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Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: An application to rugby union

Given a set of sequences comprised of time-ordered events, sequential pattern mining is useful to identify frequent subsequences from different sequences or within the same sequence. However, in sport, these techniques cannot determine the importance of particular patterns of play to good or bad out...

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Autores principales: Bunker, Rory, Fujii, Keisuke, Hanada, Hiroyuki, Takeuchi, Ichiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8460049/
https://www.ncbi.nlm.nih.gov/pubmed/34555042
http://dx.doi.org/10.1371/journal.pone.0256329
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author Bunker, Rory
Fujii, Keisuke
Hanada, Hiroyuki
Takeuchi, Ichiro
author_facet Bunker, Rory
Fujii, Keisuke
Hanada, Hiroyuki
Takeuchi, Ichiro
author_sort Bunker, Rory
collection PubMed
description Given a set of sequences comprised of time-ordered events, sequential pattern mining is useful to identify frequent subsequences from different sequences or within the same sequence. However, in sport, these techniques cannot determine the importance of particular patterns of play to good or bad outcomes, which is often of greater interest to coaches and performance analysts. In this study, we apply a recently proposed supervised sequential pattern mining algorithm called safe pattern pruning (SPP) to 490 labelled event sequences representing passages of play from one rugby team’s matches in the 2018 Japan Top League season. We obtain patterns that are the most discriminative between scoring and non-scoring outcomes from both the team’s and opposition teams’ perspectives using SPP, and compare these with the most frequent patterns obtained with well-known unsupervised sequential pattern mining algorithms when applied to subsets of the original dataset, split on the label. From our obtained results, line breaks, successful line-outs, regained kicks in play, repeated phase-breakdown play, and failed exit plays by the opposition team were found to be the patterns that discriminated most between the team scoring and not scoring. Opposition team line breaks, errors made by the team, opposition team line-outs, and repeated phase-breakdown play by the opposition team were found to be the patterns that discriminated most between the opposition team scoring and not scoring. It was also found that, probably because of the supervised nature and pruning/safe-screening mechanisms of SPP, compared to the patterns obtained by the unsupervised methods, those obtained by SPP were more sophisticated in terms of containing a greater variety of events, and when interpreted, the SPP-obtained patterns would also be more useful for coaches and performance analysts.
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spelling pubmed-84600492021-09-24 Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: An application to rugby union Bunker, Rory Fujii, Keisuke Hanada, Hiroyuki Takeuchi, Ichiro PLoS One Research Article Given a set of sequences comprised of time-ordered events, sequential pattern mining is useful to identify frequent subsequences from different sequences or within the same sequence. However, in sport, these techniques cannot determine the importance of particular patterns of play to good or bad outcomes, which is often of greater interest to coaches and performance analysts. In this study, we apply a recently proposed supervised sequential pattern mining algorithm called safe pattern pruning (SPP) to 490 labelled event sequences representing passages of play from one rugby team’s matches in the 2018 Japan Top League season. We obtain patterns that are the most discriminative between scoring and non-scoring outcomes from both the team’s and opposition teams’ perspectives using SPP, and compare these with the most frequent patterns obtained with well-known unsupervised sequential pattern mining algorithms when applied to subsets of the original dataset, split on the label. From our obtained results, line breaks, successful line-outs, regained kicks in play, repeated phase-breakdown play, and failed exit plays by the opposition team were found to be the patterns that discriminated most between the team scoring and not scoring. Opposition team line breaks, errors made by the team, opposition team line-outs, and repeated phase-breakdown play by the opposition team were found to be the patterns that discriminated most between the opposition team scoring and not scoring. It was also found that, probably because of the supervised nature and pruning/safe-screening mechanisms of SPP, compared to the patterns obtained by the unsupervised methods, those obtained by SPP were more sophisticated in terms of containing a greater variety of events, and when interpreted, the SPP-obtained patterns would also be more useful for coaches and performance analysts. Public Library of Science 2021-09-23 /pmc/articles/PMC8460049/ /pubmed/34555042 http://dx.doi.org/10.1371/journal.pone.0256329 Text en © 2021 Bunker et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Bunker, Rory
Fujii, Keisuke
Hanada, Hiroyuki
Takeuchi, Ichiro
Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: An application to rugby union
title Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: An application to rugby union
title_full Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: An application to rugby union
title_fullStr Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: An application to rugby union
title_full_unstemmed Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: An application to rugby union
title_short Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: An application to rugby union
title_sort supervised sequential pattern mining of event sequences in sport to identify important patterns of play: an application to rugby union
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8460049/
https://www.ncbi.nlm.nih.gov/pubmed/34555042
http://dx.doi.org/10.1371/journal.pone.0256329
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