A two-phase tennis game momentum prediction model based on random forest

Zemin Gan; Wenzhou Zhao; Miao Fang; Tao Liu; Yunfei Qi

doi:10.1117/12.3039409

4 September 2024 A two-phase tennis game momentum prediction model based on random forest

Zemin Gan, Wenzhou Zhao, Miao Fang, Tao Liu, Yunfei Qi

Proceedings Volume 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024); 132592E (2024) https://doi.org/10.1117/12.3039409
Event: Fourth International Conference on Automation Control, Algorithm, and Intelligent Bionics (ICAIB 2024), 2024, Yinchuan, China

Abstract

In numerous tennis matches, people have discovered that there exists a kind of momentum in tennis competitions. When this kind of momentum forms, the player occupying the momentum exhibits an almost overwhelming consecutive win in this set. To study this kind of momentum, this paper uses the tennis match data of Jeff Sackmann on GitHub. Firstly, it conducts an exploratory analysis on the data of the men's singles tennis match at Wimbledon, and uses the TOPSIS comprehensive evaluation model with critical weighting to score the performance of the momentum of the players in a game, obtaining the broken-line graph of the momentum performance score for each point of both players. Afterwards, the random forest machine learning model with particle swarm hyperparameter optimization is used to fit and predict the change of momentum, and the confusion matrix indicates that the accuracy rate reaches 93.3%. The fitting results show that the number of times the player breaks serve and the cumulative number of game wins have outstanding contributions to the player's momentum.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zemin Gan, Wenzhou Zhao, Miao Fang, Tao Liu, and Yunfei Qi "A two-phase tennis game momentum prediction model based on random forest", Proc. SPIE 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024), 132592E (4 September 2024); https://doi.org/10.1117/12.3039409

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