Research on the Prediction of Event Medal Distribution Based on XGBoost and Dynamic Markov Chain

Abstract

With the rapid development of information technology, machine learning and artificial intelligence have achieved significant breakthroughs in the field of data analysis and prediction, demonstrating great application potential. For example, in the field of sports, ordinary prediction methods can no longer meet the increasingly complex global sports events with a surge in data volume. Taking sports event prediction as a specific application scenario, this paper proposes an advanced integrated prediction system based on the XGBoost algorithm. It combines a dynamic Markov prediction framework and a comprehensive evaluation model based on multi-dimensional features to help predict the number of medals in major events such as the Olympic Games. Firstly, through multi-level feature engineering and model optimization, we achieved high-precision prediction of the number of medals. This paper innovatively introduces a dynamic weight adjustment mechanism and a multi-dimensional feature interaction analysis to improve the prediction accuracy. Secondly, when predicting the medal-winning countries, we employed the Bootstrap resampling and cross-validation methods to enhance the stability of the prediction. Finally, when analyzing the impact of events on medals, we developed a comprehensive model to evaluate the dynamic influence of event changes on the medal distribution.This model not only demonstrates remarkable prediction ability and robustness in Olympic events but also can be extended to the prediction of other large-scale sports events, making new contributions to the sports undertakings of various countries.