Academic journal article Journal of Physical Education and Sport

The Social Network Analysis of Switzerland Football Team on FIFA World Cup 2014

Academic journal article Journal of Physical Education and Sport

The Social Network Analysis of Switzerland Football Team on FIFA World Cup 2014

Article excerpt

Introduction

The football is a complex and dynamic system that depends from the interactions of many agents (Gréhaigne, Bouthier, & David, 1997). Such interactions within a team can be considered as a cooperation process that emerges based on the strategic plan for the team, the situational variables and the contextual constraints (Davids, Araújo, & Shuttleworth, 2005). Despite of many dynamic factors that can change the strategic master plan for the team, usually there are some intrinsic patterns of cooperation that comes from weekly daily training sessions (Jonsson et al., 2006). Such patterns of play aims to provide some stability to the team's performance and organize the cooperation process of different teammates (Couceiro, Clemente, Martins, & Machado, 2014). In fact, the organization of different players is one of the main challenges for the coach in the game of football.

Besides to organize the team for different matches during a season, another interesting main role of coaches and their staff is to observe, analyze and understand the interactional process of players during the match trying to identify the strength and weakness points of the team (Filipe M Clemente, Couceiro, Fernando, Mendes, & Figueiredo, 2013). In that sense, the match analysis on football is one of the fundamental processes to provide relevant feedback for the coach in order to identify the team and players' properties (Carling, Williams, & Reilly, 2005; Hughes & Bartlett, 2002). The usual match analysis provides relevant information about time-motion profiles of players and the individual efficacy of football actions (Carling, Bloomfield, Nelsen, & Reilly, 2008; Hughes & Bartlett, 2002). Despite of these important indicators, the tactical information seems to be the weakest parameter of the regular match analysis systems (Vilar, Araújo, Davids, & Bar-Yam, 2013). In fact, such evidence can be explained by the complexity of analysis and by the difficulty to produce quantitative information about human behaviors.

Lately, new methodological approaches have been proposing new quantitative solutions to quantify the tactical behavior of teams (Travassos, Davids, Araújo, & Esteves, 2013). Some approaches use the Cartesian information of players' locations in the field to compute the spatio-temporal relationships. Such tactical metrics provides very interesting collective information in a online or offline fashion (Filipe M Clemente, Couceiro, Martins, Mendes, & Figueiredo, 2013). Nevertheless, those metrics depend from the information about players' position in the field, thus depending of multi-camera tracking systems or GPS (Global Positioning System) to work (Filipe Manuel Clemente, Couceiro, Martins, Mendes, & Figueiredo, 2014). Due to very expensive costs, this alternative is not an immediate solution for all clubs and contexts.

A different approach is a semi-computational solution that crosses the individual observation of some indicators (as passes between players) and then processes such data using specific algorithms to provide information about the collective interaction and reveal some interactional tendencies within the team (Lusher, Robins, & Kremer, 2010). Such approach is called by Social Network Analysis and is based on graph theory (Wasserman & Faust, 1994). By now the use of network approach for the study of football dynamics is only beginning (Grund, 2012). One of the first network applications used some individual metrics to determine the prominence of players during European Cup 2008 (Duch, Waitzman, & Amaral, 2010). Later, a crossed approach using individual and general metrics identified the most prominent player within the most successful teams in FIFA World Cup 2010 (Peña & Touchette, 2012). A similar analysis was also performed in such competition but with a greater focus on general teams properties (Cotta, Mora, Merelo, & Merelo-Molina, 2013). …

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