Article

Quantifying the Performance of Individual Players in a Team Activity

Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois, United States of America.
PLoS ONE (Impact Factor: 3.23). 06/2010; 5(6):e10937. DOI: 10.1371/journal.pone.0010937
Source: PubMed

ABSTRACT

Teamwork is a fundamental aspect of many human activities, from business to art and from sports to science. Recent research suggest that team work is of crucial importance to cutting-edge scientific research, but little is known about how teamwork leads to greater creativity. Indeed, for many team activities, it is not even clear how to assign credit to individual team members. Remarkably, at least in the context of sports, there is usually a broad consensus on who are the top performers and on what qualifies as an outstanding performance.
In order to determine how individual features can be quantified, and as a test bed for other team-based human activities, we analyze the performance of players in the European Cup 2008 soccer tournament. We develop a network approach that provides a powerful quantification of the contributions of individual players and of overall team performance.
We hypothesize that generalizations of our approach could be useful in other contexts where quantification of the contributions of individual team members is important.

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    • "For example, Gould and Gatrell (1979) investigated passes between soccer players in the 1977 final between Liverpool and Manchester United. Duch et al. (2010) examined passes between players in matches of the European Cup 2008 soccer tournament to assess individual performance and Grund (2012) studied similar networks to link interaction patterns with team performance. I use a unique dataset of the English Premier League (EPL) – the top division of the English soccer system. "
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    ABSTRACT: Previous studies indicate the importance of experience for the performance of teams. Theory suggests that working together allows individuals to 1) improve their knowledge about who knows (and can do) what and 2) facilitates learning to combine individual resources efficiently. Yet, it remains elusive how experience translates into interaction patterns in teams. Drawing on unique data of career histories of 800 players and 283259 passes between these players in 760 English Premier League soccer matches, I propose a new measure for network experience and demonstrate how it relates to network intensity and decentralization in teams. Soccer teams exhibit a higher passing rate when players know each other from before. At the same time, network experience has no effect on the decentralization of team play. Further dyadic analyses confirm these findings. Controlling for selection effects, two players are more likely to pass the ball between each other when they know each other more. Network experience affects the way team members interact and ultimately leads to performance outcomes.
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    • "A general feature of this type of network is that a few players will tend to exhibit more links between themselves than other players will (Gama et al., 2014). Consequently, because a football team has been conceptualized as a complex, self-organising system, the number of links between players tends to display a power law distribution (Duch et al., 2010). "
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    ABSTRACT: The aim of this study was to verify whether interactions taking place between professional football players are compatible with the concept of small world networks. We observed 30 matches and analysed 7.583 collective offensive actions, since the beginning of possession of the ball to their loss, including: passes completed, passes received and crosses, involving a total of 22.518 intrateam interactions in the Portuguese Premier League, corresponding to all 2010/2011 season. The players were classified based on their tactical intervention region and movements, through four sectors: 1) goalkeepers; 2) defenders; 3) midfielders, and 4) forwards. Performance data was analysed using the Match Analysis Software Amisco® (version 3.3.7.25). We analysed the relevant actions typically used during offensive phases, including: passes to teammates, crosses into the penalty box and ball receptions. The results suggest that players’ interactive behaviours within a football match support the existence of a scale free network. Defenders and midfielders are the athletes presenting the highest level of connectivity with their teammates. It was concluded that network analysis might be useful to shed some light on the individual contributions to the collective team performance and provide insights on how creative and organizing individuals might act to orchestrate team strategies. This suggests that the proposed methodology can be used to characterize the collective behaviours that emerge through cooperation and competition between players during football matches.
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    • "entirely the strategy of the players and teams. For example , in case of soccer, the average number of shots, goals, fouls, passes are derived both for the teams and the players [2]. The systems are able to identify and evaluate the outcome of the strategies but are reluctant to extract the key components of the strategies that lead to these metered outcomes [8] [5] [4]. "
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    ABSTRACT: Technology offers new ways to measure the locations of the players and of the ball in sports. This translates to the trajectories the ball takes on the field as a result of the tactics the team applies. The challenge professionals in soccer are facing is to take the reverse path: given the trajectories of the ball is it possible to infer the underlying strategy/tactic of a team? We propose a method based on Dynamic Time Warping to reveal the tactics of a team through the analysis of repeating series of events. Based on the analysis of an entire season, we derive insights such as passing strategies for maintaining ball possession or counter attacks, and passing styles with a focus on the team or on the capabilities of the individual players.
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