Paridhi Jain

IPL Crunch '26: Data-Driven Analysis of IPL Match Outcomes

Cricket analysis often relies on popular assumptions rather than evidence. Common beliefs such as "winning the toss gives a major advantage" or "death overs decide matches" are frequently repeated but rarely validated using large-scale historical data. The objective of this project was to analyse 18 IPL seasons, 1239 matches and 294,757 ball-by-ball deliveries to identify the factors that truly influence match outcomes. The goal was to transform raw cricket data into actionable insights and determine which phases, players and performance indicators have the strongest relationship with winning. Process Collected and consolidated historical IPL match and ball-by-ball datasets spanning 18 seasons. Cleaned, transformed, and analyzed the data to create match-level and phase-level performance metrics. Examined the relationship between toss results and match outcomes to evaluate the true impact of toss advantage. Segmented innings into Powerplay, Middle Overs, and Death Overs, comparing scoring patterns between winning and losing teams. Identified leading run scorers and wicket takers to assess long-term player performance. Built visualizations and statistical summaries to communicate findings clearly. The analysis showed that toss advantage has limited influence, while middle-over dominance is the strongest predictor of IPL match success. Results Analyzed 1,239 IPL matches and 294,757 deliveries across 18 seasons. The study found that toss winners won only 50.61% of matches, indicating that toss advantage has limited influence on match outcomes. Middle overs (7–15) produced the largest run differential between winning and losing teams, with an average gap of 7.82 runs, making it the strongest predictor of victory. The analysis also highlighted Virat Kohli as the leading run scorer and Yuzvendra Chahal as the leading wicket taker in IPL history. Reflection Given more time, I would incorporate advanced statistical modeling and predictive machine learning techniques to quantify the contribution of individual match factors. I would also expand the analysis to include venue conditions, team compositions, player form, and season-specific trends. Interactive dashboards and real-time visualizations could further improve accessibility and decision-making for analysts, teams, and cricket enthusiasts.