A Hybrid Intelligent Model for Olympic Medal Prediction Based on Data-Intelligence Fusion
Ning Li, Junhao Li, Hejia Fang, Jian Wang, Qiao Yu, Yafei Shi · Technologies · 2025
This study presents a hybrid intelligent model for predicting Olympic medal distribution at the 2028 Los Angeles Games, based on data-intelligence fusion (DIF). By integrating historical medal records, athlete performance metrics, debut medal-winning countries, and coaching resources, the model aims to provide accurate national medal forecasts. The model introduces a Performance Score (PS) system combining a Traditional Advantage Index (TAI) via K-means clustering, an Athlete Strength Index (ASI) using a backpropagation neural network, and a Host effect factor. Sub-models include an autoregressive integrated moving average model for time-series forecasting, logistic regression for predicting debut medal-winning countries, and random forest regression to quantify the “Great Coach” effect. The results project America winning 44 gold and 124 total medals, and China 44 gold and 94 total medals. The model demonstrates strong accuracy with root mean square errors of 3.21 (gold) and 4.32 (total medals), and mean-relative errors of 17.6% and 8.04%. Compared to the 2024 Paris Olympics, the model projects a notable reshuffling in 2028, with the United States expected to strengthen its overall lead as host while countries like France are predicted to experience significant declines in medal counts. Findings highlight the nonlinear impact of coaching and event expansion’s role in medal growth. This model offers a strategic tool for Olympic planning, advancing medal prediction from simple extrapolation to intelligent decision support.