IPL Match Analysis — Busting Cricket Myths with Data

IPL generates massive amounts of ball-by-ball data but most cricket opinions are based on gut feeling, not numbers. This project set out to answer 3 specific data-backed questions:

1. Do teams that win the toss actually win more matches?
2. Which phase — powerplay, middle, or death overs — is most linked to winning?
3. Who are the top 5 batters and bowlers across the last 5 IPL seasons?

Dataset: 80,448 balls across 333 matches from IPL 2020-21 to 2024.

Process Started with raw ball-by-ball CSV data and followed a structured end-to-end analysis pipeline:

• Data cleaning in Python — removed ties, no-result matches, super overs, and standardized team names that changed over seasons

• Feature engineering — created phase column (Powerplay/Middle/Death) and match-level toss outcome column

• Exploratory analysis — profiled distributions, run curves, and wicket patterns before answering the 3 core questions

• Visualized findings in both Python (matplotlib, seaborn) and Power BI dashboard

• Documented everything in a structured Google Colab notebook as proof of work

Results Question 1 — Toss Winner Win Rate: 48% The toss has no meaningful advantage in modern IPL. Even captains who chose to field first (71% of the time) won only 49.8% of their matches.

Question 2 — Powerplay is the key phase Winning teams outscore losing teams most in the Powerplay (gap: +1.77 runs per match). Surprising finding: losing teams actually score MORE in death overs than winning teams — the final slog does not determine match outcomes.

Question 3 — Top Performers (2020-2024) Top Batter: F du Plessis — 2,718 runs Top Bowler: YS Chahal — 104 wickets

Reflection

1. Build a predictive model — with this clean dataset the natural next step is a match outcome predictor using powerplay score as a key feature

2. Include player consistency metrics — total runs is a good start but strike rate, average, and performance under pressure tell a deeper story

3. Venue analysis — some grounds heavily favor batting or bowling, which would add important context to the phase analysis

4. Expand to all 19 seasons — 5 seasons gives recent trends but historical comparison would show how IPL batting strategies have evolved over time