Predicting Olympic Glory: Unveiling the Path to Victory through Data-Driven Insights

Abstract

With the rapid advancement of artificial intelligence and machine learning, there is an increasing need for accurate prediction models in various fields, including sports. This study develops a prediction model for Olympic medal distribution using the XGBoost algorithm, leveraging historical athlete performance data to forecast medal outcomes for the 2028 Summer Olympics in Los Angeles. The model considers multiple factors such as historical medal counts, athlete experience, national background, and the influence of coaching on performance.

For the Model, we extensively reviewed existing literature and selected the XGBoost model due to its proven effectiveness in classification tasks. We incorporated GridSearchCV for hyperparameter tuning and Bootstrap resampling to account for un-certainty in predictions. Our preliminary analysis on historical data of 160 countries re-vealed a consistent trend of improvement in national performance over time. Specifically, the model predicts that the United States and China will continue to dominate, while coun-tries like France and Italy are expected to face a decline in medal counts. We used feature importance analysis to assess which factors most significantly influence medal outcomes. Our results show that national historical performance and the experience level of athletes are key determinants in predicting Olympic success. The model achieved an accuracy of 91.31%, with a high precision in predicting gold medal winners.

For the Mould, we conducted a Cost-benefit Analysis of the model’s implementation, focusing on the implications for National Olympic Committees (NOCs). The analysis suggests that the model can serve as a tool for strategic resource allocation and athlete development. We also explored the role of “great coaches” and found that their impact on medal outcomes is significant, with countries benefiting from strategic coaching invest-ments.

For the Movement, we applied the model to predict the medal distribution for the 2028 Olympics, specifically focusing on first-time medal winners. We found that 15 countries are likely to win their first Olympic medal, with a strong likelihood of success in certain disciplines like athletics and swimming. In addition, the analysis revealed that home-field advantages and the selection of event types have a substantial impact on medal tallies, which should inform future strategic decisions for NOCs.

Finally, we summarized our findings into a non-technical report aimed at providing insights to NOCs, helping them to optimize their training and resource allocation strate-gies. The sensitivity analysis highlighted that the model is adaptable and can improve as more data is integrated, making it an invaluable tool for future Olympic predictions.