Complex network study of Brazilian soccer players

Onody, Roberto Nicolau; Castro, Paulo Alexandre de

doi:10.1103/physreve.70.037103

Cited by 80 publications

(54 citation statements)

References 19 publications

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“…Because of its popularity and widespread diffusion in the media, an abundant source of observational data is accessible. Previous works have dealt mainly with macroscopic features measured over ensembles of matches (cups or championships) [1,2,3,4], such as the statistics of goals. Meanwhile, the present goal is to characterize a microscopic dynamics throughout each match.…”

Section: Introductionmentioning

confidence: 99%

Statistics of football dynamics

2007

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We investigate the dynamics of football matches. Our goal is to characterize statistically the temporal sequence of ball movements in this collective sport game, searching for traits of complex behavior. Data were collected over a variety of matches in South American, European and World championships throughout 2005 and 2006. We show that the statistics of ball touches presents powerlaw tails and can be described by q-gamma distributions. To explain such behavior we propose a model that provides information on the characteristics of football dynamics. Furthermore, we discuss the statistics of duration of out-of-play intervals, not directly related to the previous scenario.PACS numbers: 02.50.-r, 89.90.+n 01.80.+b,

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Section: Introductionmentioning

confidence: 99%

Statistics of football dynamics

2007

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“…A Bowler Dismissal Network (BoDN) is achieved if two bowlers dismiss the same batsman. Similar approach has been adopted in case of collaboration networks [56,57], actor − movies [35], director − firm [52], scientist − paper [53] and also soccer − clubs [17]. For Test cricket the BoDN has 3082 nodes and 490355 edges with a high clustering coefficient C = 0.73, whereas its randomization yields C = 0.10.…”

Section: Appendixmentioning

confidence: 99%

“…Such as a network approach was developed to quantify the performance of individual players in soccer [14] and football [15]. Network analysis tools have been applied to football [16], Brazilian soccer players [17], Asian Go players [18]. Successful and un-successful performance in water polo have been quantified using a network-based approach [19].…”

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confidence: 99%

Complex Network Analysis in Cricket: Community Structure, Player's Role and Performance Index

Mukherjee

2013

Advs. Complex Syst.

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This paper describes the applications of network methods for understanding interaction within members of sport teams.We analyze the interaction of batsmen in International Cricket matches. We generate batting partnership network (BPN) for different teams and determine the exact values of clustering coefficient, average degree, average shortest path length of the networks and compare them with the Erdös-Rényi model. We observe that the networks display small-world behavior. We find that most connected batsman is not necessarily the most central and most central players are not necessarily the one with high batting averages. We study the community structure of the BPNs and identify each player's role based on inter-community and intra-community links. We observe that Sir DG Bradman, regarded as the best batsman in Cricket history does not occupy the central position in the network − the so-called connector hub. We extend our analysis to quantify the performance, relative importance and effect of removing a player from the team, based on different centrality scores.Keywords: Complex network; Small world behavior; Centrality scores; Cricket. IntroductionIn recent years there has been an increase in study of activities involving team sports. Time series analysis have been applied to football [1,2], baseball [3,4], basketball [5,6,7] and soccer [8,9]. Again, a model-free approach was developed to extract the outcome of a soccer match [10]. The study of complex networks have attracted a lot of research interests in the recent years [11,12]. A salient feature of such complex networks is that they display small-world behavior [13]. The tools of complex network analysis have previously been applied to sports. Such as a network approach was developed to quantify the performance of individual players in soccer [14] and football [15]. Network analysis tools have been applied to football [16], Brazilian soccer players [17], Asian Go players [18]. Successful and un-successful performance in water polo have been quantified using a network-based approach [19]. Head-to-head matchups between Major League Baseball pitchers and batters was studied as a bipartite network [20]. More recently a network-based approach was developed to rank US college football teams [21] The complex features of numerous social systems are embedded in the inherent connectivity among system components [11,19]. Social network analysis (SNA) allow researchers to explore the intra-group and inter-group relations between players, thus providing an informal relation between various players. Such an analysis provides insight about the pattern of interaction among players and how it affects the success of a team. This article points out that topological relations between players need to be explored in order to better understand individuals who play for their teams. SNA is well suited to investigate the complex relations between team members [24]. Such an approach to cricket at the microscopic level, form a basis of elucidating the individual importance and impact...

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“…Other works are focused in predicting the outcome of sporting contests [12,13,14]. There are also investigations of the soccer goal distribution [15,16,17,18,19], the existence of a home advantage [20], the persistence in sequences of match results [21], the temporal sequence of ball movements [22] and the network of Brazilian soccer players [23]. As we a hvr@dfi.uem.br can see, the focus of these works is not to investigate the supposedly universal features of soccer tournaments.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of tournaments: the soccer case

et al. 2010

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Abstract. A random walk-like model is considered to discuss statistical aspects of tournaments. The model is applied to soccer leagues with emphasis on the scores. This competitive system was computationally simulated and the results are compared with empirical data from the English, the German and the Spanish leagues and showed a good agreement with them. The present approach enabled us to characterize a diffusion where the scores are not normally distributed, having a short and asymmetric tail extending towards more positive values. We argue that this non-Gaussian behavior is related with the difference between the teams and with the asymmetry of the scores system. In addition, we compared two tournament systems: the all-play-all and the elimination tournaments.

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Complex network study of Brazilian soccer players

Cited by 80 publications

References 19 publications

Statistics of football dynamics

Statistics of football dynamics

Complex Network Analysis in Cricket: Community Structure, Player's Role and Performance Index

Dynamics of tournaments: the soccer case

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