Literature DB >> 28941634

Applying graphs and complex networks to football metric interpretation.

E Arriaza-Ardiles1, J M Martín-González2, M D Zuniga3, J Sánchez-Flores4, Y de Saa5, J M García-Manso5.   

Abstract

This work presents a methodology for analysing the interactions between players in a football team, from the point of view of graph theory and complex networks. We model the complex network of passing interactions between players of a same team in 32 official matches of the Liga de Fútbol Profesional (Spain), using a passing/reception graph. This methodology allows us to understand the play structure of the team, by analysing the offensive phases of game-play. We utilise two different strategies for characterising the contribution of the players to the team: the clustering coefficient, and centrality metrics (closeness and betweenness). We show the application of this methodology by analyzing the performance of a professional Spanish team according to these metrics and the distribution of passing/reception in the field. Keeping in mind the dynamic nature of collective sports, in the future we will incorporate metrics which allows us to analyse the performance of the team also according to the circumstances of game-play and to different contextual variables such as, the utilisation of the field space, the time, and the ball, according to specific tactical situations.
Copyright © 2017 Elsevier B.V. All rights reserved.

Keywords:  Betweenness centrality; Closeness centrality; Clustering; Football; Network; Passing

Mesh:

Year:  2017        PMID: 28941634     DOI: 10.1016/j.humov.2017.08.022

Source DB:  PubMed          Journal:  Hum Mov Sci        ISSN: 0167-9457            Impact factor:   2.161


  5 in total

1.  Using Network Science to Analyse Football Passing Networks: Dynamics, Space, Time, and the Multilayer Nature of the Game.

Authors:  Javier M Buldú; Javier Busquets; Johann H Martínez; José L Herrera-Diestra; Ignacio Echegoyen; Javier Galeano; Jordi Luque
Journal:  Front Psychol       Date:  2018-10-08

2.  Play-by-Play Network Analysis in Football.

Authors:  Florian Korte; Daniel Link; Johannes Groll; Martin Lames
Journal:  Front Psychol       Date:  2019-07-25

3.  A new model for predicting the winner in tennis based on the eigenvector centrality.

Authors:  Alberto Arcagni; Vincenzo Candila; Rosanna Grassi
Journal:  Ann Oper Res       Date:  2022-03-07       Impact factor: 4.854

4.  Analysis of the Football Transfer Market Network.

Authors:  Tobias Wand
Journal:  J Stat Phys       Date:  2022-04-19       Impact factor: 1.762

5.  Passing Networks and Tactical Action in Football: A Systematic Review.

Authors:  Sergio Caicedo-Parada; Carlos Lago-Peñas; Enrique Ortega-Toro
Journal:  Int J Environ Res Public Health       Date:  2020-09-11       Impact factor: 3.390

  5 in total

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