Geodesic
Deep Learning-Driven Decision Tree Ensembles for Table Tennis: Analyzing Serve Strategies and First-Three-Stroke Outcomes
Computer Science and Information Systems, Tome 22 (2025) no. 3

Voir la notice de l'article provenant de la source Computer Science and Information Systems website

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.
Keywords: deep learning, decision tree, video analysis, table tennis match model, notational analysis, convolutional neural networks 
Che-Wei Chang; Sheng-Hsiang Chen; Peng-Yu Chen; Jing-Wei Liu. Deep Learning-Driven Decision Tree Ensembles for Table Tennis: Analyzing Serve Strategies and First-Three-Stroke Outcomes. Computer Science and Information Systems, Tome 22 (2025) no. 3. http://geodesic.mathdoc.fr/item/CSIS_2025_22_3_a18/
@article{CSIS_2025_22_3_a18,
     author = {Che-Wei Chang and Sheng-Hsiang Chen and Peng-Yu Chen and Jing-Wei Liu},
     title = {Deep {Learning-Driven} {Decision} {Tree} {Ensembles} for {Table} {Tennis:} {Analyzing} {Serve} {Strategies} and {First-Three-Stroke} {Outcomes}},
     journal = {Computer Science and Information Systems},
     year = {2025},
     volume = {22},
     number = {3},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2025_22_3_a18/}
}
TY  - JOUR
AU  - Che-Wei Chang
AU  - Sheng-Hsiang Chen
AU  - Peng-Yu Chen
AU  - Jing-Wei Liu
TI  - Deep Learning-Driven Decision Tree Ensembles for Table Tennis: Analyzing Serve Strategies and First-Three-Stroke Outcomes
JO  - Computer Science and Information Systems
PY  - 2025
VL  - 22
IS  - 3
UR  - http://geodesic.mathdoc.fr/item/CSIS_2025_22_3_a18/
ID  - CSIS_2025_22_3_a18
ER  - 
%0 Journal Article
%A Che-Wei Chang
%A Sheng-Hsiang Chen
%A Peng-Yu Chen
%A Jing-Wei Liu
%T Deep Learning-Driven Decision Tree Ensembles for Table Tennis: Analyzing Serve Strategies and First-Three-Stroke Outcomes
%J Computer Science and Information Systems
%D 2025
%V 22
%N 3
%U http://geodesic.mathdoc.fr/item/CSIS_2025_22_3_a18/
%F CSIS_2025_22_3_a18