Olympic Medal Count Prediction Research Based on A Hybrid ARIMA-Xgboost-Lightgbm Model

Abstract

This study proposes an integrated ARIMA–XGBoost–LightGBM model to predict both medal counts and medal-winning probabilities for the 2028 Los Angeles Olympic Games. The framework begins by applying the ARIMA model to forecast time-series features, such as the number of athletes and participating countries. These features are then fed into an XGBoost regressor to estimate gold, silver, bronze, and total medals per country. Additionally, a LightGBM classifier is utilized to predict the probability that a nation will win at least one medal. Model performance was evaluated using ten-fold cross-validation, with R² values exceeding 0.84 across all medal categories, demonstrating high accuracy and robust generalization ability. Notably, six countries are projected to win their first Olympic medals based on the predicted probabilities. This ensemble approach effectively combines time series forecasting with machine learning algorithms, showcasing its potential in supporting strategic sports planning and medal outcome prediction. It provides valuable forecasting capabilities for complex, dynamic events such as the Olympics.