Muskan Kakwani

IPL CRUNCH '26: Complete Analysis

IPL has 5 seasons of ball-by-ball data but most fan debates rely on memory and bias, not numbers. I wanted to answer 4 specific questions with data: Does winning the toss actually help? Which match phase (Powerplay, Middle, Death) is most decisive? Who are the real top performers across 5 seasons? And what does the data reveal that surprises even cricket fans? Dataset: 84,588 deliveries across 353 matches from 2021–2025 (Cricsheet.org). Process I started by loading and cleaning the raw CSV data using Pandas, checked for nulls in key columns like toss_winner, winner, batter, bowler. Then I filtered to last 5 seasons and removed duplicates by match_id. For each question I built a separate analysis block: toss win rates, phase-wise run averages, player aggregates, and team-level toss-loss win %. I built 4 charts using Matplotlib with a consistent dark theme. Initially my phase analysis was wrong because I forgot to separate innings fixed that by grouping by match_id and innings together. Results Toss winners won only 50.4% ,barely better than a coin flip. Middle overs (7–15) showed the biggest run gap of +7.8 between winners and losers, making it the most decisive phase. Shubman Gill led batting with 2927 runs at SR 139.3. HV Patel led bowling with 105 wickets. Most surprising: SRH, KKR and LSG each won 56–57% of matches WITHOUT winning the toss: proving skill consistently overrides the toss advantage. Reflection I would add venue-wise and season-wise breakdowns to see if toss impact changed over years or stadiums. I'd also include win probability shifts per over to make the phase analysis more dynamic. Presenting findings in an interactive dashboard (Plotly/Streamlit) instead of static charts would make it easier for others to explore the data themselves.