Momentum in Tennis Echoes in each Bounce

Yifei Cheng, Linhui Zhao, Jiexi Lian

Advances in Humanities and Modern Education Research Vol. 1 / No. 1 / pp. 7-17 / August 7, 2024

DOI 10.70114/ahmer.2024.1.1.P7

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Abstract

The 2023 Wimbledon Gentlemen’s final unfolded as a remarkable battle, characterized by momentum shifts. However, it is challenging to measure momentum and its effect on the events during the match. This article primarily focuses on how to quantify and evaluate momentum, predict the swings of the match, test the generality and provide useful advice to players and coaches. Firstly, we conduct data pre-processing by removing outliers on the given data according to the tennis rules. Next, in order to identify which player is performing better, we select 10 secondary indicators based on Spearman Correlation Coefficients, such as distance and rally_count from two aspects: technique and physiology. Then, we combine CRITIC method to calculate the weights of related indicators and TOPSIS method to evaluate the performance. The result indicates that the model can capture the flow of the match and Carlos Alcaraz performs better by a higher momentum of 0.06 in the given data with match_id of 2023-wimbledon-1301. Then, to assess the coach’s claim regarding the role of momentum in the match, we establish a model for on-court situation changes using statistical methods and compare it with CRITICTOPSIS model. By comparison, we conclude that the change in momentum trend shows a 59% correlation with the outcome of the next point and there is a clear correlation between the changes in Momentum and on-court situation. That is to say, momentum do play a role in the match.

Keywords

CRITIC-TOPSIS / Momentum

2024 International Conference on Education, Humanities and Sciences Development

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More in This Issue

  1. 1 Exploring the Feasibility and Educational Suggestions for Integrating OBE Educational Concepts into Higher Vocational English Courses pp. 1–6
  2. 7 Momentum in Tennis Echoes in each Bounce pp. 7–17
  3. 18 Optimizing GPT Technology in Educational Administration: Strategies, Practices, and Action Research Insights pp. 18–30
  4. 31 Digital Resources Construction and Education Research for the Course of Material Research and Testing Methods pp. 31–38
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