Harshit Goyal

IPL Crunch '26 — Ball-by-Ball Data Analysis Using Python

The objective of this project was to analyze IPL ball-by-ball data to identify the factors most associated with winning matches. The analysis focused on understanding the impact of toss decisions, batting strategy, scoring phases, player performances, and close-match trends using historical IPL data and statistical visualization techniques. Process The project began with collecting IPL match data in JSON format from Cricsheet. The raw data was converted into a structured CSV format using Python preprocessing techniques. After cleaning and organizing the dataset, exploratory data analysis was performed using Pandas, Matplotlib, and Seaborn. Multiple match-level insights were analyzed, including toss impact, batting first vs batting second performance, phase-wise scoring trends, and close-match frequency. Statistical summaries and visualizations were created to compare winning and losing teams and identify meaningful IPL trends. Results The analysis revealed that winning the toss had minimal impact on match outcomes, while teams batting second showed a noticeable winning advantage. Middle overs were identified as the most influential phase in determining match results, and nearly one-third of IPL matches were found to be closely contested. The project successfully transformed raw cricket data into actionable insights using data analytics and visualization techniques. Reflection Given more time, I would expand the analysis by building predictive machine learning models for match outcome prediction, performing venue-based analysis, and creating interactive dashboards using Power BI or Tableau. Additional player-level metrics and season-wise comparisons could further improve the depth of the analysis.