Abstract
Spatiotemporal patterns of play can be extracted from competitive environments to design representative training tasks and underlying processes that sustain performance outcomes. To support this statement, the aims of this study were: (i) describe the collective behavioural patterns that relies upon the use of player positioning in interaction with teammates, opponents and ball positioning; (ii) and define the underlying structure among the variables through application of a factorial analysis. The sample comprised a total of 1,413 ball possession sequences, obtained from twelve elite football matches from one team (the team ended the season in the top-5 position). The dynamic position of the players (from both competing teams), as well as the ball, were captured and transformed to two-dimensional coordinates. Data included the ball possession sequences from six matches played against top opponents (TOP, the three teams classified in the first 3 places at the end of the season) and six matches against bottom opponents (BOTTOM, the three teams classified in the last 3 at the end of the season). The variables calculated for each ball possession were the following: ball position; team space in possession; game space (comprising the outfield players of both teams); position and space at the end of ball possession. Statistical comparisons were carried with magnitude-based decisions and null-hypothesis analysis and factor analysis to define the underlying structure among variables according to the considered contexts. Results showed that playing against TOP opponents, there was ~38 meters game length per ~43 meters game width with 12% of coefficient of variation (%). Ball possessions lasted for ~28 seconds and tended to end at ~83m of pitch length. Against BOTTOM opponents, a decrease in the game length with an increase in game width and in the deepest location was observed in comparison with playing against TOP opponents. The duration of ball possession increased considerable (~37 seconds), and the ball speed entropy was higher, suggesting lower levels of regularity in comparison with TOP opponents. The BOTTOM teams revealed a small EPS. The Principal Component Analysis showed a strong association of the ball speed, entropy of the ball speed and the coefficient of variation (%) of the ball speed. The EPS of the team in possession was well correlated with the game space, especially the game width facing TOP opponents. Against BOTTOM opponents, there was a strong association of ball possession duration, game width, distance covered by the ball, and length/width ratio of the ball movement. The overall approach carried out in this study may serve as the starting point to elaborate normative models of positioning behaviours measures to support the coaches’ operating decisions.
Highlights
One of the biggest challenges in team sports is to validate performance indicators that contribute to optimize the coaching process and competition outcome [1]
Against BOTTOM opponents, there was a strong association of ball possession duration, game width, distance covered by the ball, and length/width ratio of the ball movement
Data included the ball possession sequences from the six matches of one team when playing against top opponents (TOP, the three top teams classified in the top-3 at the end of the season) and six matches against bottom opponents (BOTTOM, the three bottom teams classified in the bottom-3 at the end of the Extracting spatial-temporal features that describe a team match demands season)
Summary
One of the biggest challenges in team sports is to validate performance indicators that contribute to optimize the coaching process and competition outcome [1]. Within the traditional methods of performance analysis in team sports, the notational analysis has been used to obtain indicators of discrete actions and/or events by using advanced statistical procedures [2, 3] This approach allows to understand the static complexity of performance, to produce a valid and reliable description of individual and team behaviours and to describe teams’ performance by correlating a wide range of variables [4, 5]. The players’ physical and physiological competitive demands [6] and their comparison with training have been incessantly investigated over the last years, allowing to identify different match profiles according to the distances covered at different speed thresholds [7,8,9] The identification of such profiles brought relevant aspects to plan the physical loads from short to mid-term planning guidelines and to minimize the fatigue and the risk of injuries [10, 11]. Several new instruments, procedures, processing techniques, and new visuals may be incorporated into the performance analysis scope to complement the static complexity analysis and attend the new dimension of questions related to the dynamic complexity
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