IPL Match Analytics: Data-Driven Insights Using Python, SQL & Power BI

IPL generates massive amounts of ball-by-ball cricket data every season, but most match discussions remain opinion-based rather than data-driven. The goal of this project was to analyze historical IPL datasets containing 200K+ ball-by-ball records and uncover meaningful insights related to toss impact, player performance, scoring behavior, and match-winning trends. The challenge was to transform raw datasets into clear visual storytelling using Python, SQL, and Power BI while creating a beginner-to-intermediate level sports analytics MVP suitable for hackathon submission and portfolio showcase Process I collected IPL datasets from Kaggle, including match-level and ball-by-ball data, and cleaned them using Python Pandas by handling missing values, duplicate records, and inconsistent team names. After preparing the datasets, I used SQL queries and exploratory data analysis techniques to study toss impact, player performance, scoring trends, and match-winning patterns. Additional features like Powerplay, Middle Overs, and Death Overs were created to improve analysis depth. Multiple dashboard layouts and visualizations were tested before selecting the most effective storytelling approach. Since the project was built as an MVP during exams, I focused on one clean and interactive Power BI dashboard page. Results The MVP project analyzed 200K+ IPL ball-by-ball records using Python, SQL, and Power BI to uncover match-winning trends, toss impact, and player performance insights. The dashboard improved my practical skills in data cleaning, exploratory data analysis, visualization, and sports analytics while delivering a portfolio-ready hackathon submission during academic exam preparation. Reflection In future versions, I would expand the project into multiple dashboard pages with advanced analytics, venue-based trends, predictive models, and machine learning integration. I would also improve interactivity using advanced Power BI filters and richer storytelling visualizations. The current version intentionally focused on building a clean and effective MVP dashboard.