The Evolution of Indian Cricket: How IPL Transformed a Nation's Game

Codedex February 2026 Dataset Challenge Submission

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📖 The Story

The Indian Premier League (IPL) launched in 2008 and fundamentally changed cricket forever. But beyond the fireworks and celebrity owners, what does the data actually tell us?

This project analyzes committed IPL summary datasets across 1,169 IPL matches spanning 18 seasons (2008-2025) to answer three key questions. The latest committed match date is 2025-06-03.

🔍 Q1: How has batting evolved? Are teams scoring faster than ever?
🔍 Q2: Has the bat-vs-ball balance shifted? Are bowlers endangered?
🔍 Q3: Is the toss advantage a myth or reality?

🎯 Key Findings

#	Finding	Evidence
1	The Run Explosion is Real	Run rate rose about 17% from 2008-2010 to 2023-2025, with sixes per match up about 71.5%
2	Bowlers Have Adapted, Not Died	Despite soaring economy rates, wickets per match remain stable
3	The Toss is (Mostly) Irrelevant	Toss winner match-win rate is near 50% across completed matches, though chasing has become preferred in recent seasons

Surprise "Aha" Moment

IPL scoring did not rise evenly. Six-hitting growth and high-scoring recent seasons changed the shape of match totals, while wickets remained a meaningful counterweight for bowlers.

📊 Visualizations

The notebook contains reproducible Plotly visualizations based only on the CSVs committed in this repository:

1. The Run Explosion - average match runs and run rate trend
2. The Boundary Revolution - fours and sixes per match
3. Bowling Under Pressure - economy and wicket-taking trends
4. The Great Toss Debate - toss-win conversion and field-first preference
5. All-Time Run Scorers - top batters colored by strike rate
6. Top Wicket Takers - top bowlers colored by economy

📂 Project Structure

ipl-evolution-data-analysis/
├── IPL_Evolution_Analysis.ipynb    # 📓 Main analysis notebook (submission)
├── README.md                       # 📋 This file
├── data/
│   ├── ipl_matches.csv             # 📊 1,169 match summaries
│   ├── ipl_batting_stats.csv       # 🏏 Player batting stats by season
│   ├── ipl_bowling_stats.csv       # 🎳 Player bowling stats by season
│   └── DATASET.md                  # 🧾 Source, coverage, and limitations card
├── scripts/
│   ├── validate_data.py            # ✅ Validate committed CSVs
│   ├── summarize_findings.py       # 📌 Recompute README headline claims
│   ├── process_data.py             # 🔧 Optional raw data -> clean datasets pipeline
│   └── create_notebook.py          # 📓 Notebook generator script

🛠️ How to Run

Prerequisites

pip install pandas numpy plotly matplotlib seaborn jupyter

Step 1: Validate the committed data

python3 scripts/validate_data.py
python3 scripts/summarize_findings.py
python3 scripts/summarize_findings.py --verify-docs
python3 -m unittest discover -s tests

Step 2: Regenerate the notebook

python3 scripts/create_notebook.py

Step 3: Open the notebook

jupyter notebook IPL_Evolution_Analysis.ipynb

Optional: Rebuild summary datasets from raw Cricsheet files

scripts/process_data.py expects the large raw IPL Cricsheet CSV archive under data/ipl_raw/. That raw archive is intentionally not committed. When available locally:

python3 scripts/process_data.py
python3 scripts/validate_data.py
python3 scripts/create_notebook.py

📋 Data Source

• Source: Cricsheet.org — Open-source ball-by-ball cricket data
• Committed Format: CSV summary files for matches, batting-by-season, and bowling-by-season
• Processing: Optional raw match CSVs can be parsed into structured datasets using Python/Pandas
• Current committed data: 1,169 match summaries plus player-season batting and bowling summaries; latest committed match date is 2025-06-03
• Coverage Card: data/DATASET.md

🏅 Challenge Categories Targeted

Category	How
🏆 Best Storyteller	Full end-to-end narrative with clear questions, analysis, and conclusions
😍 Best Data Visualization	Six focused interactive Plotly charts with a premium dark theme
💡 Sherlock "Aha" Moment	Scoring growth + six-hitting pressure + toss myth
💌 Best Original Dataset	Summary datasets processed from open Cricsheet data

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Reproducibility Contract

• scripts/validate_data.py checks schema, exact 2008-2025 season coverage, non-negative metrics, date/season consistency, unique player-season rows, and recomputed strike rate/economy values.
• scripts/summarize_findings.py recomputes the README headline numbers from committed CSVs only, including toss calculations that exclude no-result matches.
• scripts/summarize_findings.py --verify-docs fails when headline README or dataset-card claims drift from the committed CSVs.
• scripts/create_notebook.py is the source of truth for IPL_Evolution_Analysis.ipynb; update the script first, then regenerate.

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Made with ❤️ and 🏏 for the Codedex February 2026 Dataset Challenge