "section": "References", - "text": "References\nMorse D (2023). hockeyR: Collect and Clean Hockey Stats. R package version 1.3.1, https://github.com/danmorse314/hockeyR." - }, - { - "objectID": "by-statsds-topic.html", - "href": "by-statsds-topic.html", - "title": "Datasets By Topic", - "section": "", - "text": "2023 Boston Marathon runners\n\n\n\n\n\n\n\nSummary statistics\n\n\nOutliers\n\n\nz-scores\n\n\n\n\nThe data set looks at the 2023 Boston Marathon results.\n\n\n\n\n\n\nJun 27, 2023\n\n\nJack Fay, A.J. Dykstra, and Ivan Ramler\n\n\n\n\n\n\n \n\n\n\n\nAustralian Open Tennis (2008-2022)\n\n\n\n\n\n\n\nproportions\n\n\ntwo-way tables\n\n\n\n\nResults from matches at the Men’s Australian Open Tennis Tournament between 2008 and 2022.\n\n\n\n\n\n\nAug 3, 2023\n\n\nTrent Meyer, Robin Lock\n\n\n\n\n\n\n \n\n\n\n\nBatting Statistics for Division I Softball\n\n\n\n\n\n\n\nmerging\n\n\njoining\n\n\ndata wrangling\n\n\n\n\nThese data sets contain batting statistics for Division I softball in the 2021 and 2022 seasons.\n\n\n\n\n\n\nMay 23, 2023\n\n\nSarah Weaver and Ivan Ramler\n\n\n\n\n\n\n \n\n\n\n\nJustin Verlander Pitches\n\n\n\n\n\n\n\ncategorical data\n\n\nchi-squared test\n\n\n\n\nInformation about pitches thrown by Justin Verlander in the 2019 and 2022 MLB seasons\n\n\n\n\n\n\nMar 18, 2023\n\n\nRon Yurko\n\n\n\n\n\n\n \n\n\n\n\nLacrosse PLL vs. NLL\n\n\n\n\n\n\n\nDifference in means hypothesis tests\n\n\nDifference in means confidence intervals\n\n\n\n\nThe data set looks at both lacrosse leagues, their respective teams, and their shot and goal amounts.\n\n\n\n\n\n\nJun 22, 2023\n\n\nAJ Dykstra, Jack Cowan, and Ivan Ramler\n\n\n\n\n\n\n \n\n\n\n\nLake Placid Women’s Ironman Triathlon\n\n\n\n\n\n\n\nHistograms\n\n\nScatter Plots\n\n\nCorrelation\n\n\nLinear Regression\n\n\n\n\nThe data set looks at female participatants in the triathlon Ironman.\n\n\n\n\n\n\nJun 17, 2023\n\n\nMichael Schuckers, Matthew Abel, and A.J. Dykstra\n\n\n\n\n\n\n \n\n\n\n\nLeague of Legends - Buffing and Nerfing\n\n\n\n\n\n\n\noutlier detection\n\n\nfive number summary\n\n\nz-scores\n\n\n\n\nGame play statistics for League of Legends champions in two different patches. Can detecting outliers indicate champions that should be enhanced (buffed) or scaled back (nerfed)?\n\n\n\n\n\n\nJul 1, 2023\n\n\nGeorge Charalambous, Ivan Ramler, and A.J. Dykstra\n\n\n\n\n\n\n \n\n\n\n\nNASCAR Data Description\n\n\n\n\n\n\n\ndata visualization\n\n\ndata wrangling\n\n\nlinear regression\n\n\ntransformations in regression\n\n\nmodel assumptions\n\n\nmodel building\n\n\n\n\nNASCAR Driver Ratings and statistics from 2007-2022\n\n\n\n\n\n\nJul 6, 2023\n\n\nAlyssa Bigness, Jack Fay, and Ivan Ramler\n\n\n\n\n\n\n \n\n\n\n\nNFL Draft Combine\n\n\n\n\n\n\n\nExploratory Data Analysis\n\n\nLinear regression\n\n\n\n\nNFL Scouting Combine Event scores from 2004 - 2023\n\n\n\n\n\n\nAug 3, 2023\n\n\nShane Hauk, Michael Schuckers, Robin Lock\n\n\n\n\n\n\n \n\n\n\n\nNHL Team Power Play Perfomance in 2022-2023\n\n\n\n\n\n\n\nexploratory data Analysis\n\n\n\n\nMeasures for National Hockey League teams from all 5 on 4 power plays in the 2022-2023 season.\n\n\n\n\n\n\nJul 27, 2023\n\n\nAbby Hustler, Robin Lock\n\n\n\n\n\n\n \n\n\n\n\nNational Basketball Association Player Statistics\n\n\n\n\n\n\n\nclustering\n\n\nprincipal component analysis\n\n\n\n\nNBA player statistics from the 2021-2022 regular season\n\n\n\n\n\n\nMar 25, 2023\n\n\nRon Yurko\n\n\n\n\n\n\n \n\n\n\n\nNational Football League Team Statistics\n\n\n\n\n\n\n\nlinear regression\n\n\nprincipal component analysis\n\n\nmissing data\n\n\nvariable selection\n\n\nregularization\n\n\n\n\nNFL team statistics from 1999 to 2022\n\n\n\n\n\n\nMay 24, 2023\n\n\nRon Yurko\n\n\n\n\n\n\n \n\n\n\n\nNational Hockey League Shots\n\n\n\n\n\n\n\nclassification\n\n\nlogistic regression\n\n\ngeneralized additive models\n\n\nmultinomial logistic regression\n\n\nnaive bayes classifier\n\n\ndensity estimation\n\n\n\n\nInformation about shots during the 2021-2022 NHL season\n\n\n\n\n\n\nMar 23, 2023\n\n\nRon Yurko\n\n\n\n\n\n\n \n\n\n\n\nNational Women’s Soccer League Team Statistics\n\n\n\n\n\n\n\nlinear regression\n\n\nprincipal component analysis\n\n\n\n\nNWSL team statistics from 2016 to 2022\n\n\n\n\n\n\nMar 18, 2023\n\n\nRon Yurko\n\n\n\n\n\n\n \n\n\n\n\nPremier Hockey Federation Shots\n\n\n\n\n\n\n\ncategorical data\n\n\nchi-squared test\n\n\nlogistic regression\n\n\n\n\nInformation about shots during the 2021-2022 PHF season\n\n\n\n\n\n\nMar 23, 2023\n\n\nRon Yurko\n\n\n\n\n\n\n \n\n\n\n\nSoccer Heights\n\n\n\n\n\n\n\nANOVA for means\n\n\nPairwise comparisons\n\n\n\n\nHeights for a sample of 100 soccer players by position\n\n\n\n\n\n\nAug 3, 2023\n\n\nRoderick Mpofu, Robin Lock\n\n\n\n\n\n\n \n\n\n\n\nStatistics of PGA Tournaments During the 2022 Season\n\n\n\n\n\n\n\ncorrelation\n\n\nlinear regression\n\n\n\n\nMeasures of Putting, Driving, and Success for individual golfers in 19 professional tournaments in 2022\n\n\n\n\n\n\nMay 19, 2023\n\n\nAlysssa Bigness\n\n\n\n\n\n\n \n\n\n\n\nSumo wrestler characteristics\n\n\n\n\n\n\n\ndata wrangling\n\n\ndata visualization\n\n\n\n\nSumo wrestling data 1957-2023.\n\n\n\n\n\n\nJun 29, 2023\n\n\nJack Fay, A.J. Dykstra, and Ivan Ramler\n\n\n\n\n\n\n \n\n\n\n\nTeam Statistics for Division I Women’s Volleyball\n\n\n\n\n\n\n\nmulticollinearity\n\n\nmultiple linear regression\n\n\ncorrelations\n\n\nsummary statistics\n\n\n\n\nThese data sets contain team statistics for Division I women’s volleyball in the 2022 and 2023 seasons.\n\n\n\n\n\n\nJul 24, 2023\n\n\nJack Fay, A.J. Dykstra, and Ivan Ramler\n\n\n\n\n\n\n \n\n\n\n\nTeam statistics for NCAA Division 1 Men’s and Women’s Lacrosse\n\n\n\n\n\n\n\ncorrelation\n\n\nindicator variables\n\n\nlinear regression\n\n\nside-by-side boxplots\n\n\n\n\nThese data sets contain team statistics for NCAA Division I women’s and men’s lacrosse in the 2022 and 2023 seasons.\n\n\n\n\n\n\nJul 24, 2023\n\n\nJack Fay, A.J. Dykstra, and Ivan Ramler\n\n\n\n\n\n\n \n\n\n\n\nWTA Grand Slam Matches\n\n\n\n\n\n\n\nlogistic regression\n\n\ndata wrangling\n\n\ncategorical data\n\n\nchi-squared test\n\n\nclustering\n\n\n\n\nWTA Grand Slam Matches from 2018 to 2022\n\n\n\n\n\n\nMay 24, 2023\n\n\nRon Yurko\n\n\n\n\n\n\n \n\n\n\n\nWomen’s National Basketball Association Shots\n\n\n\n\n\n\n\nclassification\n\n\nlogistic regression\n\n\ngeneralized additive models\n\n\nmultinomial logistic regression\n\n\nnaive bayes classifier\n\n\ndensity estimation\n\n\n\n\nInformation about shots during the 2021-2022 WNBA season\n\n\n\n\n\n\nMar 25, 2023\n\n\nRon Yurko\n\n\n\n\n\n\nNo matching items" + "text": "References\nPremier Lacrosse League stats. Premier Lacrosse League Stats. (n.d.). https://stats.premierlacrosseleague.com/\nPlayer stats. NLL. (2023, January 26). https://www.nll.com/stats/all-player-stats/", + "crumbs": [ + "Home", + "Lacrosse", + "Lacrosse PLL vs. NLL" + ] } ] \ No newline at end of file diff --git a/site_libs/bootstrap/bootstrap-icons.css b/site_libs/bootstrap/bootstrap-icons.css index 94f1940..285e444 100644 --- a/site_libs/bootstrap/bootstrap-icons.css +++ b/site_libs/bootstrap/bootstrap-icons.css @@ -1,8 +1,14 @@ +/*! + * Bootstrap Icons v1.11.1 (https://icons.getbootstrap.com/) + * Copyright 2019-2023 The Bootstrap Authors + * Licensed under MIT (https://github.com/twbs/icons/blob/main/LICENSE) + */ + @font-face { font-display: block; font-family: "bootstrap-icons"; src: -url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff"); +url("./bootstrap-icons.woff?2820a3852bdb9a5832199cc61cec4e65") format("woff"); } .bi::before, @@ -441,7 +447,6 @@ url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff"); .bi-cloud-fog2::before { content: "\f2a2"; } 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.bi-8-square-fill::before { content: "\f7c2"; } .bi-8-square::before { content: "\f7c3"; } -.bi-9-circle-1::before { content: "\f7c4"; } -.bi-9-circle-fill-1::before { content: "\f7c5"; } .bi-9-circle-fill::before { content: "\f7c6"; } .bi-9-circle::before { content: "\f7c7"; } .bi-9-square-fill::before { content: "\f7c8"; } @@ -1771,8 +1744,6 @@ url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff"); .bi-browser-edge::before { content: "\f7d5"; } .bi-browser-firefox::before { content: "\f7d6"; } .bi-browser-safari::before { content: "\f7d7"; } -.bi-c-circle-1::before { content: "\f7d8"; } -.bi-c-circle-fill-1::before { content: "\f7d9"; } .bi-c-circle-fill::before { content: "\f7da"; } .bi-c-circle::before { content: "\f7db"; } .bi-c-square-fill::before { content: "\f7dc"; } @@ -1783,8 +1754,6 @@ url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff"); .bi-car-front::before { content: "\f7e1"; } .bi-cassette-fill::before { content: "\f7e2"; } .bi-cassette::before { content: "\f7e3"; } -.bi-cc-circle-1::before { content: "\f7e4"; } -.bi-cc-circle-fill-1::before { content: "\f7e5"; } .bi-cc-circle-fill::before { content: "\f7e6"; } .bi-cc-circle::before { content: "\f7e7"; } .bi-cc-square-fill::before { content: "\f7e8"; } @@ -1803,8 +1772,6 @@ url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff"); .bi-filetype-sql::before { content: "\f7f5"; } .bi-fire::before { content: "\f7f6"; } .bi-google-play::before { content: "\f7f7"; } -.bi-h-circle-1::before { content: "\f7f8"; } -.bi-h-circle-fill-1::before { content: "\f7f9"; } .bi-h-circle-fill::before { content: "\f7fa"; } .bi-h-circle::before { content: "\f7fb"; } .bi-h-square-fill::before { content: "\f7fc"; } @@ -1813,8 +1780,6 @@ url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff"); .bi-lungs-fill::before { content: "\f7ff"; } .bi-lungs::before { content: "\f800"; } .bi-microsoft-teams::before { content: "\f801"; } -.bi-p-circle-1::before { content: "\f802"; } -.bi-p-circle-fill-1::before { content: "\f803"; } .bi-p-circle-fill::before { content: "\f804"; } .bi-p-circle::before { content: "\f805"; } .bi-p-square-fill::before { content: "\f806"; } @@ -1823,8 +1788,6 @@ url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff"); .bi-pass::before { content: "\f809"; } .bi-prescription::before { content: "\f80a"; } .bi-prescription2::before { content: "\f80b"; } -.bi-r-circle-1::before { content: "\f80c"; } -.bi-r-circle-fill-1::before { content: "\f80d"; } .bi-r-circle-fill::before { content: "\f80e"; } .bi-r-circle::before { content: "\f80f"; } .bi-r-square-fill::before { content: "\f810"; } @@ -2016,3 +1979,100 @@ url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff"); .bi-sina-weibo::before { content: "\f8ca"; } .bi-tencent-qq::before { content: "\f8cb"; } .bi-wikipedia::before { content: "\f8cc"; } +.bi-alphabet-uppercase::before { content: "\f2a5"; } +.bi-alphabet::before { content: "\f68a"; } +.bi-amazon::before { content: "\f68d"; } +.bi-arrows-collapse-vertical::before { content: "\f690"; } +.bi-arrows-expand-vertical::before { content: "\f695"; } +.bi-arrows-vertical::before { content: "\f698"; } +.bi-arrows::before { content: "\f6a2"; } +.bi-ban-fill::before { content: "\f6a3"; } +.bi-ban::before { content: "\f6b6"; } +.bi-bing::before { content: "\f6c2"; } +.bi-cake::before { content: "\f6e0"; } +.bi-cake2::before { content: "\f6ed"; } +.bi-cookie::before { content: "\f6ee"; } +.bi-copy::before { content: "\f759"; } +.bi-crosshair::before { content: "\f769"; } +.bi-crosshair2::before { content: "\f794"; } +.bi-emoji-astonished-fill::before { content: "\f795"; } +.bi-emoji-astonished::before { content: "\f79a"; } +.bi-emoji-grimace-fill::before { content: "\f79b"; } +.bi-emoji-grimace::before { content: "\f7a0"; } +.bi-emoji-grin-fill::before { content: "\f7a1"; } +.bi-emoji-grin::before { content: "\f7a6"; } +.bi-emoji-surprise-fill::before { content: "\f7a7"; } +.bi-emoji-surprise::before { content: "\f7ac"; } +.bi-emoji-tear-fill::before { content: "\f7ad"; } +.bi-emoji-tear::before { content: "\f7b2"; } +.bi-envelope-arrow-down-fill::before { content: "\f7b3"; } +.bi-envelope-arrow-down::before { content: "\f7b8"; } +.bi-envelope-arrow-up-fill::before { content: "\f7b9"; } +.bi-envelope-arrow-up::before { content: "\f7be"; } +.bi-feather::before { content: "\f7bf"; } +.bi-feather2::before { content: "\f7c4"; } +.bi-floppy-fill::before { content: "\f7c5"; } +.bi-floppy::before { content: "\f7d8"; } +.bi-floppy2-fill::before { content: "\f7d9"; } +.bi-floppy2::before { content: "\f7e4"; } +.bi-gitlab::before { content: "\f7e5"; } +.bi-highlighter::before { content: "\f7f8"; } +.bi-marker-tip::before { content: "\f802"; } +.bi-nvme-fill::before { content: "\f803"; } +.bi-nvme::before { content: "\f80c"; } +.bi-opencollective::before { content: "\f80d"; } +.bi-pci-card-network::before { content: "\f8cd"; } +.bi-pci-card-sound::before { content: "\f8ce"; } +.bi-radar::before { content: "\f8cf"; } +.bi-send-arrow-down-fill::before { content: "\f8d0"; } +.bi-send-arrow-down::before { content: "\f8d1"; } +.bi-send-arrow-up-fill::before { content: "\f8d2"; } +.bi-send-arrow-up::before { content: "\f8d3"; } +.bi-sim-slash-fill::before { content: "\f8d4"; } +.bi-sim-slash::before { content: "\f8d5"; } +.bi-sourceforge::before { content: "\f8d6"; } +.bi-substack::before { content: "\f8d7"; } +.bi-threads-fill::before { content: "\f8d8"; } +.bi-threads::before { content: "\f8d9"; } +.bi-transparency::before { content: "\f8da"; } +.bi-twitter-x::before { content: "\f8db"; } +.bi-type-h4::before { content: "\f8dc"; } +.bi-type-h5::before { content: "\f8dd"; } +.bi-type-h6::before { content: "\f8de"; } +.bi-backpack-fill::before { content: "\f8df"; } +.bi-backpack::before { content: "\f8e0"; } +.bi-backpack2-fill::before { content: "\f8e1"; } +.bi-backpack2::before { content: "\f8e2"; } +.bi-backpack3-fill::before { content: "\f8e3"; } +.bi-backpack3::before { content: "\f8e4"; } +.bi-backpack4-fill::before { content: "\f8e5"; } +.bi-backpack4::before { content: "\f8e6"; } +.bi-brilliance::before { content: "\f8e7"; } +.bi-cake-fill::before { content: "\f8e8"; } +.bi-cake2-fill::before { content: "\f8e9"; } +.bi-duffle-fill::before { content: "\f8ea"; } +.bi-duffle::before { content: "\f8eb"; } +.bi-exposure::before { content: "\f8ec"; } +.bi-gender-neuter::before { content: "\f8ed"; } +.bi-highlights::before { content: "\f8ee"; } +.bi-luggage-fill::before { content: "\f8ef"; } +.bi-luggage::before { content: "\f8f0"; } +.bi-mailbox-flag::before { content: "\f8f1"; } +.bi-mailbox2-flag::before { content: "\f8f2"; } +.bi-noise-reduction::before { content: "\f8f3"; } +.bi-passport-fill::before { content: "\f8f4"; } +.bi-passport::before { content: "\f8f5"; } +.bi-person-arms-up::before { content: "\f8f6"; } +.bi-person-raised-hand::before { content: "\f8f7"; } +.bi-person-standing-dress::before { content: "\f8f8"; } +.bi-person-standing::before { content: "\f8f9"; } +.bi-person-walking::before { content: "\f8fa"; } +.bi-person-wheelchair::before { content: "\f8fb"; } +.bi-shadows::before { content: "\f8fc"; } +.bi-suitcase-fill::before { content: "\f8fd"; } +.bi-suitcase-lg-fill::before { content: "\f8fe"; } +.bi-suitcase-lg::before { content: "\f8ff"; } +.bi-suitcase::before { content: "\f900"; } +.bi-suitcase2-fill::before { content: "\f901"; } +.bi-suitcase2::before { content: "\f902"; } +.bi-vignette::before { content: "\f903"; } diff --git a/site_libs/bootstrap/bootstrap-icons.woff b/site_libs/bootstrap/bootstrap-icons.woff index 18d21d4..dbeeb05 100644 Binary files a/site_libs/bootstrap/bootstrap-icons.woff and b/site_libs/bootstrap/bootstrap-icons.woff differ diff --git a/site_libs/bootstrap/bootstrap.min.css b/site_libs/bootstrap/bootstrap.min.css index e69d2f2..105956a 100644 --- a/site_libs/bootstrap/bootstrap.min.css +++ b/site_libs/bootstrap/bootstrap.min.css @@ -1,10 +1,12 @@ /*! - * Bootstrap v5.1.3 (https://getbootstrap.com/) - * Copyright 2011-2021 The Bootstrap Authors - * Copyright 2011-2021 Twitter, Inc. + * Bootstrap v5.3.1 (https://getbootstrap.com/) + * Copyright 2011-2023 The Bootstrap Authors * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) - */@import"https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@300;400;700&display=swap";:root{--bs-blue: #2780e3;--bs-indigo: #6610f2;--bs-purple: #613d7c;--bs-pink: #e83e8c;--bs-red: #ff0039;--bs-orange: #f0ad4e;--bs-yellow: #ff7518;--bs-green: #3fb618;--bs-teal: #20c997;--bs-cyan: #9954bb;--bs-white: #fff;--bs-gray: #6c757d;--bs-gray-dark: #373a3c;--bs-gray-100: #f8f9fa;--bs-gray-200: #e9ecef;--bs-gray-300: #dee2e6;--bs-gray-400: #ced4da;--bs-gray-500: #adb5bd;--bs-gray-600: #6c757d;--bs-gray-700: #495057;--bs-gray-800: #373a3c;--bs-gray-900: #212529;--bs-default: #373a3c;--bs-primary: #6D6E71;--bs-secondary: #373a3c;--bs-success: #3fb618;--bs-info: #9954bb;--bs-warning: #ff7518;--bs-danger: #ff0039;--bs-light: #f8f9fa;--bs-dark: #373a3c;--bs-default-rgb: 55, 58, 60;--bs-primary-rgb: 109, 110, 113;--bs-secondary-rgb: 55, 58, 60;--bs-success-rgb: 63, 182, 24;--bs-info-rgb: 153, 84, 187;--bs-warning-rgb: 255, 117, 24;--bs-danger-rgb: 255, 0, 57;--bs-light-rgb: 248, 249, 250;--bs-dark-rgb: 55, 58, 60;--bs-white-rgb: 255, 255, 255;--bs-black-rgb: 0, 0, 0;--bs-body-color-rgb: 55, 58, 60;--bs-body-bg-rgb: 255, 255, 255;--bs-font-sans-serif: "Source Sans Pro", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--bs-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;--bs-gradient: linear-gradient(180deg, rgba(255, 255, 255, 0.15), rgba(255, 255, 255, 0));--bs-root-font-size: 17px;--bs-body-font-family: var(--bs-font-sans-serif);--bs-body-font-size: 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.dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;-webkit-flex-direction:column;align-items:flex-start;-webkit-align-items:flex-start;justify-content:center;-webkit-justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(1px*-1)}.nav{--bs-nav-link-padding-x: 1rem;--bs-nav-link-padding-y: 0.5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: #6D6E71;--bs-nav-link-hover-color: #57585a;--bs-nav-link-disabled-color: rgba(52, 58, 64, 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #343a40;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #343a40;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23343a40'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%232c2c2d'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #b6b7b8;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(109, 110, 113, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #2c2c2d;--bs-accordion-active-bg: #e2e2e3}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23a7a8aa'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23a7a8aa'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #6D6E71;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #57585a;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #57585a;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(109, 110, 113, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #6D6E71;--bs-pagination-active-border-color: #6D6E71;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #6D6E71;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #6D6E71;--bs-list-group-active-border-color: #6D6E71;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(109, 110, 113, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(52, 58, 64, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color)}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #fff;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:"Source Sans Pro",-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI 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#fff);--bslib-value-box-border-color-default: var(--bs-card-border-color, rgba(0, 0, 0, 0.175));color:var(--bslib-value-box-color);background-color:var(--bslib-value-box-bg, var(--bslib-value-box-bg-default));border-color:var(--bslib-value-box-border-color, var(--bslib-value-box-border-color-default))}.bslib-value-box .value-box-grid{display:grid;grid-template-areas:"left right";align-items:center;overflow:hidden}.bslib-value-box .value-box-showcase{height:100%;max-height:var(---bslib-value-box-showcase-max-h, 100%)}.bslib-value-box .value-box-showcase,.bslib-value-box .value-box-showcase>.html-fill-item{width:100%}.bslib-value-box[data-full-screen=true] .value-box-showcase{max-height:var(---bslib-value-box-showcase-max-h-fs, 100%)}@media screen and (min-width: 575.98px){@container bslib-value-box (max-width: 300px){.bslib-value-box:not(.showcase-bottom) .value-box-grid{grid-template-columns:1fr !important;grid-template-rows:auto auto;grid-template-areas:"top" 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this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" ").forEach((t=>{if("click"===t)j.on(this._element,this.constructor.Event.CLICK,this._config.selector,(t=>this.toggle(t)));else if("manual"!==t){const e=t===hn?this.constructor.Event.MOUSEENTER:this.constructor.Event.FOCUSIN,i=t===hn?this.constructor.Event.MOUSELEAVE:this.constructor.Event.FOCUSOUT;j.on(this._element,e,this._config.selector,(t=>this._enter(t))),j.on(this._element,i,this._config.selector,(t=>this._leave(t)))}})),this._hideModalHandler=()=>{this._element&&this.hide()},j.on(this._element.closest(ln),cn,this._hideModalHandler),this._config.selector?this._config={...this._config,trigger:"manual",selector:""}:this._fixTitle()}_fixTitle(){const t=this._element.getAttribute("title"),e=typeof this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach((t=>{Gi.has(t)&&delete e[t]})),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),a(Qi,t,this.constructor.DefaultType),t.sanitize&&(t.template=Yi(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new 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H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(us);const fs=".bs.scrollspy",ps=`activate${fs}`,ms=`click${fs}`,gs=`load${fs}.data-api`,_s="active",bs="[href]",vs=".nav-link",ys=`${vs}, .nav-item > ${vs}, .list-group-item`,ws={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},As={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Es extends W{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return ws}static get DefaultType(){return As}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=r(t.target)||document.body,t.rootMargin=t.offset?`${t.offset}px 0px -30%`:t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map((t=>Number.parseFloat(t)))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(N.off(this._config.target,ms),N.on(this._config.target,ms,bs,(t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=z.find(bs,this._config.target);for(const e of t){if(!e.hash||l(e))continue;const t=z.findOne(decodeURI(e.hash),this._element);a(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),N.