Pitch networks reveal organizational and spatial patterns of Guardiola’s F.C. Barcelona

Herrera-Diestra, José L.; Echegoyen, Ignacio; Martínez, Johann H.; Garrido, David; Busquets, Javier; Io, F.Seirul.; Buldú, Javier M.

doi:10.1016/j.chaos.2020.109934

Cited by 15 publications

(11 citation statements)

References 25 publications

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“…Pitch passing networks contain information about how teams move the ball along the match and what are the most connected areas of the pitch. Furthermore, they contain spatial information that is lost when the nodes of the passing networks are the players themselves 14,18 . It is worth mentioning that there is no unique pitch passing network for a given match, but a "family" of them.…”

Section: Discussionmentioning

confidence: 99%

“…Multi-scale pitch passing networks. Pitch passing networks describe how the ball flows through the field during the possession of a given team using specific partitions of the pitch 18 . The construction of this kind of networks requires several steps, beginning with the division of the pitch into m = p × q areas, each one corresponding to a node of the passing network, as shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…to quantify team performance 16 and analyze differences in the playing patterns of football teams 18 . In our case, we tracked the evolution of the pitch-passing networks of Spanish football teams playing at the first division of LaLiga during the season 2018/2019.…”

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confidence: 99%

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Consistency and identifiability of football teams: a network science perspective

Garrido

Antequera

Busquets

et al. 2020

Sci Rep

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We investigated the ability of football teams to develop a particular playing style by looking at their passing patterns. Using the information contained in the pass sequences during matches, we constructed the pitch passing networks of teams, whose nodes are the divisions of the pitch for a given spatial scale and links account for the number of passes from region to region. We translated football passings networks into their corresponding adjacency matrices. We calculated the correlations between matrices of the same team to quantify how consistent the passing patterns of a given team are. Next, we quantified the differences with other teams’ matrices and obtained an identifiability parameter that indicates how unique are the passing patterns of a given team. Consistency and identifiability rankings were calculated during a whole season, allowing to detect those teams of a league whose passing patterns are different from the rest. Furthermore, we found differences between teams playing at home or away. Finally, we used the identifiability parameter to investigate what teams imposed their passing patterns over the rivals during a given match.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Consistency and identifiability of football teams: a network science perspective

Garrido

Antequera

Busquets

et al. 2020

Sci Rep

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“…Recent works has focused on describing cooperative on-ball interaction in football within the framework of network science [27][28][29][30][31]. In [32], for instance, D. Garrido et al studied the so call Pitch Passing Networks in the games of the Spanish League at 2018/2019 season.…”

Section: Introductionmentioning

confidence: 99%

“…Usually, features of these collective behaviors are described by using simple group-level metrics [17][18][19][20][21][22]. Furthermore, temporal sequences of ball and player movements in football, showing traits of complex behaviors, have been reported and studied using stochastic models and statistical analysis [23][24][25][26].Recent works has focused on describing cooperative on-ball interaction in football within the framework of network science [27][28][29][30][31]. In [32], for instance, D. Garrido et al studied the so call Pitch Passing Networks in the games of the Spanish League at 2018/2019 season.…”

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Stochastic model for football's collective dynamics

Chacoma,

Almeira,

Perotti

et al. 2021

Preprint

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In this paper, we study collective interaction dynamics emerging in the game of football-soccer.To do so, we surveyed a database containing body-sensors traces measured during three professional football matches, where we observed statistical patterns that we used to propose a stochastic model for the players' motion in the field. The model, which is based on linear interactions, captures in good approximation the spatiotemporal dynamics of a football team. Our theoretical framework, therefore, becomes an effective analytical tool to uncover the underlying cooperative mechanisms behind the complexity of football plays. Moreover, we showed that it can provide handy theoretical support for coaches to evaluate teams' and players' performances in competitive scenarios. I. INTRODUCTIONThe use of complex systems theory as an alternative paradigm for analyzing elite sport dynamics is currently arousing intense academic interest [1][2][3][4][5]. Fostered by the new advances in data acquisition [6,7] and artificial intelligence techniques [8,9], the use of state-of-theart statistical tools to evaluate teams' performances is, nowadays, shaping a new profile of data-driven-based professional coaches worldwide.Formally, sports teams can be thought of as complex sociotechnical systems [10], where a wide range of organizational factors might interact to influence athletes' performances [11][12][13][14]. Particularly, in collective games like football, cooperative interplay dynamics seem to be a key feature to be analyzed [15,16]. In principle, collective behaviors in soccer are important since they are connected to team tactics and strategies. Usually, features of these collective behaviors are described by using simple group-level metrics [17][18][19][20][21][22]. Furthermore, temporal sequences of ball and player movements in football, showing traits of complex behaviors, have been reported and studied using stochastic models and statistical analysis [23][24][25][26].Recent works has focused on describing cooperative on-ball interaction in football within the framework of network science [27][28][29][30][31]. In [32], for instance, D. Garrido et al. studied the so call Pitch Passing Networks in the games of the Spanish League at 2018/2019 season.

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The Interpretable Representation of Football Player Roles Based on Passing/Receiving Patterns

Sattari

Johansson

Wilderoth

et al. 2022

Communications in Computer and Information Science

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Pitch networks reveal organizational and spatial patterns of Guardiola’s F.C. Barcelona

Cited by 15 publications

References 25 publications

Consistency and identifiability of football teams: a network science perspective

Consistency and identifiability of football teams: a network science perspective

Stochastic model for football's collective dynamics

The Interpretable Representation of Football Player Roles Based on Passing/Receiving Patterns

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