Authors - Nazia Sultana, Kumar P K Abstract - This research details the design and implementation of the AI-Driven Penalty Performance Analysis System, a desktop application aimed at bridging the technological divide in football analytics. The system focuses particularly on environmental and situational influences, such as crowd size, match context, and time of day, on penalty outcomes. The system employs a robust data pipeline and a comparative evaluation of multiple machine learning classifiers to predict the likelihood of penalty kick success. Using a dataset of professional penalties, we engineered novel features such as a ‘PressureIndex‘ to quantify situational fac tors. A suite of models, including Logistic Regression, K-Nearest Neighbours, Decision Tree, Random Forest, and Gradient Boosting, was trained and evalu ated. The optimal Gradient Boosting model achieved an accuracy of 79.1% and an AUC-ROC score of 0.87. A critical contribution is the integration of Explain able AI (XAI) using SHapley Additive exPlanations (SHAP), which transforms the system from a predictive ’black box’ into a transparent, diagnostic tool. This provides coaches and players with actionable, data-driven insights, validating the system’s potential to democratize advanced sports analytics.
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