Decoding Olympic Medal Success: A Multi-Factor Analysis and Predictive Framework

A comprehensive and interpretable framework is proposed to forecast and analyze Olympic medal distributions by integrating ensemble machine learning techniques with statistical concentration diagnostics. Utilizing a structured dataset comprising both athlete-level and nation-level features—such as performance records, sport-specific metadata, host advantages, and historical patterns—the framework employs the XGBoost algorithm to predict medal counts for the 2028 Summer Olympics. The model achieves strong predictive performance, particularly for gold medal forecasting RMSE = 2.42, accuracy = 93.66 , and is validated through rigorous cross-validation procedures. To explore structural disparities in medal allocation, Gini and Herfindahl–Hirschman indices are computed across multiple disciplines, revealing significant concentration in sports like swimming, gymnastics, and athletics, where a limited number of countries consistently dominate podium outcomes. Model interpretability is enhanced using SHAP SHapley Additive exPlanations , which identifies the relative contributions of demographic, structural, and sport-specific variables to medal predictions. This integrative approach not only enables accurate and explainable Olympic forecasting but also provides actionable insights for evaluating competitive equity and informing national sports investment strategies.