What can artificial intelligence do for tennis?
DOI:
https://doi.org/10.52383/itfcoaching.v33i92.563Keywords:
machine learning, performance analysis, artificial intelligence, researchAbstract
In the current era of Artificial Intelligence, we are witnessing how this technology is revolutionizing the world of sports. Through a review of the main Machine Learning research in tennis over the last decade, players, coaches, and fitness trainers can discover new proposals to improve and personalize training sessions, enhance player effectiveness, and optimize decision-making during competition.
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Chase, C. (2020). The data revolution: Cloud computing, artificial intelligence, and machine learning in the future of sports. 21st century sports: How technologies will change sports in the digital age, 175-189. DOI: https://doi.org/10.1007/978-3-030-50801-2_10
Giles, B., Kovalchik, S. & Reid, M. (2020) A machine learning approach for automatic detection and classification of changes of direction from player tracking data in professional tennis, Journal of Sports Sciences, 38:1, 106-113, DOI: 10.1080/02640414.2019.1684132 DOI: https://doi.org/10.1080/02640414.2019.1684132
Kovalchik, S. & Reid, M. (2018) A shot taxonomy in the era of tracking data in professional tennis, Journal of Sports Sciences, 36:18, 2096-2104, DOI: 10.1080/02640414.2018.1438094 DOI: https://doi.org/10.1080/02640414.2018.1438094
Kovalchik, S. A., & Albert, J. (2022). A statistical model of serve return impact patterns in professional tennis. arXiv preprint arXiv:2202.00583.
Martínez-Gallego, R., Ramón-Llin, J., & Crespo, M. (2021). A cluster analysis approach to profile men and women’s volley positions in professional tennis matches (doubles). Sustainability, 13(11), 6370. DOI: https://doi.org/10.3390/su13116370
McCarthy, J. (2004). What is artificial intelligence?.
Mecheri, S., Rioult, F., Mantel, B., Kauffmann, F., Benguigui, N. (2016). The serve impact in tennis: First large-scale study of big Hawk-Eye data. Statistical Analysis and Data Mining: The ASA Data Science Journal, 9, 310-325. https://doi.org/10.1002/sam.11316 DOI: https://doi.org/10.1002/sam.11316
Mitchell, T. (1997). Machine Learning. McGraw Hill. p. 2. ISBN 978-0-07-042807-2.
Russell, S. J., & Norvig, P. (2010). Artificial intelligence a modern approach. London.
Samuel A. (1959). Some studies in machine learning using the game of checkers. IBM Journal of research and development, 3(3):210-29. DOI: https://doi.org/10.1147/rd.33.0210
Shimizu, T., Hachiuma, R., Saito, H., Yoshikawa, T., & Lee, C. (2019, October). Prediction of future shot direction using pose and position of tennis player. In Proceedings Proceedings of the 2nd International Workshop on Multimedia Content Analysis in Sports (pp. 59-66). DOI: https://doi.org/10.1145/3347318.3355523
Stanko, I. (2020). The Architectures of Geoffrey Hinton. In: Skansi, S. (eds) Guide to Deep Learning Basics. Springer, Cham. https://doi.org/10.1007/978-3-030-37591-1_8 DOI: https://doi.org/10.1007/978-3-030-37591-1_8
Stappen, L., Milling, M., Munst, V., Hoffmann, K., & Schuller, B. W. (2022). Predicting Sex and Stroke Success--Computer-aided Player Grunt Analysis in Tennis Matches. arXiv preprint arXiv:2202.09102.
Thakur, A., & Konde, A. (2021). Fundamentals of neural networks. International Journal for Research in Applied Science and Engineering Technology, 9(VIII), 407-426. DOI: https://doi.org/10.22214/ijraset.2021.37362
Vives, F., Lázaro, J., Guzmán, J. F., Martínez-Gallego, R., & Crespo, M. (2023). Optimizing Sporting Actions Effectiveness: A Machine Learning Approach to Uncover Key Variables in the Men’s Professional Doubles Tennis Serve. Applied Sciences, 13(24), 13213. DOI: https://doi.org/10.3390/app132413213
Wei, X., Lucey, P., Morgan, S., Carr, P., Reid, M., & Sridharan, S. (2015, August). Predicting serves in tennis using style priors. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 2207-2215). DOI: https://doi.org/10.1145/2783258.2788598
Whiteside, D. and Reid, M. (2017). Spatial characteristics of professional tennis serves with implications for serving aces: A machine learning approach, Journal of Sports Sciences, 35:7, 648-654, https://doi.org/10.1080/02640414.2016.1183805 DOI: https://doi.org/10.1080/02640414.2016.1183805
Zhou J. Q. & Liu, Y. (2024) Probability prediction of groundstroke stances among male professional tennis players using a tree-augmented Bayesian network, International Journal of Performance Analysis in Sport, DOI: 10.1080/24748668.2024.2314646 DOI: https://doi.org/10.1080/24748668.2024.2314646
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