Constructing Spaces and Times for Tactical Analysis in Football

Abstract

A possible objective in analyzing trajectories of multiple simultaneously moving objects, such as football players during a game, is to extract and understand the general patterns of coordinated movement in different classes of situations as they develop. For achieving this objective, we propose an approach that includes a combination of query techniques for flexible selection of episodes of situation development, a method for dynamic aggregation of data from selected groups of episodes, and a data structure for representing the aggregates that enables their exploration and use in further analysis. The aggregation, which is meant to abstract general movement patterns, involves construction of new time-homomorphic reference systems owing to iterative application of aggregation operators to a sequence of data selections. As similar patterns may occur at different spatial locations, we also propose constructing new spatial reference systems for aligning and matching movements irrespective of their absolute locations. The approach was tested in application to tracking data from two Bundesliga games of the 2018/2019 season. It enabled detection of interesting and meaningful general patterns of team behaviors in three classes of situations defined by football experts. The experts found the approach and the underlying concepts worth implementing in tools for football analysts.

Data-driven detection of counterpressing in professional football

“…There are significant differences in team's defensive and offensive tactical line-ups (Bialkowski et al 2014(Bialkowski et al , 2015Andrienko 2019;Shaw and Mark 2019). The transition phase describes the period following a win or loss of possession in which the team transitions between its offensive and defensive tactical line-ups and vice versa.…”

“…The transition phase describes the period following a win or loss of possession in which the team transitions between its offensive and defensive tactical line-ups and vice versa. When a team is in possession for at least a certain amount of time, we can assume that generally its tactical formation is optimized for offensive play and, consequently, suboptimal in terms of defending its own goal (Andrienko 2019;Shaw and Mark 2019). Therefore, the first seconds after losing the ball are critical from a defensive perspective.…”

“…Many recent scientific investigations aimed to establish new key performance indicator (KPI)-metrics quantifying certain aspects of the game: pass evaluation metrics were examined (Steiner et al 2019;Goes et al 2019), metrics to quantify controlled space were defined (Kim 2004;Fernandez and Bornn 2018;Brefeld et al 2019) and several studies evaluated shot metrics (Lucey et al 2014;Rathke 2017;Fairchild et al 2018;Anzer and Bauer 2021) 4 and goal scoring opportunities through possession values (Link et al 2016;Spearman 2018;Fernandez and Bornn 2018;Decroos et al 2020). Additionally, there are many approaches for measuring the defensive behavior of teams (Santos et al 2018;Andrienko 2019;Goes et al 2019), and even approaches aiming to quantify pressing (Bojinov and Luke 2016;Andrienko 2017;Robberechts 2019). Although pressing and counterpressing are closely related, they are two different phenomena.…”

Beyond Objects in Space-Time: Towards a Movement Analysis Framework with ‘How’ and ‘Why’ Elements

“…We are therefore motivated in this research by the need for extensions to spatiotemporal data mining algorithms [7] and CMA, that incorporate reasoning processes to provide this explanation for the mechanisms and causes behind movement decisions. In so doing, we hope to transcend the legacy of the data-centric view within GIScience that has limited CMA to aim, at best, for bridging the gap between low-level spatiotemporal data and the high-level movement patterns.…”

“…These data mining approaches have led to significant progress in organizing unstructured and messy movement data, but not in modelling the processes that bring about movement behaviour. For example, Andrienko et al [6,7] propose analytical frameworks to convert movement data of professional football players into behavioural movement patterns by generalisation, aggregation, and comparative visual exploration. Explaining such behaviour, however, is a sophisticated task that demands going beyond representing the distribution of objects across space or summarizing observations as patterns and associations.…”