Ayush Kandpal
Featured project
IPL Crunch ’26: Data-Driven Analysis of 1,218 IPL Matches
Cricket discussions are often driven by assumptions rather than evidence. This project aimed to analyze 1,218 IPL matches (2007–2025) using ball-by-ball and match-level data to determine whether commonly accepted beliefs — such as toss advantage, phase dominance, and star player impact — are actually supported by data. The goal was to uncover measurable patterns behind winning outcomes and present them through clear, data-driven storytelling. Process he project began with collecting IPL datasets from Cricsheet and organizing both match-level and ball-by-ball records. After cleaning missing values and standardizing team names across seasons, match deliveries were grouped into phases: Powerplay (1–6), Middle Overs (7–15), and Death Overs (16–20). Exploratory Data Analysis was then performed to compare winning and losing teams across different match conditions. Toss outcomes, phase-wise scoring patterns, and player performances over the last five seasons were aggregated and visualized using Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn. Several visualization approaches were tested before finalizing cleaner, insight-focused charts that prioritized readability and storytelling over dashboard complexity. The final analysi Results Analysis of 1,218 IPL matches revealed that toss advantage is statistically negligible, with toss winners winning only 50.5% of matches. The study also found that death overs create the largest scoring gap between winning and losing teams, making them the strongest predictor of victory. The project improved my skills in data analysis, visualization, and insight-driven storytelling using real IPL datasets. Reflection With more time, I would extend the project using machine learning models to predict match outcomes based on live match conditions. I would also add venue analysis, player form trends, and interactive dashboards for deeper cricket insights.