Academic Journal of Computing & Information Science, 2024, 7(4); doi: 10.25236/AJCIS.2024.070414.
Jia Yang
College of Science, Wuhan University of Science and Technology, Wuhan, China
This paper focuses on the influence of "momentum" in tennis and analyzes the scoring pattern of athletes using LightGBM model and BP neural network model. First, the selected influencing factors were subjected to PCA dimensionality reduction. Then, we built the LightGBM model to evaluate the real-time performance of the players. Secondly, we quantified "momentum" using the entropy weighting method and confirmed the importance of "momentum" in the game by comparing the cumulative "momentum" with the actual win/loss situation and conducting the chi-square test. The importance of "momentum" in the game was confirmed by comparing the cumulative "momentum" with the actual winning and losing situations and conducting chi-square test. Finally, a BP neural network model was developed to predict momentum fluctuations and to identify the factors associated with these fluctuations.
Momentum, LightGBM, Bayesian Variable Point Detection, BP Neural Networks
Jia Yang. Research on Tennis Match Strategies Based on LightGBM and BP Neural Network Models. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 4: 103-109. https://doi.org/10.25236/AJCIS.2024.070414.
[1] Dietl, Helmut, and Cornel Nesseler. "Momentum in tennis: Controlling the match." UZH Business Working Paper Series, 365 (2017).
[2] Li, Tao, et al. "A PCA-based method for construction of composite sustainability indicators." The International Journal of Life Cycle Assessment, 17 (2012): 593-603.
[3] Ke, Guolin, et al. "Lightgbm: A highly efficient gradient boosting decision tree." Advances in neural information processing systems, 30 (2017).
[4] Vierra, Andrew, Abdul Razzaq, and Athena Andreadis. "Categorical Variable Analyses: Chi-square, Fisher Exact, and Mantel–Haenszel." Translational Surgery. Academic Press, (2023). 171-175.
[5] Nitzan, Eyal, Topi Halme, and Visa Koivunen. "Bayesian methods for multiple change-point detection with reduced communication." IEEE Transactions on Signal Processing, 68 (2020): 4871-4886.
[6] Merk, Timon, et al. "Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation." Experimental Neurology, 351 (2022): 113993.
[7] Benth, Fred Espen, Nils Detering, and Luca Galimberti. "Neural networks in Fréchet spaces." Annals of Mathematics and Artificial Intelligence, 91.1 (2023): 75-103.