Author(s)

Xinyu Lu ¹, Qilong Li ², Shuo Yuan ³, Yucheng Mo ⁴

Affiliation(s)

¹ College of Control Science and Engineering, China University of Petroleum, Qingdao, 266580, China
² School of Economics and Trade, Hunan University of Technology, Zhuzhou, 412007, China
³ School of Geosciences, China University of Petroleum, Qingdao, 266580, China
⁴ School of Economics and Management•UPC, China University of Petroleum, Qingdao, 266580, China

Corresponding Author

ABSTRACT

Tennis, as a popular sport around the world, has become the focus of sports data science research. The quantification of players' momentum and the prediction of key swings in tennis matches are of great significance for mastering the dynamics of matches and improving the performance of players. Based on the men's singles data of the 2023 Wimbledon Tennis Championship, this study uses comprehensive mathematical methods and models, including confusion matrix, Logistic regression, exponential moving average (EMA), Bessel curve fitting, and run test, and is implemented by Python and MATLAB to obtain the probability of players winning at any time. Momentum is continuous, and the momentum related to the winning streak and turning point of the game is verified by running tests. This paper offers a research direction for studying the fluctuation of player momentum during a match, aiming to quantify player momentum and predict the likelihood of victory in a match.

KEYWORDS

CITE THIS PAPER

Xinyu Lu, Qilong Li, Shuo Yuan, Yucheng Mo, Data-Driven Insights into Tennis Match Momentum: A Predictive Study from the 2023 Wimbledon Championships. Automation and Machine Learning (2024) Vol. 5: 154-161. DOI: http://dx.doi.org/10.23977/autml.2024.050120.

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