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Play-by-Play Network Analysis in Football

This study identifies dominant and intermediary players in football by applying a play-by-play social network analysis (SNA) on 70 professional matches from the 1. and 2. German Bundesliga during the 2017/2018 season. SNA provides a quantification of the complex interaction patterns between players...

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Autores principales: Korte, Florian, Link, Daniel, Groll, Johannes, Lames, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6669815/
https://www.ncbi.nlm.nih.gov/pubmed/31402892
http://dx.doi.org/10.3389/fpsyg.2019.01738
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author Korte, Florian
Link, Daniel
Groll, Johannes
Lames, Martin
author_facet Korte, Florian
Link, Daniel
Groll, Johannes
Lames, Martin
author_sort Korte, Florian
collection PubMed
description This study identifies dominant and intermediary players in football by applying a play-by-play social network analysis (SNA) on 70 professional matches from the 1. and 2. German Bundesliga during the 2017/2018 season. SNA provides a quantification of the complex interaction patterns between players in team sports. So far, the individual contributions and roles of players in football have only been studied at match-level considering the overall passing of a team. In order to consider the real structure of football, a play-by-play network analysis is needed that reflects actual interplay. Moreover, a distinction between plays of certain characteristics is important to qualify different interaction phases. As it is often impossible to calculate well known network metrics such as betweenness on play-level, new adequate metrics are required. Therefore, flow betweenness is introduced as a new playmaker indicator on play-level and computed alongside flow centrality. The data on passing and the position of players was provided by the Deutsche Fußball Liga (DFL) and gathered through a semi-automatic multiple-camera tracking system. Central defenders are identified as dominant and intermediary players, however, mostly in unsuccessful plays. Offensive midfielders are most involved and defensive midfielders are the main intermediary players in successful plays. Forward are frequently involved in successful plays but show negligible playmaker status. Play-by-play network analysis facilitates a better understanding of the role of players in football interaction.
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spelling pubmed-66698152019-08-09 Play-by-Play Network Analysis in Football Korte, Florian Link, Daniel Groll, Johannes Lames, Martin Front Psychol Psychology This study identifies dominant and intermediary players in football by applying a play-by-play social network analysis (SNA) on 70 professional matches from the 1. and 2. German Bundesliga during the 2017/2018 season. SNA provides a quantification of the complex interaction patterns between players in team sports. So far, the individual contributions and roles of players in football have only been studied at match-level considering the overall passing of a team. In order to consider the real structure of football, a play-by-play network analysis is needed that reflects actual interplay. Moreover, a distinction between plays of certain characteristics is important to qualify different interaction phases. As it is often impossible to calculate well known network metrics such as betweenness on play-level, new adequate metrics are required. Therefore, flow betweenness is introduced as a new playmaker indicator on play-level and computed alongside flow centrality. The data on passing and the position of players was provided by the Deutsche Fußball Liga (DFL) and gathered through a semi-automatic multiple-camera tracking system. Central defenders are identified as dominant and intermediary players, however, mostly in unsuccessful plays. Offensive midfielders are most involved and defensive midfielders are the main intermediary players in successful plays. Forward are frequently involved in successful plays but show negligible playmaker status. Play-by-play network analysis facilitates a better understanding of the role of players in football interaction. Frontiers Media S.A. 2019-07-25 /pmc/articles/PMC6669815/ /pubmed/31402892 http://dx.doi.org/10.3389/fpsyg.2019.01738 Text en Copyright © 2019 Korte, Link, Groll and Lames. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Korte, Florian
Link, Daniel
Groll, Johannes
Lames, Martin
Play-by-Play Network Analysis in Football
title Play-by-Play Network Analysis in Football
title_full Play-by-Play Network Analysis in Football
title_fullStr Play-by-Play Network Analysis in Football
title_full_unstemmed Play-by-Play Network Analysis in Football
title_short Play-by-Play Network Analysis in Football
title_sort play-by-play network analysis in football
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6669815/
https://www.ncbi.nlm.nih.gov/pubmed/31402892
http://dx.doi.org/10.3389/fpsyg.2019.01738
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