IPL MATCH INTELLIGENCE SYSTEM

IPL matches are often seen as unpredictable, especially during run chases. Teams usually rely on scoreboards and intuition, but there is no system that clearly measures match pressure, momentum shifts, or the real impact of wickets and scoring phases during a chase. This project aims to use IPL ball-by-ball data analytics and machine learning to understand hidden match patterns, predict chase outcomes, and create a pressure-based cricket intelligence system that provides meaningful tactical insights instead of just raw statistics. Process The project started with collecting and preprocessing IPL ball-by-ball and match-level datasets from multiple seasons. Missing values, inconsistent records, and duplicate entries were cleaned using Python and Pandas. Exploratory Data Analysis (EDA) was then performed to identify important match patterns such as toss impact, venue influence, phase-wise scoring trends, and chase pressure behavior. After analysis, custom features like Pressure Index, Required Run Rate, wickets left, and momentum metrics were engineered to improve prediction quality. Multiple machine learning models including Logistic Regression, Random Forest, and XGBoost were trained and compared, where XGBoost achieved the best performance. SHAP explainability was integrated to interpret model predictions. Results The analysis revealed that IPL chase outcomes are strongly influenced by pressure buildup, middle-over wickets, scoring phases, and venue conditions rather than just toss results. The custom Pressure Index successfully identified high-risk chase situations and showed strong correlation with match collapse patterns. The project also uncovered strategic insights such as the importance of death-over acceleration and the limited impact of toss wins on final outcomes. The final machine learning system achieved 90.35% prediction accuracy with an AUC score of 0.970 using XGBoost. Reflection Unlike traditional IPL dashboards that only show scores and statistics, this project focused on uncovering hidden match patterns using ball-by-ball IPL data. It analyzed pressure buildup, momentum shifts, phase-wise scoring, venue impact, and middle-over wicket effects through advanced data analytics and a custom Pressure Index. The system further combined explainable machine learning with an interactive Streamlit dashboard for real-time tactical cricket insights and chase prediction.