Frontiers in Sport Research, 2024, 6(3); doi: 10.25236/FSR.2024.060304.
Xinlin Zhu1, Shuojie Wang2, Zhaonan Wu1
1School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, 212013, China
2College of Computer and Information Engineering (College of Artificial Intelligene), Nanjing Tech University, Nanjing, 211816, China
Tennis is an increasingly popular sport worldwide. This paper focuses on the study of "momentum" in tennis matches, which is utilized to predict match fluctuations. In this paper, we quantify the momentum about physical momentum and construct an evaluation model. Using principal component analysis to analyze the long-term influencing factors, four principal components are obtained; considering that the influence of the actual game situation has periodical timeliness, a mathematical model of the short-term factors is constructed. Then, based on the established evaluation model, this paper quantifies the fluctuation, and based on the data of the finals, conducts ARIMA time series prediction of the fluctuation, and uses the neural network model to determine the most relevant factors affecting the fluctuation.
Principal Component Analysis, ARIMA Time Series Prediction, Neural Network
Xinlin Zhu, Shuojie Wang, Zhaonan Wu. Prediction of the Swings in Plays Based on Momentum of Tennis Matches. Frontiers in Sport Research (2024) Vol. 6, Issue 3: 29-36. https://doi.org/10.25236/FSR.2024.060304.
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