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


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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    • "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.
    • "In the individual analysis of players , one of the first studies that used the graph theory was applied in the European Cup 2008 tournament . The study aimed to quantify the contributions of each player and the overall team performance ( Duch et al . , 2010 ) . That study used a network approach to identify the best individual performance of players and the best team performance ."
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    ABSTRACT: This study aimed to analyze the most prominent players' positions that contributed to the build of attack in football during FIFA World Cup 2014. The connections among teammates in all matches of the tournament were analyzed, and the tactical lineup and players' positions of players were codified as independent variables. Four centrality network metrics were used to identify the pertinence of each players' position. A total of 37,864 passes between teammates were recorded. Each national team was analyzed in terms of all their matches, thus all 64 matches from the FIFA World Cup 2014 tournament were analyzed and codified in this study. A total of 128 adjacency matrices and corresponding network graphs were generated and used to compute the centrality metrics. Results revealed that the players' position (p = 0.001; η2 p = 0.143; Power = 1.00; moderate effect size) showed significant main effects on centrality measures. The central midfielders possessed the main values in all centrality measures in the majority of analyzed tactical lineups. Therefore, this study showed that independent of the team strategy, the players' position of a central midfielder significantly contributed to the build of attack in football, for example, greater cooperation and activity profile.
    International Journal of Performance Analysis in Sport 08/2015; 15(2):704-722. · 0.80 Impact Factor
    • "Despite such evidence in social network analysis, the knowledge about the network characteristics that occur in team sports during a match is not solid (Passos et al., 2011). Some studies have been published in the last couple of years (Bourbousson et al., 2010; Duch et al., 2010; Grund, 2012), yet the multiple and disperse uses of the network metrics studied do not allow strong evidence on the importance of a specific kind of network characteristic to the overall performance of a team. "
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    ABSTRACT: This study analyzed the network characteristics of successful and unsuccessful national teams that participated in FIFA World Cup 2014. The relationship between the variables of overall team performance and the network characteristics measured on the basis of the passes between teammates was also investigated. A dataset of 37,864 passes between teammates in 64 soccer matches enabled the study on network structure and team performance of 32 national soccer teams. Our results showed significant differences in the dependent variables of network density (F4,123 = 2.72; p = 0.03; η 2p = 0.04; small effect size) and total links (F4,123 = 2.73; p = 0.03; η 2p = 0.04; small effect size) between the teams that reached the later stages of the tournament. Goals scored presented a small positive correlation with total links (r = 0.24; p = 0.001), network density (r = 0.24; p = 0.001), and clustering coefficient (r = 0.17; p > 0.050). High levels of goals scored were associated with high levels of total links, network density, and clustering coefficient. This study showed that successful teams have a high level of network density, total links, and clustering coefficient. Thus, large values of connectivity between teammates are associated with better overall team performance.
    International Journal of Performance Analysis in Sport 03/2015; 15(1). · 0.80 Impact Factor
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