Momentum Capture and Prediction System Based on Wimbledon Open2023 Tournament Data
Chang Liu, Tongyuan Yang, Yan Zhao

TL;DR
This paper presents a novel evaluation and predictive model for tennis momentum, combining entropy-based analysis and machine learning to accurately forecast match swings and understand momentum's role in match outcomes.
Contribution
It introduces an integrated evaluation model for tennis momentum and a highly accurate predictive system using XGBoost and SHAP, validated across major tournaments.
Findings
Momentum significantly influences match outcomes.
The predictive model achieves over 99% accuracy.
The model adapts well across different Grand Slam tournaments.
Abstract
There is a hidden energy in tennis, which cannot be seen or touched. It is the force that controls the flow of the game and is present in all types of matches. This mysterious force is Momentum. This study introduces an evaluation model that synergizes the Entropy Weight Method (EWM) and Gray Relation Analysis (GRA) to quantify momentum's impact on match outcomes. Empirical validation was conducted through Mann-Whitney U and Kolmogorov-Smirnov tests, which yielded p values of 0.0043 and 0.00128,respectively. These results underscore the non-random association between momentum shifts and match outcomes, highlighting the critical role of momentum in tennis. Otherwise, our investigation foucus is the creation of a predictive model that combines the advanced machine learning algorithm XGBoost with the SHAP framework. This model enables precise predictions of match swings with exceptional…
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Taxonomy
TopicsSports Analytics and Performance · Video Analysis and Summarization
MethodsShapley Additive Explanations
