IPL Crunch: Data Over Opinions
Turned 289K+ ball-by-ball records into a filterable dashboard proving the toss is neutral, chasing edges wins, and death overs decide more than the powerplay.
rohit jishtu
Overview
IPL Crunch ’26 asked us to work with real ball-by-ball data and answer high-impact questions:
• Do teams that win the toss actually win more matches? • Which phase matters most — Powerplay, Middle, or Death? • Who are the top batters and bowlers in terms of winning, not just career stats? • What hidden patterns change how we read IPL results?
Process I treated this like a real analytics project, not a one-off notebook.
- Understand the brief — mapped Wooble’s questions to measurable metrics.
- Audit the data — inspected columns, match counts, team/venue label inconsistencies.
- Clean & normalize — merged duplicate franchise and stadium names before aggregating.
- Engineer metrics — built win-contribution ratings, chase/defense scores, MVP scoring, and win-driver tags.
- Analyze in Python — single-pass pipeline over ~289K ball rows.
- Tell the story — written report + interactive dashboard with filters.
- Ship & validate — rebuilt artifacts, published repo + live GitHub Pages dashboard.
Tools: Python 3 (stdlib only), CSV processing, static HTML dashboard with client-side filtering. No black-box ML
Results Live: https://rohitjishtu.github.io/ipl-crunch-26/dashboard/index.html
Reflection Phase filters in-browser · lighter deploy (dashboard only) · test one stranger on filters before submit.