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))z.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of z.parents(t,".nav, .list-group"))for(const t of z.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=z.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=Es.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No 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e=this._getActiveElem(),i=e?N.trigger(e,Cs,{relatedTarget:t}):null;N.trigger(t,xs,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Fs),this._activate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),N.trigger(t,ks,{relatedTarget:e})):t.classList.add(Ws)}),t,t.classList.contains(Hs)))}_deactivate(t,e){t&&(t.classList.remove(Fs),t.blur(),this._deactivate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),N.trigger(t,Os,{relatedTarget:e})):t.classList.remove(Ws)}),t,t.classList.contains(Hs)))}_keydown(t){if(![$s,Is,Ns,Ps,Ms,js].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!l(t)));let 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this._element.classList.contains(io)}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){N.on(this._element,Qs,(t=>this._onInteraction(t,!0))),N.on(this._element,Xs,(t=>this._onInteraction(t,!1))),N.on(this._element,Ys,(t=>this._onInteraction(t,!0))),N.on(this._element,Us,(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=ro.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return 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t=e.state,n=e.name,r=t.rects.reference,o=t.rects.popper,i=t.modifiersData.preventOverflow,a=J(t,{elementContext:"reference"}),s=J(t,{altBoundary:!0}),f=ve(a,r),c=ve(s,o,i),p=ye(f),u=ye(c);t.modifiersData[n]={referenceClippingOffsets:f,popperEscapeOffsets:c,isReferenceHidden:p,hasPopperEscaped:u},t.attributes.popper=Object.assign({},t.attributes.popper,{"data-popper-reference-hidden":p,"data-popper-escaped":u})}},be=Z({defaultModifiers:[ee,te,oe,ie]}),xe=[ee,te,oe,ie,ae,le,he,me,ge],we=Z({defaultModifiers:xe});e.applyStyles=ie,e.arrow=me,e.computeStyles=oe,e.createPopper=we,e.createPopperLite=be,e.defaultModifiers=xe,e.detectOverflow=J,e.eventListeners=ee,e.flip=le,e.hide=ge,e.offset=ae,e.popperGenerator=Z,e.popperOffsets=te,e.preventOverflow=he,Object.defineProperty(e,"__esModule",{value:!0})})); diff --git a/site_libs/quarto-html/quarto.js b/site_libs/quarto-html/quarto.js index c3935c7..3ebd49c 100644 --- a/site_libs/quarto-html/quarto.js +++ b/site_libs/quarto-html/quarto.js @@ -9,7 +9,7 @@ const layoutMarginEls = () => { // Find any conflicting margin elements and add margins to the // top to prevent overlap const marginChildren = window.document.querySelectorAll( - ".column-margin.column-container > * " + ".column-margin.column-container > *, .margin-caption, .aside" ); let lastBottom = 0; @@ -18,25 +18,14 @@ const layoutMarginEls = () => { // clear the top margin so we recompute it marginChild.style.marginTop = null; const top = marginChild.getBoundingClientRect().top + window.scrollY; - console.log({ - childtop: marginChild.getBoundingClientRect().top, - scroll: window.scrollY, - top, - lastBottom, - }); if (top < lastBottom) { - const margin = lastBottom - top; + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; marginChild.style.marginTop = `${margin}px`; } const styles = window.getComputedStyle(marginChild); const marginTop = parseFloat(styles["marginTop"]); - - console.log({ - top, - height: marginChild.getBoundingClientRect().height, - marginTop, - total: top + marginChild.getBoundingClientRect().height + marginTop, - }); lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; } } @@ -46,7 +35,15 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { // Recompute the position of margin elements anytime the body size changes if (window.ResizeObserver) { const resizeObserver = new window.ResizeObserver( - throttle(layoutMarginEls, 50) + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) ); resizeObserver.observe(window.document.body); } diff --git a/site_libs/quarto-nav/quarto-nav.js b/site_libs/quarto-nav/quarto-nav.js index 3b21201..ebfc262 100644 --- a/site_libs/quarto-nav/quarto-nav.js +++ b/site_libs/quarto-nav/quarto-nav.js @@ -85,6 +85,17 @@ window.document.addEventListener("DOMContentLoaded", function () { } } + function dashboardOffset() { + const dashboardNavEl = window.document.getElementById( + "quarto-dashboard-header" + ); + if (dashboardNavEl !== null) { + return dashboardNavEl.clientHeight; + } else { + return 0; + } + } + function updateDocumentOffsetWithoutAnimation() { updateDocumentOffset(false); } @@ -92,7 +103,7 @@ window.document.addEventListener("DOMContentLoaded", function () { function updateDocumentOffset(animated) { // set body offset const topOffset = headerOffset(); - const bodyOffset = topOffset + footerOffset(); + const bodyOffset = topOffset + footerOffset() + dashboardOffset(); const bodyEl = window.document.body; bodyEl.setAttribute("data-bs-offset", topOffset); bodyEl.style.paddingTop = topOffset + "px"; @@ -205,9 +216,9 @@ window.document.addEventListener("DOMContentLoaded", function () { // Observe size changed for the header const headerEl = window.document.querySelector("header.fixed-top"); if (headerEl && window.ResizeObserver) { - const observer = new window.ResizeObserver( - updateDocumentOffsetWithoutAnimation - ); + const observer = new window.ResizeObserver(() => { + setTimeout(updateDocumentOffsetWithoutAnimation, 0); + }); observer.observe(headerEl, { attributes: true, childList: true, @@ -233,7 +244,7 @@ window.document.addEventListener("DOMContentLoaded", function () { // Fixup any sharing links that require urls // Append url to any sharing urls const sharingLinks = window.document.querySelectorAll( - "a.sidebar-tools-main-item" + "a.sidebar-tools-main-item, a.quarto-navigation-tool, a.quarto-navbar-tools, a.quarto-navbar-tools-item" ); for (let i = 0; i < sharingLinks.length; i++) { const sharingLink = sharingLinks[i]; diff --git a/site_libs/quarto-search/autocomplete.umd.js b/site_libs/quarto-search/autocomplete.umd.js index 619c57c..ae0063a 100644 --- a/site_libs/quarto-search/autocomplete.umd.js +++ b/site_libs/quarto-search/autocomplete.umd.js @@ -1,3 +1,3 @@ -/*! @algolia/autocomplete-js 1.7.3 | MIT License | © Algolia, Inc. and contributors | https://github.com/algolia/autocomplete */ -!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof globalThis?globalThis:e||self)["@algolia/autocomplete-js"]={})}(this,(function(e){"use strict";function t(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function n(e){for(var n=1;n=0||(o[n]=e[n]);return o}(e,t);if(Object.getOwnPropertySymbols){var i=Object.getOwnPropertySymbols(e);for(r=0;r=0||Object.prototype.propertyIsEnumerable.call(e,n)&&(o[n]=e[n])}return o}function a(e,t){return function(e){if(Array.isArray(e))return e}(e)||function(e,t){var n=null==e?null:"undefined"!=typeof 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O.value.core.environment.addEventListener("resize",e),function(){O.value.core.environment.removeEventListener("resize",e)}})),a((function(){if(!