IPL Crunch 2026: Data-Driven Match Insights and Performance Analysis

Cricket discussions often rely on assumptions such as winning the toss being a major advantage or certain match phases deciding outcomes. The goal of this project was to validate these assumptions using IPL data and answer three questions: 1. Do teams winning the toss actually win more matches? 2. Which phase (Powerplay, Middle Overs, Death Overs) is most linked to winning? 3. Who are the top-performing batters and bowlers across seasons? Process The project began with collecting IPL match data in JSON format from Cricsheet. The raw JSON files were parsed and converted into two structured datasets: matches.csv for match-level information and deliveries.csv for ball-by-ball information. Data preprocessing was performed by removing duplicate records, checking missing values, and standardizing team names to maintain consistency. Match phases such as Powerplay, Middle Overs, and Death Overs were created through feature engineering. Exploratory Data Analysis (EDA) was then performed using Python libraries including Pandas and Matplotlib to study toss impact, compare phase-wise performance of winning and losing teams, identify top players, create visualizations, and extract insights. Results The analysis showed that winning the toss had only a limited impact on match outcomes. Comparing scoring across different phases revealed that Death Overs had the largest difference between winning and losing teams, making them the phase most strongly linked with victories. The project also identified top-performing batters and bowlers based on runs and wickets across seasons, highlighting the importance of consistent performance. Reflection If given additional time, I would spend more effort exploring additional match patterns and improving the interpretation of results. I would also refine the visual presentation of the analysis and include more comparative charts to make insights clearer and easier to understand.