2028 Olympic Medal Prediction Model: Integrating Random Forest and Monte Carlo Simulation

Abstract

This article investigates the prediction of medal outcomes for the 2028 Los Angeles Summer Olympics and establishes a multi-model framework to analyze and forecast the potential results for various countries. A random forest regression model is used to analyze historical medal counts and competition data, alongside a Monte Carlo simulation to quantify uncertainty. The findings suggest that the United States and China are expected to lead in both the number of gold medals and total medals. Additionally, an analysis predicts that two countries may secure medals for the first time in 2028, with the associated error quantified. A weighted scoring model evaluates the importance of different Olympic events for various countries, revealing that host countries tend to excel in sports with historical advantages or strategic investments. The impact of the "Great Coach" effect on medal performance is also examined, demonstrating that high-profile coaches significantly improve the performance of national teams. Finally, the study compares the differences in medal distributions among countries and highlights notable examples of athletes whose performances have evolved over time.