基于XGBoost和时域特征提取的网球比赛实时胜率预测与势头分析

Live Winning Probability Prediction and Momentum Analysis in Tennis Matches Based on XGBoost and Time-Domain Feature Extraction

摘要: 实时胜率和势头是用于分析网球比赛动态的重要指标，在体育博彩、赛事解说和技术指导中具有应用价值。然而，当前二者的量化方法主要依赖单一模型和静态统计，难以全面捕捉赛事动态。为此，本文引入实时胜率和单次得分价值等概念，基于历史赛事和当前比赛实时数据，采用XGBoost构建分层状态转移模型以预测胜率，量化每个得分点对胜率的影响，在此基础上结合时域特征提取定义比赛双方势头(Momentum)，深入分析球员表现与比赛动态变化。

Abstract: Live winning probability (LWP) and momentum are crucial indicators for analyzing tennis dynamics, with applications in sports betting, commentary, and coaching. Current quantification methods, relying on single models and static statistics, fail to fully capture match dynamics. By introducing the concepts of LWP and point impact value (PIV), and using historical and real-time data, we employ XGBoost to build a layered state transition model, predicting win probability and quantifying each point’s impact, based on which time domain feature extraction is used to define player momentum, enabling deeper analysis of performance and match dynamics.