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Updated: May 7, 2025
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Comparison of Kinetic Characteristics of Footwork during Stroke in Table Tennis: Cross-Step and Chasse Step
Published on: June 16, 2021
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A multidimensional momentum chain model for tennis matches based on difference equations.
Jingya Wang1, Sihang Guo2, Yuanyun Zhou2
1School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, Shenzhen, Guangdong, China.
Plos One
|December 31, 2024
Summary
This study quantifies sports momentum using a novel multidimensional model. Higher momentum significantly boosts player performance and increases the likelihood of winning tennis matches, with an 80% prediction accuracy.
Area of Science:
- Sports Science
- Psychology
- Data Science
Background:
- Momentum is recognized as a key factor influencing competitive sports outcomes.
- Understanding the psychological and behavioral impact of momentum is crucial for performance enhancement.
Purpose of the Study:
- To develop a multidimensional model for quantifying athlete momentum dynamics in tennis.
- To investigate the relationship between momentum, player performance, and match outcomes.
Main Methods:
- Utilized statistical analysis and linear weighting with difference equations to build a momentum chain model.
- Incorporated a forgetting curve to refine momentum fluctuation analysis.
- Analyzed 7,284 scoring points from Wimbledon 2023 men's singles and 6,870 additional tennis matches.
Main Results:
- Dominant players exhibited shorter distances and higher net-stroke success rates, indicating performance enhancement from positive events.
- Higher player momentum correlated with increased scoring probability and an 84% chance of victory for the serving side.
- The model achieved over 80% prediction accuracy across numerous tennis matches.
Conclusions:
- Momentum significantly impacts psychological and behavioral performance in athletes.
- The developed model provides a quantitative approach to understanding and predicting momentum dynamics.
- Strategies for harnessing the 'hot hand' phenomenon and training suggestions based on momentum mechanisms are proposed.
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