Rating Player Actions in Soccer

Uwe Dick; Maryam Tavakol; Ulf Brefeld

doi:10.3389/fspor.2021.682986

Rating Player Actions in Soccer

Uwe Dick
, Maryam Tavakol
, Ulf Brefeld^*

^*Corresponding author for this work

Professorship of Information Systems, in particular Machine Learning

Eindhoven University of Technology

Research output: Journal contributions › Journal articles › Research › peer-review

10 Citations (Scopus)

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.

Original language	English
Article number	682986
Journal	Frontiers in Sports and Active Living
Volume	3
Number of pages	14
ISSN	2642-9367
DOIs	https://doi.org/10.3389/fspor.2021.682986
Publication status	Published - 15.07.2021

Bibliographical note

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Research areas and keywords

graph networks
soccer
sports analytics
trajectory data
trajectory prediction
Informatics
Business informatics

ASJC Scopus Subject Areas

Physiology
Tourism, Leisure and Hospitality Management
Orthopedics and Sports Medicine
Anthropology
Public Health, Environmental and Occupational Health
Physical Therapy, Sports Therapy and Rehabilitation

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10.3389/fspor.2021.682986Licence: CC BY

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Rating Player Actions in Soccer
Dick, U. (Creator), Tavakol, M. (Creator) & Brefeld, U. (Creator), Medien- und Informationszentrum, Leuphana Universität Lüneburg, 19.11.2024
DOI: 10.48548/pubdata-1507
Dataset

Cite this

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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",

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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

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ER -

Rating Player Actions in Soccer

Abstract

Bibliographical note

UN SDGs

Research areas and keywords

ASJC Scopus Subject Areas

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Rating Player Actions in Soccer

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