Prediction and Analysis of Olympic Medal Distribution Using a Hyperparameter-Optimized Random Forest Algorithm

As global sports events continue to develop, the Olympic Games, as one of the most influential sports events, has always been a focal point of attention in both the sports world and academia regarding the prediction of its medal distribution. To predict medals for the 2028 Los Angeles Olympics, we developed a Random Forest-based model. By analyzing historical data, including the number of medals won by countries in previous Olympics, the number of participants, and the experience of athletes, we built a feature matrix. The model uses the number of medals as the prediction target and is trained and predicted using the Random Forest algorithm. Hyperparameters were adjusted using the Bayesian optimization algorithm, which improved the accuracy of the model’s predictions. The final prediction of the medal standings for 2028 was made, and the performance of countries in that Olympics was analyzed.