Prediction of potential energy on player scoring trend in tennis match based on Hopfield neural network model

Haodong Bian; Shijia Ni; Yuchen Liu

doi:10.54097/9emvr219

Authors

Haodong Bian
Shijia Ni
Yuchen Liu

DOI:

https://doi.org/10.54097/9emvr219

Keywords:

Entropy Weight Method, Hopfield Neural Network, the Flow of Match.

Abstract

In tennis, it is very important to catch the direction and trend of the match when the score happens. This paper aims to establish a mathematical model to predict the dynamic change of match progress and the appearance of match points, and explain the role of "momentum" in the match. By mining the real-time match data of 2023 Wimbledon Tennis Open, based on Hopfield neural network and entropy weight method, the technical level, psychological pressure and fatigue level of players in the match are selected as the influencing factors, and the mathematical model between the "momentum" of players and the change of the match flow is established, and the change of the match schedule can be predicted more accurately. The change of momentum may affect the motivation of players, and the choice of a reasonable strategy may be a magic weapon to win the match. It is hoped that this can help the pre-competition training of tennis players and promote the vigorous development of tennis events.

Downloads

Download data is not yet available.

References

[1] Šarčević A, Vranić M, Pintar D, et al. Predictive modeling of tennis matches: a review[C]//2022 45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO). IEEE, 2022: 1099-1104.

[2] Wilkens S. Sport’s prediction and betting models in the machine learning age: The case of tennis [J]. Journal of Sports Analytics, 2021, 7(2): 99-117.

[3] Sim M K, Choi D G. The winning probability of a game and the importance of points in tennis matches [J]. Research quarterly for exercise and sport, 2020, 91(3): 361-372.

[4] Gao Z, Kowalczyk A. Random forest model identifies serve strength as a key predictor of tennis match outcome [J]. Journal of Sports Analytics, 2021, 7(4): 255-262.

[5] Ünal T. Predicting tennis match outcome: a machine learning approach using the SRP-CRISP-DM framework [D]. Middle East Technical University, 2023.

[6] Gollub J. Forecasting serve performance in professional tennis matches [J]. Journal of Sports Analytics, 2021, 7(4): 223-233.

[7] Martínez-Gallego R, Crespo M, Ramón Llin J, et al. Men's doubles professional tennis on hard courts: Game structure and point ending characteristics [J]. 2020.

[8] Cui Y, Liu H, Liu H, et al. Data-driven analysis of point-by-point performance for male tennis player in Grand Slams[J]. Motricidade, 2019, 15(1): 49-61.

[9] O'Donoghue P, Ingram B. A notational analysis of elite tennis strategy [J]. Journal of sports sciences, 2001, 19(2): 107-115.

[10] Gu W, Saaty T L. Predicting the outcome of a tennis tournament: Based on both data and judgments [J]. Journal of Systems Science and Systems Engineering, 2019, 28: 317-343.

[11] Bayram F, Garbarino D, Barla A. Predicting tennis match outcomes with network analysis and machine learning[C]//SOFSEM 2021: Theory and Practice of Computer Science: 47th International Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2021, Bolzano-Bozen, Italy, January 25–29, 2021, Proceedings 47. Springer International Publishing, 2021: 505-518.