Rating Player Actions in Soccer.
graph networks
soccer
sports analytics
trajectory data
trajectory prediction
Journal
Frontiers in sports and active living
ISSN: 2624-9367
Titre abrégé: Front Sports Act Living
Pays: Switzerland
ID NLM: 101765780
Informations de publication
Date de publication:
2021
2021
Historique:
received:
19
03
2021
accepted:
26
05
2021
entrez:
2
8
2021
pubmed:
3
8
2021
medline:
3
8
2021
Statut:
epublish
Résumé
We present a data-driven model that rates actions of the player in soccer with respect to their contribution to ball possession phases. This study approach consists of two interconnected parts: (i) a trajectory prediction model that is learned from real tracking data and predicts movements of players and (ii) a prediction model for the outcome of a ball possession phase. Interactions between players and a ball are captured by a graph recurrent neural network (GRNN) and we show empirically that the network reliably predicts both, player trajectories as well as outcomes of ball possession phases. We derive a set of aggregated performance indicators to compare players with respect to. to their contribution to the success of their team.
Identifiants
pubmed: 34337404
doi: 10.3389/fspor.2021.682986
pmc: PMC8319236
doi:
Types de publication
Journal Article
Langues
eng
Pagination
682986Informations de copyright
Copyright © 2021 Dick, Tavakol and Brefeld.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Références
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