Abstract
We report preliminary results to automatically identify effective tactics of elite table tennis players. We define these tactics as subgroups of winning strokes that table tennis experts seek to identify in order to train players and adapt their strategy during play. We first report how we identify and classify these subgroups using the weighted relative accuracy measure (WRAcc). We then present the subgroups using visualizations to communicate these results to our expert. These exchanges allow rapid feedback on our results and makes it possible further improvements to our discoveries.
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Acknowledgment
This project was partially funded by Action Transversale at LIRIS Lab. We thank Table Tennis National partner for the annotated game they provided us and their time to provide us with feedback on the results. We also thank the reviewers for their thoughtful comments.
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Duluard, P., Li, X., Plantevit, M., Robardet, C., Vuillemot, R. (2023). Discovering and Visualizing Tactics in a Table Tennis Game Based on Subgroup Discovery. In: Brefeld, U., Davis, J., Van Haaren, J., Zimmermann, A. (eds) Machine Learning and Data Mining for Sports Analytics. MLSA 2022. Communications in Computer and Information Science, vol 1783. Springer, Cham. https://doi.org/10.1007/978-3-031-27527-2_8
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