Janani S
Featured project
IPL Crunch ’26 – Data Analytics Project
IPL generates massive amounts of ball-by-ball cricket data every season, but extracting meaningful performance insights from raw datasets is challenging. This project focuses on analyzing IPL match data to identify winning patterns, batting and bowling trends, match-phase impact, and strategic factors influencing team success through data analytics and visualization. Process The project began with collecting IPL ball-by-ball datasets from publicly available cricket data sources. Data cleaning and preprocessing were performed using Python and Pandas in Google Colab to handle missing values, inconsistent records, and match-level transformations. Exploratory Data Analysis (EDA) techniques were applied to study batting performance, bowling efficiency, toss impact, strike rates, venue trends, and match-phase scoring patterns. Multiple visualization approaches were tested to present insights clearly and professionally. Initially, some charts appeared cluttered and inconsistent with the presentation theme. The visualization style was refined using custom-designed charts, color consistency, and structured storytelling to improve readability and presentation quality. Results The analysis successfully identified major IPL performance trends and strategic insights. Middle overs were found to contribute the highest cumulative runs, while aggressive strike rates and boundary-hitting significantly influenced winning probabilities. The project produced multiple analytical dashboards and visual reports covering batting performance, bowling impact, toss analysis, venue scoring patterns, and match-phase analysis. The final presentation transformed raw cricket data into clear, actionable insights through professional storytelling and visualization. Reflection If given additional time, I would integrate machine learning models to predict match outcomes and player performance more accurately. I would also build interactive dashboards using Power BI or Streamlit to provide real-time analytical exploration and improve user interaction with the data insights.