MOHAMED SHAJITH S

IPL Crunch ’26 — IPL Match Data Analytics Using Python

The goal of this project was to analyze real IPL match data and identify patterns that influence match outcomes. Many cricket fans believe that winning the toss guarantees a higher chance of winning, while others believe scoring quickly in the Powerplay is the key to victory. However, these opinions are often based on assumptions rather than actual data. Using over 1200 IPL match datasets from Cricsheet, this project aimed to answer important analytical questions such as whether toss advantage truly impacts results, which match phase contributes most to winning, Process The project was completed using Python, Pandas, JSON processing, and Matplotlib for visualization. The first step was collecting IPL match datasets from Cricsheet in JSON format. Since each match was stored as an individual JSON file, the initial challenge was understanding the structure of the dataset and extracting useful information from hundreds of files. The analysis process began by reading all JSON files using Python and combining important match details such as toss winners, match winners, innings data, batters, bowlers, and runs scored. Data cleaning was necessary because some matches had missing values, abandoned games, or incomplete results, which had to be excluded from the analysis. The first analysis focused on toss impact. I compared toss winners with actual match winners.. Results The project successfully analyzed over 1210 completed IPL matches and transformed raw ball-by-ball data into meaningful insights and visualizations. The analysis showed that teams winning the toss won only 51.65% of matches, indicating that toss advantage has a relatively small impact on match outcomes. Phase-wise analysis revealed that winning teams consistently scored more runs across all innings phases. The Death Overs showed the largest scoring gap between winning and losing teams, making it the strongest indicator of match success among the three phases analyzed. Reflection This project analyzed over 1210 IPL matches using ball-by-ball JSON datasets. The analysis showed that teams winning the toss won only 51.65% of matches, suggesting that toss advantage has a limited impact on overall results. Phase-wise analysis revealed that winning teams consistently scored more runs in all innings phases. The biggest difference was observed during the Death Overs, indicating that strong finishing performance plays a major role in match victories. The project also identified top-performing players, with Virat Kohli leading in total runs and Yuzvendra Chahal leading in wicket