Rating Player Actions in Soccer

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Rating Player Actions in Soccer. / Dick, Uwe; Tavakol, Maryam; Brefeld, Ulf.
In: Frontiers in Sports and Active Living, Vol. 3, 682986, 15.07.2021.

Research output: Journal contributionsJournal articlesResearchpeer-review

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Dick U, Tavakol M, Brefeld U. Rating Player Actions in Soccer. Frontiers in Sports and Active Living. 2021 Jul 15;3:682986. doi: 10.3389/fspor.2021.682986

Bibtex

@article{5e7866ce44cf4d4caf5bbe6296bef8d2,
title = "Rating Player Actions in Soccer",
abstract = "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.",
keywords = "graph networks, soccer, sports analytics, trajectory data, trajectory prediction, Informatics, Business informatics",
author = "Uwe Dick and Maryam Tavakol and Ulf Brefeld",
note = "This publication was funded by the Open Access Publication Fund of Leuphana University L{\"u}neburg.",
year = "2021",
month = jul,
day = "15",
doi = "10.3389/fspor.2021.682986",
language = "English",
volume = "3",
journal = "Frontiers in Sports and Active Living",
issn = "2642-9367",
publisher = "Frontiers Research Foundation",

}

RIS

TY - JOUR

T1 - Rating Player Actions in Soccer

AU - Dick, Uwe

AU - Tavakol, Maryam

AU - Brefeld, Ulf

N1 - This publication was funded by the Open Access Publication Fund of Leuphana University Lüneburg.

PY - 2021/7/15

Y1 - 2021/7/15

N2 - 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.

AB - 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.

KW - graph networks

KW - soccer

KW - sports analytics

KW - trajectory data

KW - trajectory prediction

KW - Informatics

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=85124574199&partnerID=8YFLogxK

U2 - 10.3389/fspor.2021.682986

DO - 10.3389/fspor.2021.682986

M3 - Journal articles

C2 - 34337404

AN - SCOPUS:85124574199

VL - 3

JO - Frontiers in Sports and Active Living

JF - Frontiers in Sports and Active Living

SN - 2642-9367

M1 - 682986

ER -

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