IPL Crunch '26 - What Actually Wins IPL Matches?

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 1. Loaded and explored ball-by-ball IPL match data using Python and Pandas in Google Colab. 2. Cleaned the data — extracted unique matches, handled missing values, created phase labels for each ball (Powerplay, Middle, Death). 3. Analyzed toss impact using match-level data to avoid counting the same match multiple times. 4. Compared run rates and wickets across phases for winning vs losing teams. 5. Identified top 5 batters and bowlers across all seasons excluding run outs. 6. Discovered surprise insights — toss decision and chasing vs defending patterns. 7. 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.