Discovering and Visualizing Tactics in Table Tennis Games Based on Subgroup Discovery

From LRDE

Abstract

We report on preliminary results to automatically identify efficient tactics of elite players in table tennis games. We define such tactics as subgroups of winning strokes which table tennis experts sought to obtain to train players and adapt their strategy during games. We first report on the creation of such subgroups and their ranking by weighted relative accuracy measure (WRAcc). We then report on representation of the subgroups using visualizations that enabled our expert to provide rapid feedback and hence provided us with guidance towards further improvements of our discoveries


Bibtex (lrde.bib)

@InProceedings{	  duluard.22.mlsa,
  title		= {Discovering and Visualizing Tactics in Table Tennis Games
		  Based on Subgroup Discovery},
  author	= {Pierre Duluard and Xinqing Li and Marc Plantevit and
		  C\'eline Robardet and Romain Vuillemot},
  booktitle	= {Machine Learning and Data Mining for Sports Analytics -
		  9th International Workshop, MLSA 2022},
  year		= {2022},
  note		= {Workshop co-located with ECMLPKDD'22},
  abstract	= {We report on preliminary results to automatically identify
		  efficient tactics of elite players in table tennis games.
		  We define such tactics as subgroups of winning strokes
		  which table tennis experts sought to obtain to train
		  players and adapt their strategy during games. We first
		  report on the creation of such subgroups and their ranking
		  by weighted relative accuracy measure (WRAcc). We then
		  report on representation of the subgroups using
		  visualizations that enabled our expert to provide rapid
		  feedback and hence provided us with guidance towards
		  further improvements of our discoveries}
}