Data Set for Rhythms of Victory: Predicting Professional Tennis Matches Using Machine Learning

Forecasting  the winning matches of professional tennis players has a wide range of practical applications. We introduced an innovative approach to quantify and combine strategic and psychological momentum using the entropy weight method and analytic hierarchy process, and tested its effectiveness. Utilizing data from the Wimbledon Championship 2023, we constructed a support vector machine  model to predict the turning point and winner of each point, and optimized it using particle swarm optimization. Our model achieved a significant level of accuracy (96.09\% for turning point and 83.52\% for predicting the winner) and performed well in different courts and players. Furthermore, we compared its performance with commonly utilized predictive models, including ARIMA, LSTM and BP networks, and found that our model exhibited higher accuracy than other existing models in predicting the point winner. Our study provides a reference for the role of momentum in dynamic matches, and our model can be used to calculate the odds of tennis matches and provide guidance to coaches.

预测职业网球选手的赛事获胜结果，具备广泛的实际应用价值。本研究提出一种创新方法，借助熵权法（Entropy Weight Method）与层次分析法（Analytic Hierarchy Process）对战略动量与心理动量进行量化融合，并验证了该方法的有效性。本研究依托2023年温布尔登网球锦标赛（Wimbledon Championship 2023）的赛事数据，构建支持向量机（Support Vector Machine）模型以预测每一分的转折点与得分方，并采用粒子群优化（Particle Swarm Optimization）算法对模型进行优化。所提模型的预测精度表现优异：转折点预测准确率达96.09%，得分方预测准确率达83.52%，且在不同赛场与选手场景下均展现出良好的泛化性能。此外，本研究将所提模型与自回归积分滑动平均模型（ARIMA）、长短期记忆网络（LSTM）及反向传播（BP）神经网络等常用预测模型进行性能对比，结果显示，在单分得分方预测任务中，本模型的准确率优于所有现有对比模型。本研究为动量在动态赛事中的作用机制提供了理论参考，所提模型可用于计算网球赛事的获胜赔率，并为教练团队提供战术指导。