IPL Crunch '26 — Data Analytics Challenge

An end-to-end data analytics project analyzing 1,226 IPL matches (2007–2026) with 291,574 ball-by-ball records from Cricsheet.

Built with Python, Pandas, Matplotlib, and Seaborn. Submitted for the Wooble IPL Crunch '26 challenge.

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Key Questions Answered

1. Do teams that win the toss actually win more matches?

No. The toss winner wins only 50.6% of matches — essentially a coin flip.

• Teams that field first win 53.8% of the time (66% of captains choose to field)
• Teams that bat first win only 44.3% of the time
• The toss advantage is minimal in IPL. Skill and execution matter far more.

2. Which phase impacts victory the most?

Phase	Winning RR	Losing RR	Gap
Powerplay (1–6)	8.51	7.60	+0.91
Middle (7–15)	8.23	7.24	+0.99
Death (16–20)	10.88	8.90	+1.98

Death overs show the biggest gap between winners and losers. Teams that finish strong win.

3. Top Batters Across Seasons

Player	Runs
V Kohli	9,155
RG Sharma	7,291
S Dhawan	6,769

4. Top Bowlers Across Seasons

Player	Wickets
YS Chahal	229
B Kumar	221
SP Narine	205

5. Hidden Patterns

• Chasing advantage: Teams batting second win 53.9% of matches
• Run rate evolution: IPL average run rate has climbed from 7.98 (early seasons) to 9.30 (recent) — a 16.5% increase
• 60 unique venues have hosted IPL matches
• Teams overwhelmingly prefer to field first (66% of decisions), and it pays off

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Files

File	Description
ipl_crunch_26_analysis.ipynb	Main Jupyter notebook with all code, analysis, and charts
matches.csv	Consolidated match-level data (1,226 rows)
deliveries.csv	Consolidated ball-by-ball data (291,574 rows)
01_toss_vs_win.png	Toss advantage analysis charts
02_phase_impact.png	Phase-wise run rate comparison
03_top_batters.png	Top run-scorers across IPL history
03b_top_batter_per_season.png	Highest run-scorer each season
04_top_bowlers.png	Top wicket-takers across IPL history
04b_top_bowler_per_season.png	Highest wicket-taker each season
05_hidden_patterns.png	Chasing advantage, venue impact
06_run_rate_trend.png	IPL run rate evolution over time

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Tools Used

• Python 3 — Pandas, NumPy, Matplotlib, Seaborn
• Google Colab — Runtime environment
• Cricsheet — Raw IPL data source

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How to Run

1. Open ipl_crunch_26_analysis.ipynb in Google Colab or Jupyter
2. Upload matches.csv and deliveries.csv when prompted
3. Run all cells (Runtime → Run all)
4. Download the results ZIP with all charts

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Key Insight

[Add your one surprising insight here — something you discovered in the data that you didn't expect.]

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Author: [Your Name]
Challenge: IPL Crunch '26 by Wooble
Dataset: Cricsheet (https://cricsheet.org)