Discovering and visualizing tactics in a table tennis game based on subgroup discovery
(Entdeckung und Visualisierung von Taktiken in einem Tischtennisspiel auf der Grundlage der Entdeckung von Untergruppen)
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.
© Copyright 2022 Machine Learning and Data Mining for Sports Analytics. MLSA 2022. Communications in Computer and Information Science. Veröffentlicht von Springer. Alle Rechte vorbehalten.
| Schlagworte: | Tischtennis Taktik Wettkampf Visualisierung Analyse |
|---|---|
| Notationen: | Naturwissenschaften und Technik Spielsportarten |
| Tagging: | data mining |
| DOI: | 10.1007/978-3-031-27527-2_8 |
| Veröffentlicht in: | Machine Learning and Data Mining for Sports Analytics. MLSA 2022. Communications in Computer and Information Science |
| Herausgeber: | U. Brefeld, J. Davis, J. Van Haaren, A. Zimmermann |
| Veröffentlicht: |
Cham
Springer
2022
|
| Schriftenreihe: |
Communications in Computer and Information Science, 1783 |
| Seiten: | 101-112 |
| Dokumentenarten: | Artikel |
| Sprache: | Englisch |
| Level: | hoch |