Difference between revisions of "Publications/duluard.22.mlsa"
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9th International Workshop, MLSA 2022<nowiki>}</nowiki>, |
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note = <nowiki>{</nowiki>Workshop co-located with ECMLPKDD'22<nowiki>}</nowiki>, |
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visualizations that enabled our expert to provide rapid |
visualizations that enabled our expert to provide rapid |
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feedback and hence provided us with guidance towards |
feedback and hence provided us with guidance towards |
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+ | further improvements of our discoveries<nowiki>}</nowiki>, |
| + | doi = <nowiki>{</nowiki>10.1007/978-3-031-27527-2_8<nowiki>}</nowiki> |
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Latest revision as of 19:07, 7 April 2023
- Authors
- Pierre Duluard, Xinqing Li, Marc Plantevit, Céline Robardet, Romain Vuillemot
- Where
- Machine Learning and Data Mining for Sports Analytics - 9th International Workshop, MLSA 2022
- Type
- inproceedings
- Keywords
- IA
- Date
- 2022-09-19
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},
month = sep,
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},
doi = {10.1007/978-3-031-27527-2_8}
}