A Random Forest-based Prediction Model for Turning Points in Antagonistic Event-Group Competitions

TL;DR

This paper introduces a Random Forest-based prediction model that accurately identifies turning points in antagonistic event-group competitions by quantifying competitive potential energy and analyzing its dynamic changes.

Contribution

It proposes a novel quantitative equation for competitive potential energy and integrates it with an optimized Random Forest model to predict competition turning points.

Findings

Quantitative equation effectively reflects competition dynamics

Model achieves 86.13% recall rate in predicting turning points

The approach provides real-time feedback for athletes and coaches

Abstract

At present, most of the prediction studies related to antagonistic event-group competitions focus on the prediction of competition results, and less on the prediction of the competition process, which can not provide real-time feedback of the athletes' state information in the actual competition, and thus can not analyze the changes of the competition situation. In order to solve this problem, this paper proposes a prediction model based on Random Forest for the turning point of the antagonistic event-group. Firstly, the quantitative equation of competitive potential energy is proposed; Secondly, the quantitative value of competitive potential energy is obtained by using the dynamic combination of weights method, and the turning point of the competition situation of the antagonistic event-group is marked according to the quantitative time series graph; Finally, the random forest prediction model based on the optimisation of the KM-SMOTE algorithm and the grid search method is established. The experimental analysis shows that: The quantitative equation of competitive potential energy can effectively reflect the dynamic situation of the competition; The model can effectively predict the turning point of the competition situation of the antagonistic event-group, and the recall rate of the model in the test set is 86.13%; The model has certain significance for the future study of the competition situation of the antagonistic event-group.