2024-07-29-yan24b.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Prediction of Momentum in Tennis Using Random Forest Based on Bayesian Optimization and LSTM-ARIMA model	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.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	yan24b	0	Prediction of Momentum in Tennis Using Random Forest Based on Bayesian Optimization and LSTM-ARIMA model	308	315	308-315	308	false	Yan, Dong and Ruokai, Zhang and Yihang, Jin	given family Dong Yan given family Zhang Ruokai given family Jin Yihang	2024-07-29		Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing	245	inproceedings	date-parts 2024 7 29	https://raw.githubusercontent.com/mlresearch/v245/main/assets/yan24b/yan24b.pdf