_.value)return function(){};function e(e){A.value.detachedContainer.classList.toggle("aa-DetachedContainer--modal",e)}function t(t){e(t.matches)}var n=O.value.core.environment.matchMedia(getComputedStyle(O.value.core.environment.document.documentElement).getPropertyValue("--aa-detached-modal-media-query"));e(n.matches);var r=Boolean(n.addEventListener);return r?n.addEventListener("change",t):n.addListener(t),function(){r?n.removeEventListener("change",t):n.removeListener(t)}})),a((function(){return requestAnimationFrame(E),function(){}})),n(n({},w),{},{update:C,destroy:function(){l()}})},e.getAlgoliaFacets=function(e){var t=hr({transformResponse:function(e){return e.facetHits}}),r=e.queries.map((function(e){return n(n({},e),{},{type:"facet"})}));return t(n(n({},e),{},{queries:r}))},e.getAlgoliaResults=Or,Object.defineProperty(e,"__esModule",{value:!0})})); diff --git a/site_libs/quarto-search/quarto-search.js b/site_libs/quarto-search/quarto-search.js index f5d852d..5f723d7 100644 --- a/site_libs/quarto-search/quarto-search.js +++ b/site_libs/quarto-search/quarto-search.js @@ -43,7 +43,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { const mainEl = window.document.querySelector("main"); // highlight matches on the page - if (query !== null && mainEl) { + if (query && mainEl) { // perform any highlighting highlight(escapeRegExp(query), mainEl); @@ -57,7 +57,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { // (e.g. if the user edits the query or clears it) let highlighting = true; const resetHighlighting = (searchTerm) => { - if (mainEl && highlighting && query !== null && searchTerm !== query) { + if (mainEl && highlighting && query && searchTerm !== query) { clearHighlight(query, mainEl); highlighting = false; } @@ -98,6 +98,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { classNames: { form: "d-flex", }, + placeholder: language["search-text-placeholder"], translations: { clearButtonTitle: language["search-clear-button-title"], detachedCancelButtonText: language["search-detached-cancel-button-title"], @@ -110,6 +111,8 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { return item.href; }, onStateChange({ state }) { + // If this is a file URL, note that + // Perhaps reset highlighting resetHighlighting(state.query); @@ -359,7 +362,8 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { state, setActiveItemId, setContext, - refresh + refresh, + quartoSearchOptions ); }, }, @@ -374,6 +378,32 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { focusSearchInput(); }; + document.addEventListener("keyup", (event) => { + const { key } = event; + const kbds = quartoSearchOptions["keyboard-shortcut"]; + const focusedEl = document.activeElement; + + const isFormElFocused = [ + "input", + "select", + "textarea", + "button", + "option", + ].find((tag) => { + return focusedEl.tagName.toLowerCase() === tag; + }); + + if ( + kbds && + kbds.includes(key) && + !isFormElFocused && + !document.activeElement.isContentEditable + ) { + event.preventDefault(); + window.quartoOpenSearch(); + } + }); + // Remove the labeleledby attribute since it is pointing // to a non-existent label if (quartoSearchOptions.type === "overlay") { @@ -385,11 +415,30 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { } } + function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; + } + // If the main document scrolls dismiss the search results // (otherwise, since they're floating in the document they can scroll with the document) - window.document.body.onscroll = () => { - setIsOpen(false); - }; + window.document.body.onscroll = throttle(() => { + // Only do this if we're not detached + // Bug #7117 + // This will happen when the keyboard is shown on ios (resulting in a scroll) + // which then closed the search UI + if (!window.matchMedia(detachedMediaQuery).matches) { + setIsOpen(false); + } + }, 50); if (showSearchResults) { setIsOpen(true); @@ -429,15 +478,27 @@ function configurePlugins(quartoSearchOptions) { const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ insightsClient: window.aa, onItemsChange({ insights, insightsEvents }) { - const events = insightsEvents.map((event) => { - const maxEvents = event.objectIDs.slice(0, 20); - return { - ...event, - objectIDs: maxEvents, - }; + const events = insightsEvents.flatMap((event) => { + // This API limits the number of items per event to 20 + const chunkSize = 20; + const itemChunks = []; + const eventItems = event.items; + for (let i = 0; i < eventItems.length; i += chunkSize) { + itemChunks.push(eventItems.slice(i, i + chunkSize)); + } + // Split the items into multiple events that can be sent + const events = itemChunks.map((items) => { + return { + ...event, + items, + }; + }); + return events; }); - insights.viewedObjectIDs(...events); + for (const event of events) { + insights.viewedObjectIDs(event); + } }, }); return algoliaInsightsPlugin; @@ -613,20 +674,30 @@ function showCopyLink(query, options) { /* Search Index Handling */ // create the index var fuseIndex = undefined; +var shownWarning = false; + +// fuse index options +const kFuseIndexOptions = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, +}; + async function readSearchData() { // Initialize the search index on demand if (fuseIndex === undefined) { - // create fuse index - const options = { - keys: [ - { name: "title", weight: 20 }, - { name: "section", weight: 20 }, - { name: "text", weight: 10 }, - ], - ignoreLocation: true, - threshold: 0.1, - }; - const fuse = new window.Fuse([], options); + if (window.location.protocol === "file:" && !shownWarning) { + window.alert( + "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server." + ); + shownWarning = true; + return; + } + const fuse = new window.Fuse([], kFuseIndexOptions); // fetch the main search.json const response = await fetch(offsetURL("search.json")); @@ -646,6 +717,7 @@ async function readSearchData() { ); } } + return fuseIndex; } @@ -674,7 +746,8 @@ function renderItem( state, setActiveItemId, setContext, - refresh + refresh, + quartoSearchOptions ) { switch (item.type) { case kItemTypeDoc: @@ -684,7 +757,9 @@ function renderItem( item.title, item.section, item.text, - item.href + item.href, + item.crumbs, + quartoSearchOptions ); case kItemTypeMore: return createMoreCard( @@ -709,15 +784,46 @@ function renderItem( } } -function createDocumentCard(createElement, icon, title, section, text, href) { +function createDocumentCard( + createElement, + icon, + title, + section, + text, + href, + crumbs, + quartoSearchOptions +) { const iconEl = createElement("i", { class: `bi bi-${icon} search-result-icon`, }); const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContents = [iconEl, titleEl]; + const showParent = quartoSearchOptions["show-item-context"]; + if (crumbs && showParent) { + let crumbsOut = undefined; + const crumbClz = ["search-result-crumbs"]; + if (showParent === "root") { + crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined; + } else if (showParent === "parent") { + crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined; + } else { + crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined; + crumbClz.push("search-result-crumbs-wrap"); + } + + const crumbEl = createElement( + "p", + { class: crumbClz.join(" ") }, + crumbsOut + ); + titleContents.push(crumbEl); + } + const titleContainerEl = createElement( "div", { class: "search-result-title-container" }, - [iconEl, titleEl] + titleContents ); const textEls = []; @@ -1099,17 +1205,19 @@ function algoliaSearch(query, limit, algoliaOptions) { const remappedHits = response.hits.map((hit) => { return hit.map((item) => { const newItem = { ...item }; - ["href", "section", "title", "text"].forEach((keyName) => { - const mappedName = indexFields[keyName]; - if ( - mappedName && - item[mappedName] !