Wooble
Back to Marutinandan.h.singh's profile
Verified on Wooble

Crunch-26 · IPL Data Challenge ·

Winning the toss means nothing. Middle overs win matches. The data proves what 76% of captains already knew.

Marutinandan.h.singh singhCrunch-26 · IPL Data Challenge ·

50.4%

Toss winners win only half of matches

2,927

Shubman Gill is best batter right now

72%

72% of all wickets are caught

Overview

IPL analysis is dominated by commentary and intuition. Fans assume toss winners have an edge, that death overs decide games, and that strike rate is the only batter metric that matters. This project challenges those assumptions with evidence. The questions that drove this Three specific questions formed the brief: (1) Do teams that win the toss actually win more matches? 2) Which phase powerplay, middle overs, or death overs is most linked to winning? (3) Who are the top 5 batters and bowlers across 5 seasons? Process Downloaded 5 seasons of ball-by-ball data from Cricsheet (289K rows). Cleaned mixed data types, merged RCB's two name variants, and fixed phase averaging that was being skewed by wides and no-balls. Built match-level aggregations for toss outcomes, phase run totals split by winner vs loser, and batter/bowler career stats. Tested each broadcast narrative against the numbers toss, phase importance, batting first. Visualised findings in a Chart.js dashboard, wrote a structured report, and built a 10-slide deck with the reasoning and iterations. Results Toss winners win 50.4% of matches identical to a coin flip. Teams that won the toss and batted first won only 47.1%, lower than toss losers. The middle overs (7–15) show the biggest gap between winners and losers at +7.4 runs, beating death overs (+3.6) by nearly double. Shubman Gill leads all batters with 2,927 runs. Rashid Khan stands out with 83 wickets at 7.95 economy — over a run cheaper per over than anyone else in the top 5. Gujarat Titans have the best win rate at 61.7% across the period. The one finding that genuinely surprised me: death overs are overrated. Reflection Run proper significance tests chi-square on the toss data, t-tests on phase differences instead of relying on descriptive stats alone. Build an efficiency composite metric that combines runs, strike rate, wickets, and economy into one score, so volume players don't automatically rank above efficient ones like Rashid Khan. Segment the analysis by venue. Toss dynamics in dew-heavy Mumbai are very different from dry-pitch Jaipur. The current analysis treats all venues the same. Add a pressure metric for death overs based on required run rate vs actual run rate, not just raw runs scored.

Links & files

Artifacts

3

Gallery

2