Prediction of Momentum in Tennis Using Random Forest Based on Bayesian Optimization and LSTM-ARIMA model

Dong Yan; Zhang Ruokai; Jin Yihang

Prediction of Momentum in Tennis Using Random Forest Based on Bayesian Optimization and LSTM-ARIMA model

Dong Yan, Zhang Ruokai, Jin Yihang

Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, PMLR 245:308-315, 2024.

Abstract

Athletes’ performances are often inﬂuenced by an intangible factor, momentum, which reﬂects the ability to perform exceptionally or consistently at a speciﬁc moment. Our model quantiﬁes momentum and predicts match win rates, aiding athletes and coaches in optimizing their game strategies. We analyzed factors such as break points and winning streaks, employing a Random Forest Model to evaluate momentum’s inﬂuence. Through the SHAP model, we established a quantiﬁable relationship with momentum and considered previous momentum using exponential weighted moving averages (EWMA). We developed a Gaussian Distribution Maximum Distance (GDMD) Threshold and utilized an LSTM-ARIMA model to predict momentum differences and identify turning points. The most critical factors were winning break points, running distance, and runs of success. Players are advised to be aware of their opponents’ turning points and conserve energy to break them. Potential improvements include considering external factors like audience impact and expected goals, as well as incorporating more data to enhance model generalization capability.

Cite this Paper

BibTeX


@InProceedings{pmlr-v245-yan24b,
  title = 	 {Prediction of Momentum in Tennis Using Random Forest Based on Bayesian Optimization and LSTM-ARIMA model},
  author =       {Yan, Dong and Ruokai, Zhang and Yihang, Jin},
  booktitle = 	 {Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing},
  pages = 	 {308--315},
  year = 	 {2024},
  editor = 	 {Nianyin, Zeng and Pachori, Ram Bilas},
  volume = 	 {245},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {26--28 Apr},
  publisher =    {PMLR},
  pdf = 	 {https://raw.githubusercontent.com/mlresearch/v245/main/assets/yan24b/yan24b.pdf},
  url = 	 {https://proceedings.mlr.press/v245/yan24b.html},
  abstract = 	 {Athletes’ performances are often inﬂuenced by an intangible factor, momentum, which reﬂects the ability to perform exceptionally or consistently at a speciﬁc moment. Our model quantiﬁes momentum and predicts match win rates, aiding athletes and coaches in optimizing their game strategies. We analyzed factors such as break points and winning streaks, employing a Random Forest Model to evaluate momentum’s inﬂuence. Through the SHAP model, we established a quantiﬁable relationship with momentum and considered previous momentum using exponential weighted moving averages (EWMA). We developed a Gaussian Distribution Maximum Distance (GDMD) Threshold and utilized an LSTM-ARIMA model to predict momentum differences and identify turning points. The most critical factors were winning break points, running distance, and runs of success. Players are advised to be aware of their opponents’ turning points and conserve energy to break them. Potential improvements include considering external factors like audience impact and expected goals, as well as incorporating more data to enhance model generalization capability.}
}

Endnote

%0 Conference Paper
%T Prediction of Momentum in Tennis Using Random Forest Based on Bayesian Optimization and LSTM-ARIMA model
%A Dong Yan
%A Zhang Ruokai
%A Jin Yihang
%B Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing
%C Proceedings of Machine Learning Research
%D 2024
%E Zeng Nianyin
%E Ram Bilas Pachori	
%F pmlr-v245-yan24b
%I PMLR
%P 308--315
%U https://proceedings.mlr.press/v245/yan24b.html
%V 245
%X Athletes’ performances are often inﬂuenced by an intangible factor, momentum, which reﬂects the ability to perform exceptionally or consistently at a speciﬁc moment. Our model quantiﬁes momentum and predicts match win rates, aiding athletes and coaches in optimizing their game strategies. We analyzed factors such as break points and winning streaks, employing a Random Forest Model to evaluate momentum’s inﬂuence. Through the SHAP model, we established a quantiﬁable relationship with momentum and considered previous momentum using exponential weighted moving averages (EWMA). We developed a Gaussian Distribution Maximum Distance (GDMD) Threshold and utilized an LSTM-ARIMA model to predict momentum differences and identify turning points. The most critical factors were winning break points, running distance, and runs of success. Players are advised to be aware of their opponents’ turning points and conserve energy to break them. Potential improvements include considering external factors like audience impact and expected goals, as well as incorporating more data to enhance model generalization capability.

APA


Yan, D., Ruokai, Z. & Yihang, J.. (2024). Prediction of Momentum in Tennis Using Random Forest Based on Bayesian Optimization and LSTM-ARIMA model. Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 245:308-315 Available from https://proceedings.mlr.press/v245/yan24b.html.

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