Quantifying Momentum in Tennis: A Case Study of the 2023 Wimbledon Gentlemen’s Final
DOI:
https://doi.org/10.54097/79n8c895Keywords:
Wimbledon Gentlemen’s Final, Support Vector Machine, Multi-layer Perception Model.Abstract
In sports competitions, especially in famous sports like tennis, momentum has a very significant impact on the outcome of the competition. In the era of big data, research can predict the momentum changes in the game based on historical data, thereby providing players and coaches with reasonable strategies. This analysis can increase the player's winning rate. Explore the presence and changes of momentum in the game through a case study of the 2023 Wimbledon men's singles final. The research selected key moments in the game, including serve games, break games, and errors, to capture momentum through a series of quantifiable indicators, and applied random forest, support vector machine, and multi-layer perception models. Their accuracy rates are 0.6691, 0.6643, and 0.6479 respectively. The research aimed to predict scoring results and evaluate player performance. The research provides coaches and players with a new tool to quantify momentum in tennis matches, helping to better understand match flow, develop tactics, and improve match performance.
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References
Rivera, J.: Tennis scoring, explained: A guide to understanding the rules, terms & point system at Wimbledon. The Sporting News. Retrieved April 5, 2024, from https://www.sportingnews.com/us/tennis/news/tennis-scoring-explained-rules-system-points-terms/7uzp2evdhbd11obdd59p3p1cx. last accessed 2024/16/01.
Momentum.: Merriam-Webster.com Dictionary, Merriam-Webster, https://www.merriam-webster.com/dictionary/momentum. last accessed 2024/07/04.
Meier, P., Flepp, R., Ruedisser, M., Franck, E.: Separating psychological momentum from strategic momentum: Evidence from men’s professional tennis. Journal of Economic Psychology, 78, Article 102269 (2020).
Dietl, H., Nesseler, C.: Momentum in tennis: Controlling the match. UZH Business Working Paper Series (No. 365). University of Zurich (2017).
Silva, J. M., Hardy, C. J., Crace, R. K.: Analysis of psychological momentum in intercollegiate tennis. Journal of Sport & Exercise Psychology, 10, 346-354 (1988).
Leo, B.: Bagging predictors, Machine Learning, 24.2: 123-140 (1996).
Rosenblatt, F.: The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386 (1958).
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning, 20(3), 273–297 (1995).
Anderson, R., Anderson, L. F.: AI and machine learning in sports analytics: A New Era of data-driven athlete performance and match strategy. Journal of Sports Analytics, 7(1), 54-65 (2021).
Zhao, Y., van Buiten, M.: Real-time strategy optimization using machine learning: A case study on tennis. International Journal of Performance Analysis in Sport, 22(2), 245-260 (2022).
Martin, A. P., Jones, G., Ibrahim, K.: Implementing Machine Learning Techniques for Tactical Insights in Professional Tennis. Journal of Computational Science, 45, Article 101329 (2022).
Lee, D., Kim, J. Y., Han, O.: The role of data science in sports decision making: current landscape and future prospects. Data Science and Management, 3(2), 1-12 (2020).
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