IPL Analytics

analyzed IPL data and answer questions such as: Do teams that win the toss actually win more matches? Which phase impacts victory the most — Powerplay, Middle Overs, or Death Overs? Who are the top batters and bowlers across seasons? What hidden patterns or surprises can you discover from the data? Process So the whole project started with a simple question — does winning the toss in IPL actually help you win the match? Sounds obvious right, like of course it should matter. But that's exactly what we wanted to check with real data instead of just assuming. We took a ball-by-ball IPL dataset — meaning every single delivery bowled across all IPL seasons is one row in the data. That's over two lakh rows. First thing we did was just clean it up a bit — removed matches where the result wasn't clear, made sure each match had one consistent winner recorded throughout. Then we ran our first test. We checked how often the toss winner actually went on to win the match. Turns out it's about 51-52%. And here's the interesting part — we didn't just stop at that number. We ran a chi-square test on it, whi Results The toss finding was the most surprising. Toss winners win about 51-52% of matches — and when we ran a chi-square significance test on that, it came back as statistically indistinguishable from a coin flip. Meaning across nearly a thousand IPL matches, winning the toss gave no meaningful advantage. But the decision after the toss — bat or field — did show a difference. Teams choosing to field first and chase won more consistently. For phases, the death overs (17-20) showed the largest gap between winning and losing teams. Powerplay runs were roughly similar between both sides. It's how you fin Reflection Most IPL notebooks on Kaggle just print a top-10 list and draw a bar chart. This one asked whether the findings were statistically real, not just visually interesting. The chi-square test, the phase delta calculation, the bowler scatter — these are all about going one layer deeper than the obvious answer. The toss analysis is a good example — the first number says 52% which sounds like it matters, but the test says it doesn't. That's the difference between observing data and actually understanding it.