Journal's Facebook pageDeep Learning-Driven Decision Tree Ensembles for Table Tennis: Analyzing Serve Strategies and First-Three-Stroke Outcomes
- Department of Recreational Sport, National Taiwan University of Sport, No. 16, Sec. 1
Shuangshi Rd., North Dist., Taichung City, 404401 Taiwan (R.O.C.)
chewei@gm.ntus.edu.tw, 60931ponpon@gmail.com - Department of Sport Information and Communication, National Taiwan University of Sport
No. 16, Sec. 1, Shuangshi Rd., North Dist., Taichung City, 404401 Taiwan (R.O.C.)
harvestpaleale@gmail.com, liujingwei.ntus@gmail.com
Abstract
This paper presents a novel artificial intelligence system that integrates deep learning-driven decision tree ensemble algorithms (DLDDTEA) for table tennis match analysis. By analyzing videos of professional matches featuring Lin Yun-Ju and Ma Long, the system extracts key insights into player techniques, hitting positions, and scoring outcomes. DLDDTEA processes the video data and constructs a predictive model to determine optimal serve positions and estimate point win/loss probabilities within the first three exchanges. The results revealed distinct serve strategies and techniques: Lin Yun-Ju favors backhands, whereas Ma Long prefers forehands. Based on these findings, this study offers specific training and strategic recommendations for both players. Thus, the proposed system offers a comprehensive framework for table tennis match analysis, enabling players to gain a deeper understanding of their strengths and weaknesses, ultimately facilitating the development of more effective training and competitive strategies.
Key words
deep learning, decision tree, video analysis, table tennis match model, notational analysis, convolutional neural networks
How to cite
Chang, C., Chen, S., Chen, P., Liu, J.: Deep Learning-Driven Decision Tree Ensembles for Table Tennis: Analyzing Serve Strategies and First-Three-Stroke Outcomes. Computer Science and Information Systems
