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Momentum prediction models of tennis match based on CatBoost regression and random forest algorithms.

Authors :
Lv, Xingchen
Gu, Dingyu
Liu, Xianghu
Dong, Jingwen
li, Yanfang
Source :
Scientific Reports. 8/13/2024, Vol. 14 Issue 1, p1-17. 17p.
Publication Year :
2024

Abstract

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20452322
Volume :
14
Issue :
1
Database :
Academic Search Index
Journal :
Scientific Reports
Publication Type :
Academic Journal
Accession number :
179040421
Full Text :
https://doi.org/10.1038/s41598-024-69876-5