Prediction and Strategic Analysis of Olympic Medals Based on Hybrid Modeling Method

Abstract

This study developed a hybrid prediction framework to address the issues of Olympic medal prediction and strategic analysis, with a focus on model architecture, integration, and validation. A combination of linear regression and random forest algorithm was proposed, which was dynamically weighted through grid search optimization. This model combines athlete performance indicators such as participation frequency, historical medal count, national strength indicators, host country advantages, and sports specific dynamics, and rigorously preprocesses the data to address inconsistencies in event classification and historical country codes. Construct a prediction interval through error distribution analysis, and achieve a coverage rate of 91.4-93.2% through the country specific chi storial error quantile. The entropy weighted evaluation of medal concentration, competitiveness, and stability is used for the first time, and the constrained model prioritizes medal prediction for highly probable sports (athletics, swimming, shooting, boxing). This article establishes three indicators to evaluate the importance of different sports to a country: medal concentration, international competitiveness, and time stability. The entropy weight method is used to determine the weights of indicators. At the same time, the changes in medals won by the host country in additional events were counted. For the "Great Coach" effect, this article specifically counted the medals won by Lang Ping and Bella Karoli in volleyball and gymnastics. Research has found that the 'big coach' effect does indeed exist.