Discovering and Visualizing Tactics in a Table Tennis Game Based on Subgroup Discovery

被引：3

作者：

Duluard, Pierre ^{[1
]}

Li, Xinqing ^{[2
]}

Plantevit, Marc ^{[3
]}

Robardet, Celine ^{[2
]}

Vuillemot, Romain ^{[1
]}

机构：

[1] Univ Lyon 2, UMR5205, LIRIS, Univ Lyon,Cent Lyon,CNRS,INSA Lyon,UCBL, F-69130 Ecully, France

[2] Univ Lyon, CNRS, INSA Lyon, UCBL,LIRIS,UMR5205, F-69621 Villeurbanne, France

[3] Lab Rech EPITA LRE, F-94276 Paris, France

来源：

MACHINE LEARNING AND DATA MINING FOR SPORTS ANALYTICS, MLSA 2022 | 2023年 / 1783卷

关键词：

Data mining; Sports data visualization; Table tennis;

D O I：

10.1007/978-3-031-27527-2_8

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：101 / 112

页数：12

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