Random Forest-Based Prediction of First Olympic Medals for Non-Medaling Nations

This study employs a random forest model (91% cross-validation accuracy) to predict Olympic medal outcomes, significantly outperforming logistic regression (65%), highlighting its superior ability to capture complex interactions in sports resource allocation. Feature importance analysis identifies targeted discipline development and athlete training quality as critical predictors, while revealing five countries (Papua New Guinea, Madagascar, Gambia, Maldives, Guinea) with no historical medals. The model demonstrates how strategic investments in high-return disciplines and optimized training infrastructure drive medal surges, offering policymakers actionable insights: prioritizing under-resourced talent development, performance-based athlete incentives, and shared sports facilities. These data-driven strategies empower financially constrained nations to maximize competitive success through efficient resource targeting, transcending the limitations of linear models like logistic regression in addressing multidimensional sporting dynamics.