Abstract
This study aimed to identify the tactical patterns and the timescales of variables during a soccer match, allowing understanding the multilevel organization of tactical behaviors, and to determine the similarity of patterns performed by different groups of teammates during the first and second halves. Positional data from 20 professional male soccer players from the same team were collected using high frequency global positioning systems (5 Hz). Twenty-nine categories of tactical behaviors were determined from eight positioning-derived variables creating multivariate binary (Boolean) time-series matrices. Hierarchical principal component analysis (PCA) was used to identify the multilevel structure of tactical behaviors. The sequential reduction of each set level of principal components revealed a sole principal component as the slowest collective variable, forming the global basin of attraction of tactical patterns during each half of the match. In addition, the mean dwell time of each positioning-derived variable helped to understand the multilevel organization of collective tactical behavior during a soccer match. This approach warrants further investigations to analyze the influence of task constraints on the emergence of tactical behavior. Furthermore, PCA can help coaches to design representative training tasks according to those tactical patterns captured during match competitions and to compare them depending on situational variables.
Highlights
In team sports settings, the interaction between team players and environment gives rise to interpersonal coordination movements that dynamically arise during the game
The aim of this study was to identify the tactical patterns and the timescales of positioningderived variables that define the patterns during a soccer match, allowing understanding the multilevel organization of tactical behaviors
The salient structure of principal components (PCs) was defined by those categories with a high absolute component score
Summary
The interaction between team players and environment gives rise to interpersonal coordination movements that dynamically arise during the game. Interpersonal interactions are thought to be non-linear and some studies have shown this explicitly (Schmidt et al, 1990; Richardson et al, 2007). Under conditions of non-linear interpersonal interactions, behavioral patterns are hypothesized to be spontaneously organized at the macroscopic level as a result of functional grouping of components which are temporarily assembled, through a self-organization process (Gréhaigne et al, 1997; McGarry et al, 2002; Araujo et al, 2006). Many investigations have studied the emergence of coordination patterns on individual (Travassos et al, 2012), dyadic (Sampaio and Maçãs, 2012) and collective levels of analysis (Silva et al, 2014). Notwithstanding, there is little literature that attempts to identify the theoretically existing relation among these levels of game constraints (Bourbousson et al, 2014).
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