IPL-Data-Analysis

IPL Data Analysis Using Python - Team Project

🏏 IPL Advanced Performance & Inference Engine

A professional data analytics pipeline and interactive dashboard that processes historic IPL data, isolates tactical situational splits, and applies scientific statistical testing to validate common cricket assumptions.

👉 **[Click Here to View the Live Interactive Dashboard] https://ipl-data-analysis-cwm47nzbuprapjb7cywrox.streamlit.app/

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📊 Executive Insights Discovered

• The Toss Myth Debunked: A Chi-Square ($\chi^2$) Test of Independence proved that winning the toss grants no statistically significant advantage ($p > 0.05$) to winning the match in the long term.
• Venue DNA Profiles: A Kruskal-Wallis test structurally confirmed ($p < 0.05$) that stadium boundary dimensions and pitch archetypes create permanent, distinct scoring baselines rather than random variance.
• Situational Masters: High-velocity filtering isolated custom tactical splits, ranking the top 10 historical players by powerplay strike-rates and death-overs finishing efficiency.

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🛠️ Tech Stack & Architecture

• Frontend Dashboard: Streamlit, Plotly Express
• Data Processing: Python, Pandas (Optimized via Dictionary Key-Value Mapping)
• Statistical Inference: SciPy (Stats module)

Pipeline Breakdown

1. notebooks/01_data_cleaning.ipynb: Schema standardization, handling corporate franchise name changes, and structural formatting.
2. notebooks/02_descriptive_eda.ipynb: Evaluates macro trends, stadium scoring distributions, and asymmetric head-to-head franchise rivalries.
3. notebooks/03_situational_analysis.ipynb: Isolates situational pressure phases (Powerplay vs Death Overs) using high-performance filtering.
4. notebooks/04_statistical_testing.ipynb: Mathematical verification layer running non-parametric ANOVA and independence tests.
5. dashboard/app.py: Production-ready presentation layer with automated relative path fallbacks for cloud deployment.