Momentum Capture and Prediction System Based on Wimbledon Open2023 Tournament Data
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914899350781952 |
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| author | Liu, Chang Yang, Tongyuan Zhao, Yan |
| author_facet | Liu, Chang Yang, Tongyuan Zhao, Yan |
| contents | 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 accuracy (0.999013 for multiple matches and 0.992738 for finals). The model's ability to identify the influence of specific factors on match dynamics,such as bilateral distance run during points, demonstrates its prowess.The model's generalizability was thoroughly evaluated using datasets from the four Grand Slam tournaments. The results demonstrate its remarkable adaptability to different match scenarios,despite minor variations in predictive accuracy. It offers strategic insights that can help players effectively respond to opponents' shifts in momentum,enhancing their competitive edge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01544 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Momentum Capture and Prediction System Based on Wimbledon Open2023 Tournament Data Liu, Chang Yang, Tongyuan Zhao, Yan Machine Learning Applications 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 accuracy (0.999013 for multiple matches and 0.992738 for finals). The model's ability to identify the influence of specific factors on match dynamics,such as bilateral distance run during points, demonstrates its prowess.The model's generalizability was thoroughly evaluated using datasets from the four Grand Slam tournaments. The results demonstrate its remarkable adaptability to different match scenarios,despite minor variations in predictive accuracy. It offers strategic insights that can help players effectively respond to opponents' shifts in momentum,enhancing their competitive edge. |
| title | Momentum Capture and Prediction System Based on Wimbledon Open2023 Tournament Data |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2408.01544 |