== undefined && - mappedName !== keyName - ) { - newItem[keyName] = item[mappedName]; - delete newItem[mappedName]; + ["href", "section", "title", "text", "crumbs"].forEach( + (keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } } - }); + ); newItem.text = highlightMatch(query, newItem.text); return newItem; }); @@ -1120,8 +1228,34 @@ function algoliaSearch(query, limit, algoliaOptions) { }); } -function fuseSearch(query, fuse, fuseOptions) { - return fuse.search(query, fuseOptions).map((result) => { +let subSearchTerm = undefined; +let subSearchFuse = undefined; +const kFuseMaxWait = 125; + +async function fuseSearch(query, fuse, fuseOptions) { + let index = fuse; + // Fuse.js using the Bitap algorithm for text matching which runs in + // O(nm) time (no matter the structure of the text). In our case this + // means that long search terms mixed with large index gets very slow + // + // This injects a subIndex that will be used once the terms get long enough + // Usually making this subindex is cheap since there will typically be + // a subset of results matching the existing query + if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) { + // Use the existing subSearchFuse + index = subSearchFuse; + } else if (subSearchFuse !== undefined) { + // The term changed, discard the existing fuse + subSearchFuse = undefined; + subSearchTerm = undefined; + } + + // Search using the active fuse + const then = performance.now(); + const resultsRaw = await index.search(query, fuseOptions); + const now = performance.now(); + + const results = resultsRaw.map((result) => { const addParam = (url, name, value) => { const anchorParts = url.split("#"); const baseUrl = anchorParts[0]; @@ -1135,6 +1269,18 @@ function fuseSearch(query, fuse, fuseOptions) { section: result.item.section, href: addParam(result.item.href, kQueryArg, query), text: highlightMatch(query, result.item.text), + crumbs: result.item.crumbs, }; }); + + // If we don't have a subfuse and the query is long enough, go ahead + // and create a subfuse to use for subsequent queries + if (now - then > kFuseMaxWait && subSearchFuse === undefined) { + subSearchTerm = query; + subSearchFuse = new window.Fuse([], kFuseIndexOptions); + resultsRaw.forEach((rr) => { + subSearchFuse.add(rr.item); + }); + } + return results; } diff --git a/sitemap.xml b/sitemap.xml index 962f021..56fac42 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -1,107 +1,119 @@ - https://data.scorenetwork.org/esports/lol-buff-nerf.html - 2023-08-03T21:58:24.225Z + https://data.scorenetwork.org/data-sources.html + 2024-03-05T15:06:01.023Z - https://data.scorenetwork.org/wrestling/sumo_wrestingling_since_1957.html - 2023-08-03T21:58:21.241Z + https://data.scorenetwork.org/lacrosse/lacrosse_ncaa_div1.html + 2024-03-05T15:06:01.071Z - https://data.scorenetwork.org/hockey/phf-shots.html - 2023-08-03T21:58:20.149Z + https://data.scorenetwork.org/motor_sports/NASCAR_driver_ratings.html + 2024-03-05T15:06:01.071Z - https://data.scorenetwork.org/baseball/verlander-pitches.html - 2023-08-03T21:58:19.085Z + https://data.scorenetwork.org/tennis/wta-grand-slam-matches.html + 2024-03-05T15:06:01.075Z - https://data.scorenetwork.org/tennis/australian-open.html - 2023-08-03T21:58:18.005Z + https://data.scorenetwork.org/running/boston_marathon_2023.html + 2024-03-05T15:06:01.071Z - https://data.scorenetwork.org/volleyball/volleyball_ncaa_team_stats.html - 2023-08-03T21:58:16.869Z + https://data.scorenetwork.org/soccer/D3_wsoc2022.html + 2024-03-05T15:06:01.071Z - https://data.scorenetwork.org/basketball/wnba-shots.html - 2023-08-03T21:58:15.717Z + https://data.scorenetwork.org/hockey/nhl_powerplay.html + 2024-03-05T15:06:01.071Z - https://data.scorenetwork.org/soccer/nwsl-team-stats.html - 2023-08-03T21:58:14.729Z + https://data.scorenetwork.org/hockey/phf-shots.html + 2024-03-05T15:06:01.071Z - https://data.scorenetwork.org/football/nfl-draft-combine.html - 2023-08-03T21:58:13.537Z + https://data.scorenetwork.org/golf/pga_drive_putt.html + 2024-03-05T15:06:01.071Z - https://data.scorenetwork.org/motor_sports/NASCAR_driver_ratings.html - 2023-08-03T21:58:12.541Z + https://data.scorenetwork.org/basketball/wnba-shots.html + 2024-03-05T15:06:01.023Z - https://data.scorenetwork.org/golf/pga_drive_putt.html - 2023-08-03T21:58:11.353Z + https://data.scorenetwork.org/basketball/WNBA20Yrs.html + 2024-03-05T15:06:01.023Z - https://data.scorenetwork.org/data-sources.html - 2023-08-03T21:58:10.397Z + https://data.scorenetwork.org/esports/lol-buff-nerf.html + 2024-03-05T15:06:01.071Z - https://data.scorenetwork.org/lacrosse/lacrosse_pll_vs_nll.html - 2023-08-03T21:58:09.217Z + https://data.scorenetwork.org/baseball/verlander-pitches.html + 2024-03-05T15:06:01.023Z - https://data.scorenetwork.org/running/boston_marathon_2023.html - 2023-08-03T21:58:08.348Z + https://data.scorenetwork.org/football/nfl-draft-combine.html + 2024-03-05T15:06:01.071Z - https://data.scorenetwork.org/lacrosse/lacrosse_ncaa_div1.html - 2023-08-03T21:58:10.001Z + https://data.scorenetwork.org/by-statsds-topic.html + 2024-03-05T15:06:01.023Z + + + https://data.scorenetwork.org/football/nfl-team-statistics.html + 2024-03-05T15:06:01.071Z https://data.scorenetwork.org/index.html - 2023-08-03T21:58:10.793Z + 2024-03-05T15:06:01.071Z https://data.scorenetwork.org/softball/softball_d1_2021-2022.html - 2023-08-03T21:58:11.997Z + 2024-03-05T15:06:01.075Z https://data.scorenetwork.org/triathlon/triathlon_lakeplacid_women_ironman.html - 2023-08-03T21:58:13.081Z + 2024-03-05T15:06:01.075Z - https://data.scorenetwork.org/football/nfl-team-statistics.html - 2023-08-03T21:58:14.193Z + https://data.scorenetwork.org/basketball/nba-player-stats.html + 2024-03-05T15:06:01.023Z - https://data.scorenetwork.org/soccer/soccer-heights.html - 2023-08-03T21:58:15.181Z + https://data.scorenetwork.org/volleyball/volleyball_ncaa_team_stats.html + 2024-03-05T15:06:01.075Z - https://data.scorenetwork.org/basketball/nba-player-stats.html - 2023-08-03T21:58:16.289Z + https://data.scorenetwork.org/hockey/nhl_2223.html + 2024-03-05T15:06:01.071Z - https://data.scorenetwork.org/tennis/wta-grand-slam-matches.html - 2023-08-03T21:58:17.453Z + https://data.scorenetwork.org/hockey/nhl-shots.html + 2024-03-05T15:06:01.071Z - https://data.scorenetwork.org/submit-data.html - 2023-08-03T21:58:18.557Z + https://data.scorenetwork.org/soccer/nwsl-team-stats.html + 2024-03-05T15:06:01.071Z - https://data.scorenetwork.org/hockey/nhl_powerplay.html - 2023-08-03T21:58:19.597Z + https://data.scorenetwork.org/soccer/soccer-heights.html + 2024-03-05T15:06:01.075Z - https://data.scorenetwork.org/hockey/nhl-shots.html - 2023-08-03T21:58:20.725Z + https://data.scorenetwork.org/tennis/australian-open.html + 2024-03-05T15:06:01.075Z - https://data.scorenetwork.org/by-statsds-topic.html - 2023-08-03T21:58:23.709Z + https://data.scorenetwork.org/submit-data.html + 2024-03-05T15:06:01.075Z + + + https://data.scorenetwork.org/wrestling/sumo_wrestingling_since_1957.html + 2024-03-05T15:06:01.075Z + + + https://data.scorenetwork.org/lacrosse/lacrosse_pll_vs_nll.html + 2024-03-05T15:06:01.071Z diff --git a/soccer/D3_wsoc2022.html b/soccer/D3_wsoc2022.html new file mode 100644 index 0000000..b4fcdc6 --- /dev/null +++ b/soccer/D3_wsoc2022.html @@ -0,0 +1,996 @@ + + + + + + + + + + + +SCORE Sports Data Repository - 2022 Divison III Women’s Soccer Results + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

