Author(s)

Mengyang Li ¹, Siyuan Peng ², Peijia Li ¹

Affiliation(s)

¹ College of Arts and Sciences, Northeast Agricultural University, Harbin, China
² College of Economics and Management, Northeast Agricultural University, Harbin, China

Corresponding Author

Mengyang Li

ABSTRACT

In the 2023 Wimbledon men's singles final, frequent changes in momentum have become one of the focus of attention. This paper aims to explore the performance, momentum advantage and momentum changes in athletes. First, the data were characterized by dimension reduction and feature extraction. Then, nonlinear regression was used to construct a time point-player Performance score evaluation model, in which the performance score of each player at each time point in each game was calculated according to the characteristics. Then the GA-BP neural network prediction model was constructed based on the athlete scores, and we found that the model had good results for predicting the inflection point in the game, with an accuracy of 88.64%. According to the contribution rate of the characteristic indicators to the fluctuation, give the suggestions for different players in the new game, covering the technical level, physical factors and tactical strategies.

KEYWORDS

Athlete Momentum, GA-BP, Neural Network, Non-linear regression score model, tactical strategies

CITE THIS PAPER

Mengyang Li, Siyuan Peng, Peijia Li, Research on Athlete Momentum Based on GA-BP Neural Network. Journal of Human Movement Science (2024) Vol. 5: 80-87. DOI: http://dx.doi.org/10.23977/jhms.2024.050112.

REFERENCES

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