Prediction of winning and losing in tennis match based on entropy weight -TOPSIS and machine learning model

Quankun Zang; Zetong Li; Jiren Hu; Tingxu Zhang; Weiqi Zhang

doi:10.62051/fkvxrd83

Authors

Quankun Zang
Zetong Li
Jiren Hu
Tingxu Zhang
Weiqi Zhang

DOI:

https://doi.org/10.62051/fkvxrd83

Keywords:

Entropy weight -TOPSIS; Machine learning; Decision tree.

Abstract

Tennis is a beloved sport worldwide, and spectators often eagerly predict the winners of both sides based on various playing conditions. Research has shown that there is a significant correlation between players' performance in the game and their momentum. To explore the impact of players' momentum on match results, this paper establishes a TOPSIS evaluation model based on entropy weight method to measure players' momentum value at every moment. Additionally, this paper establishes a decision tree model to predict real-time trends in match results according to match data. Ultimately, this paper findings suggest that players with higher momentum perform better at any given time, and predictions about game outcomes can be made based on differences in momentum. This study sheds light on various indicators and psychological states of athletes during competition and training while providing scientific recommendations for developing strategies, adjusting mentality as needed, and achieving optimal results.

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