2022 Divison III Women’s Soccer Results

+
+
Correlation
+
Chi-square test for association
+
+
+ +
+
+ Division III women’s soccer teams game results from 2022 season +
+
+ + +
+ +
+
Author
+
+

Hope Donoghue, Robin Lock

+
+
+ + + +
+ + + +
+ + +
+

Motivation

+

Have you ever wondered if you know how many goals the home team scored in a soccer game then can you predict how many goals the opponent scored? Is there a relationship between this? With this data set on NCAA Division III women’s soccer data from the 2022 season you will be able to explore this question.

+
+
+

Data

+

The data set consists of 3753 games among 431 teams in the 2022 Division III women’s soccer season. Each row represents one game in the season.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
D3_wsoc2022.csv
VariableDescription
away_teamName away team
away_scoreThe number of goals the away team scored in game
home_teamName of home team
home_scoreThe number of goals the home team scored in game
dateDate of game
+
+
+

Questions

+
    +

  1. Do you think the correlation value for Home Score vs. Away Score will be negative or positive? Explain your answer.

    2. +

  2. Find the correlation value for Home Score vs. Away Score from the data set. Interpret the correlation value in context.

    3. +

  3. Perform a test for association to see if the correlation value from above is statistically significant. Make sure to state the hypotheses clearly.

    4. +

  4. Describe the distribution of home scores or away scores. Would a normal (symmetric, bell-shaped) distribution be reasonable for scores?

    5. +

  5. There are a lot of games in the data set with many different scorelines that range from 0-0 all the way to 18-0. But there are very few games with high scores. Treat the scores as categories, but lump all of the larger scores into one category (e.g. 0, 1, 2, 3 or more). Create a two-way contingency table of home vs, away scores using these categories and test for an association.

    6. +
+
+
+

References

+

Data web scraped from the D3soccer.com website. https://www.d3soccer.com/seasons/women/2022/schedule

+ + +
+ +
+ +
+ + + + + + \ No newline at end of file diff --git a/soccer/nwsl-team-stats.html b/soccer/nwsl-team-stats.html index c87bd5a..5afb23b 100644 --- a/soccer/nwsl-team-stats.html +++ b/soccer/nwsl-team-stats.html @@ -2,7 +2,7 @@ - + @@ -48,7 +48,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -57,6 +63,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -70,9 +77,9 @@
-
-
-
+
-
+

National Women’s Soccer League Team Statistics

@@ -509,8 +534,10 @@

National Women’s Soccer League Team Statistics

+
+

Motivation

The National Women’s Soccer League (NWSL) is the top professional women’s soccer league in the United States. While a team’s record ultimately determines their ranking, goal differential (goals scored - goals conceded) is often a better indicator of a team’s ability. But what aspects of a team’s performance are related to their goal differential? The NWSL records a variety of statistics describing a team’s performance, such as the percentage of time they maintain possession, percentage of shots on target, etc. With this dataset, you can explore variation between teams as well as which statistics are relevant predictor variables of goal differential.

@@ -677,10 +704,9 @@

References

// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -690,8 +716,17 @@

References

interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -705,6 +740,125 @@

References

const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -747,6 +901,7 @@

References

} div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -772,6 +927,32 @@

References

}); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -837,7 +1018,9 @@

References

 
-
Maintained by Ron Yurko
+
+

Maintained by Ron Yurko

+
 
@@ -846,4 +1029,5 @@

References

+ \ No newline at end of file diff --git a/soccer/soccer-heights.html b/soccer/soccer-heights.html index 5efc1e9..1f32c1e 100644 --- a/soccer/soccer-heights.html +++ b/soccer/soccer-heights.html @@ -2,7 +2,7 @@ - + @@ -48,7 +48,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -57,6 +63,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -70,9 +77,9 @@
-
-
-
+
-
+

Soccer Heights

@@ -509,8 +534,10 @@

Soccer Heights

+
+

Motivation

A researcher wants to investigate whether there are differences among the mean height for different positions (Goalkeeper, Defender, Midfield, Attack, or Missing) of soccer players. Goalkeepers are typically thought to be taller than other positions to make up in wingspan to cover the goal better. The dataset contains information on each player including their name, position, preferred foot, and height. For this analysis, only the position and height of the player will be used. A sample of 20 players was taken from each position for this analysis.

@@ -641,10 +668,9 @@

References

// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -654,8 +680,17 @@

References

interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -669,6 +704,125 @@

References

const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -711,6 +865,7 @@

References

} div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -736,6 +891,32 @@

References

}); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -801,7 +982,9 @@

References

 
-
Maintained by Ron Yurko
+
+

Maintained by Ron Yurko

+
 
@@ -810,4 +993,5 @@

References

+ \ No newline at end of file diff --git a/softball/softball_d1_2021-2022.html b/softball/softball_d1_2021-2022.html index e4bd1ad..37bab79 100644 --- a/softball/softball_d1_2021-2022.html +++ b/softball/softball_d1_2021-2022.html @@ -2,7 +2,7 @@ - + @@ -48,7 +48,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -57,6 +63,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -70,9 +77,9 @@
-
-
-
+
-
+

Batting Statistics for Division I Softball

@@ -510,8 +535,10 @@

Batting Statistics for Division I Softball

+
+

Motivation

Data for a particular sport is often stored across numerous locations. For example, in NCAA Division I Softball, batting statistics for each season are typically stored in separate tables. (See for example the statistics hosted by https://d1softball.com/)

@@ -733,10 +760,9 @@

References

// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -746,8 +772,17 @@

References

interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -761,6 +796,125 @@

References

const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -803,6 +957,7 @@

References

} div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -828,6 +983,32 @@

References

}); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -893,7 +1074,9 @@

References

 
-
Maintained by Ron Yurko
+
+

Maintained by Ron Yurko

+
 
@@ -902,4 +1085,5 @@

References

+ \ No newline at end of file diff --git a/submit-data.html b/submit-data.html index 81c88f2..d10b390 100644 --- a/submit-data.html +++ b/submit-data.html @@ -2,7 +2,7 @@ - + @@ -45,7 +45,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -54,6 +60,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -67,9 +74,9 @@
-
-
-
+
@@ -487,8 +512,10 @@

Submit a Dataset

+
+

The goal of this repository is to provide a collection of relevant and engaging sports datasets to be used in class, handouts, assignments, projects, and SCORE modules. We are always looking for new datasets and welcome public submissions, but these datasets will need to meet the following requirements:

  • The data must be publicly shareable, and there should not be any licensing restrictions that prevent us from sharing it for educational purposes. By submitting, you certify that the dataset can be made public, and that you accept all responsibility for the publication of the data. The SCORE Network is not responsible for publication of the data.
    • @@ -617,10 +644,9 @@

    Submitting templa // clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -630,8 +656,17 @@

    Submitting templa interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -645,6 +680,125 @@

    Submitting templa const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -687,6 +841,7 @@

    Submitting templa } div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -712,6 +867,32 @@

    Submitting templa }); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -777,7 +958,9 @@

    Submitting templa
     
    -
    Maintained by Ron Yurko
    +
    +

    Maintained by Ron Yurko

    +
     
    @@ -786,4 +969,5 @@

    Submitting templa + \ No newline at end of file diff --git a/tennis/australian-open.html b/tennis/australian-open.html index dd0368e..87ac31b 100644 --- a/tennis/australian-open.html +++ b/tennis/australian-open.html @@ -2,7 +2,7 @@ - + @@ -48,7 +48,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -57,6 +63,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -70,9 +77,9 @@
    -
    -

@@ -264,6 +277,12 @@ -

Edit this page

Report an issue

+
-
+

Australian Open Tennis (2008-2022)

@@ -509,8 +534,10 @@

Australian Open Tennis (2008-2022)

+
+

Motivation

The Australian Open is an annual tennis tournament in Melbourne, Australia. We have data from the men’s tournament in the years 2008-2022, with variables for both the winner and loser of each match, along with information such as what hand they play with and their Association of Tennis Professionals (ATP) rank and characteristics of the players. Which characteristics might help predict the outcome of the matches?

@@ -704,10 +731,9 @@

References

// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -717,8 +743,17 @@

References

interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -732,6 +767,125 @@

References

const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -774,6 +928,7 @@

References

} div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -799,6 +954,32 @@

References

}); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -864,7 +1045,9 @@

References

 
-
Maintained by Ron Yurko
+
+

Maintained by Ron Yurko

+
 
@@ -873,4 +1056,5 @@

References

+ \ No newline at end of file diff --git a/tennis/wta-grand-slam-matches.html b/tennis/wta-grand-slam-matches.html index 1b4c8fb..c3abc65 100644 --- a/tennis/wta-grand-slam-matches.html +++ b/tennis/wta-grand-slam-matches.html @@ -2,7 +2,7 @@ - + @@ -48,7 +48,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -57,6 +63,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -70,9 +77,9 @@
-
-
-
+
-
+

WTA Grand Slam Matches

@@ -512,8 +537,10 @@

WTA Grand Slam Matches

+
+

Motivation

The Women’s Tennis Associate (WTA) organizes the top women’s professional tennis tour in the world. Throughout the year, there are four major tournaments yielding the most ranking points, prize money, and fame. These are known as the Grand Slam tournaments, consisting of (in order): Australian Open, French Open (aka Roland Garros), Wimbledon, and the US Open. With this dataset of information about winners and losers in WTA Grand Slam matches from 2018 to 2022, you’ll be able to explore statistics collected during matches and information about the athletes to predict match outcomes.

