IPL Crunch '26 - What Actually Wins IPL Matches?
Analyzed whole data of IPL ball-by-ball to reveal that toss decision, death over runs and powerplay wickets decide match outcomes - not just the toss itself.
MADDIRALA SAI SIDDHARTHA
50.5%
Toss win rate
53.7%
Field first win rate
7
Charts & Insights
Overview
IPL has millions of opinions — who should bat first, which phase matters most, does the toss decide matches. But very few back these opinions with real data. This project analyzes 5+ seasons of IPL ball-by-ball data to find what actually wins matches — using numbers, not opinions.
Process
- Loaded and explored ball-by-ball IPL match data using Python and Pandas in Google Colab.
- Cleaned the data — extracted unique matches, handled missing values, created phase labels for each ball (Powerplay, Middle, Death).
- Analyzed toss impact using match-level data to avoid counting the same match multiple times.
- Compared run rates and wickets across phases for winning vs losing teams.
- Identified top 5 batters and bowlers across all seasons excluding run outs.
- Discovered surprise insights — toss decision and chasing vs defending patterns.
- Added a bonus Virat Kohli career stats analysis using the same dataset.
Results
- Toss winners win 50.5% of matches — negligible edge
- Captains choosing to field win 53.7% vs 44.3%, for batting — toss decision matters more
- Chasing teams win more matches overall — directly explains why captains prefer to field
- Death overs run rate is the biggest gap between winning and losing teams
- Powerplay wickets are critical — early pressure decides match momentum
- Virat Kohli leads all batters with 9000+ runs across all seasons
Reflection I would improve the phase analysis by also studying individual player performances per phase — not just team-level data. I would also add a venue-wise analysis to see whether home-ground advantage affects outcomes. Next time, I would use Plotly for interactive charts instead of static matplotlib charts.