Lv Xingchen, Gu Dingyu, Liu Xianghu, Dong Jingwen, Li Yanfang
Department of Arts and Sciences, Suqian University, Jiangsu, 223800, Suqian, People's Republic of China.
Sci Rep. 2024 Aug 13;14(1):18834. doi: 10.1038/s41598-024-69876-5.
As we all know, momentum plays a crucial role in ball game. Based on the 2023 Wimbledon final data, this paper investigated momentum in tennis. Firstly, we initially trained a decision tree regression model on reprocessed data for prediction, and established the CBRF model based on CatBoost regression and random forest regression models to obtain prediction data. Secondly, significant non-zero autocorrelation coefficients were found, confirming the correlation between momentum and success. Thirdly, Based on these key factors, we proposed winning strategies for the players, conducted predictive analyses for six specific time intervals of the game. At last, by implementing these models to women's matches, championships, matches on different surfaces, the results demonstrated that the models have effective generalization ability.
众所周知，动量在球类运动中起着至关重要的作用。基于2023年温布尔登网球锦标赛决赛数据，本文对网球运动中的动量进行了研究。首先，我们在重新处理后的数据上初步训练了一个决策树回归模型用于预测，并基于CatBoost回归和随机森林回归模型建立了CBRF模型以获取预测数据。其次，发现了显著的非零自相关系数，证实了动量与成功之间的相关性。第三，基于这些关键因素，我们为球员提出了获胜策略，并对比赛的六个特定时间间隔进行了预测分析。最后，通过将这些模型应用于女子比赛、锦标赛以及不同场地的比赛，结果表明这些模型具有有效的泛化能力。