@@ -718,10 +745,9 @@

References

// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -731,8 +757,17 @@

References

interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -746,6 +781,125 @@

References

const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -788,6 +942,7 @@

References

} div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -813,6 +968,32 @@

References

}); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -878,7 +1059,9 @@

References

 
-
Maintained by Ron Yurko
+
+

Maintained by Ron Yurko

+
 
@@ -887,4 +1070,5 @@

References

+ \ No newline at end of file diff --git a/triathlon/triathlon_lakeplacid_women_ironman.html b/triathlon/triathlon_lakeplacid_women_ironman.html index 2cd205b..cdb30c4 100644 --- a/triathlon/triathlon_lakeplacid_women_ironman.html +++ b/triathlon/triathlon_lakeplacid_women_ironman.html @@ -2,7 +2,7 @@ - + @@ -48,7 +48,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -57,6 +63,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -70,9 +77,9 @@
-
-
-
+
-
+

Lake Placid Women’s Ironman Triathlon

@@ -512,8 +537,10 @@

Lake Placid Women’s Ironman Triathlon

+
+

Motivation

The motivation for this data analysis is to explore the relationships between swim times, bike times, and run times (in minutes) in order to gain insights into the performance patterns of the athletes. By analyzing these relationships, we can understand the interplay between different segments of the race and potentially identify areas of improvement for athletes.

@@ -715,10 +742,9 @@

References

// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -728,8 +754,17 @@

References

interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -743,6 +778,125 @@

References

const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -785,6 +939,7 @@

References

} div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -810,6 +965,32 @@

References

}); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -875,7 +1056,9 @@

References

 
-
Maintained by Ron Yurko
+
+

Maintained by Ron Yurko

+
 
@@ -884,4 +1067,5 @@

References

+ \ No newline at end of file diff --git a/volleyball/volleyball_ncaa_team_stats.html b/volleyball/volleyball_ncaa_team_stats.html index 0fb8d6d..86c64c5 100644 --- a/volleyball/volleyball_ncaa_team_stats.html +++ b/volleyball/volleyball_ncaa_team_stats.html @@ -2,7 +2,7 @@ - + @@ -48,7 +48,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -57,6 +63,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -70,9 +77,9 @@
-
-
-
+
-
+

Team Statistics for Division I Women’s Volleyball

@@ -511,8 +536,10 @@

Team Statistics for Division I Women’s Volleyball

+
+

Motivation

Division 1 NCAA women’s volleyball is the highest level of collegiate competition for women’s volleyball in the United States. With well over 300 teams compete at the Division 1 level, there is fierce competition each year as only 64 teams make the post-season Volleyball Championship. The division demands high-level athletic performances, teamwork, and strategic game play from a variety of aspects. With each team having their own strengths and weaknesses, there is no exception of exciting matchups and and shocking results.

@@ -685,10 +712,9 @@

References

// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -698,8 +724,17 @@

References

interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -713,6 +748,125 @@

References

const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -755,6 +909,7 @@

References

} div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -780,6 +935,32 @@

References

}); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -845,7 +1026,9 @@

References

 
-
Maintained by Ron Yurko
+
+

Maintained by Ron Yurko

+
 
@@ -854,4 +1037,5 @@

References

+ \ No newline at end of file diff --git a/wrestling/sumo_wrestingling_since_1957.html b/wrestling/sumo_wrestingling_since_1957.html index fe901bf..73416b4 100644 --- a/wrestling/sumo_wrestingling_since_1957.html +++ b/wrestling/sumo_wrestingling_since_1957.html @@ -2,7 +2,7 @@ - + @@ -48,7 +48,13 @@ "collapse-after": 3, "panel-placement": "end", "type": "overlay", - "limit": 20, + "limit": 50, + "keyboard-shortcut": [ + "f", + "/", + "s" + ], + "show-item-context": false, "language": { "search-no-results-text": "No results", "search-matching-documents-text": "matching documents", @@ -57,6 +63,7 @@ "search-more-match-text": "more match in this document", "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", + "search-text-placeholder": "", "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit", "search-label": "Search" @@ -70,9 +77,9 @@
-
-
-
+
-
+

Sumo wrestler characteristics

@@ -509,8 +534,10 @@

Sumo wrestler characteristics

+
+

Motivation

The Banzuke is a ranking system used in Japanese sumo wrestling to determine the order and matchups for tournaments. In the Makuuchi Banzuke division, which is the top division, the history can be traced back to the 18th century. The Banzuke lists the wrestlers’ ranks, names, stables, and more in a hierarchical order based on their performance and records in previous tournaments. Over the years, the Makuuchi Banzuke has evolved, incorporating changes in the number of ranks and divisions. Today, it plays a crucial role in organizing and structuring sumo tournaments, reflecting the wrestlers’ skill, rank, and accomplishments in the sport.

@@ -667,10 +694,9 @@

References

// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -680,8 +706,17 @@

References

interactive: true, interactiveBorder: 10, theme: 'quarto', - placement: 'bottom-start' + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } window.tippy(el, config); } const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]'); @@ -695,6 +730,125 @@

References

const note = window.document.getElementById(id); return note.innerHTML; }); + } + const xrefs = window.document.querySelectorAll('a.quarto-xref'); + const processXRef = (id, note) => { + // Strip column container classes + const stripColumnClz = (el) => { + el.classList.remove("page-full", "page-columns"); + if (el.children) { + for (const child of el.children) { + stripColumnClz(child); + } + } + } + stripColumnClz(note) + if (id === null || id.startsWith('sec-')) { + // Special case sections, only their first couple elements + const container = document.createElement("div"); + if (note.children && note.children.length > 2) { + container.appendChild(note.children[0].cloneNode(true)); + for (let i = 1; i < note.children.length; i++) { + const child = note.children[i]; + if (child.tagName === "P" && child.innerText === "") { + continue; + } else { + container.appendChild(child.cloneNode(true)); + break; + } + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(container); + } + return container.innerHTML + } else { + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + return note.innerHTML; + } + } else { + // Remove any anchor links if they are present + const anchorLink = note.querySelector('a.anchorjs-link'); + if (anchorLink) { + anchorLink.remove(); + } + if (window.Quarto?.typesetMath) { + window.Quarto.typesetMath(note); + } + // TODO in 1.5, we should make sure this works without a callout special case + if (note.classList.contains("callout")) { + return note.outerHTML; + } else { + return note.innerHTML; + } + } + } + for (var i=0; i res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.getElementById(id); + if (note !== null) { + const html = processXRef(id, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + } else { + // See if we can fetch a full url (with no hash to target) + // This is a special case and we should probably do some content thinning / targeting + fetch(url) + .then(res => res.text()) + .then(html => { + const parser = new DOMParser(); + const htmlDoc = parser.parseFromString(html, "text/html"); + const note = htmlDoc.querySelector('main.content'); + if (note !== null) { + // This should only happen for chapter cross references + // (since there is no id in the URL) + // remove the first header + if (note.children.length > 0 && note.children[0].tagName === "HEADER") { + note.children[0].remove(); + } + const html = processXRef(null, note); + instance.setContent(html); + } + }).finally(() => { + instance.enable(); + instance.show(); + }); + } + }, function(instance) { + }); } let selectedAnnoteEl; const selectorForAnnotation = ( cell, annotation) => { @@ -737,6 +891,7 @@

References

} div.style.top = top - 2 + "px"; div.style.height = height + 4 + "px"; + div.style.left = 0; let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); if (gutterDiv === null) { gutterDiv = window.document.createElement("div"); @@ -762,6 +917,32 @@

References

}); selectedAnnoteEl = undefined; }; + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + if (selectedAnnoteEl) { + selectCodeLines(selectedAnnoteEl); + } + }, 10) + ); + function throttle(fn, ms) { + let throttle = false; + let timer; + return (...args) => { + if(!throttle) { // first call gets through + fn.apply(this, args); + throttle = true; + } else { // all the others get throttled + if(timer) clearTimeout(timer); // cancel #2 + timer = setTimeout(() => { + fn.apply(this, args); + timer = throttle = false; + }, ms); + } + }; + } // Attach click handler to the DT const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); for (const annoteDlNode of annoteDls) { @@ -827,7 +1008,9 @@

References

 
-
Maintained by Ron Yurko
+
+

Maintained by Ron Yurko

+
 
@@ -836,4 +1019,5 @@

References

+ \ No newline at end of file