Research on Momentum of Tennis Players Based on Gaussian Mixture Clustering and TOPSIS


  • Yuanhao Bai



Tennis, Momentum, GMM, Entropy weight-TOPSIS, Ahp-entropy weight method.


In order to investigate the interplay between player victories and tennis performance with the overarching goal of predicting match outcomes and refining athletes' training regimens, this study introduces the concept of 'momentum'. Six metrics were extracted for this study utilizing a large dataset carefully selected from the first round of men's singles matches at the 2023 Wimbledon Open. Subsequently, this study used Gaussian Mixture Clustering Model (GMM) and Entropy Analysis-TOPSIS algorithm to systematically rank the scores of the eight categories. From there, the performance of the players was delineated. Subsequently, Spearman's correlation coefficient and analysis of variance (ANOVA) were used to scrutinize the complex relationship between "momentum" and the variables that encompass performance fluctuations. The empirical findings indicated that the correlation coefficients between Momentum and the correlation indices in this study ranged from [0.014-0.220] and that the p-values of the findings were consistently lower than 0.001.The results of the subgroup ANOVA were statistically significant, thus reinforcing the robustness of our analytical framework.


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How to Cite

Bai, Y. (2024). Research on Momentum of Tennis Players Based on Gaussian Mixture Clustering and TOPSIS. Highlights in Science, Engineering and Technology, 105, 189-197.