Modeling ball possession dynamics in the game of football

Chacoma, Andrés; Almeira, Nahuel; Perotti, Juan Ignacio; Billoni, Orlando V.

doi:10.1103/physreve.102.042120

Cited by 18 publications

(12 citation statements)

References 31 publications

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“…Lauren, Quarrie et al [ 28 ] proposed a collective motion-based simulation for rugby union, where players are represented by agents that begin their movement on fixed tracks (representing a plan or formation forming a first order constraint) for a period of time and are then governed by local interactions. Chacoma, Almeira et al [ 29 ] developed an agent-based model for soccer that simulates a subset of the game, with three players (two attackers and a single defender). In this scenario, the defender attempts to intercept the attacking player in possession of the ball, while attackers advance and move the ball.…”

Section: Introductionmentioning

confidence: 99%

Collective states and their transitions in football

2021

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Movement, positioning and coordination of player formations is a key aspect for the performance of teams within field-based sports. The increased availability of player tracking data has given rise to numerous studies that focus on the relationship between simple descriptive statistics surrounding team formation and performance. While these existing approaches have provided a high-level a view of team-based spatial formations, there is limited research on the nature of collective movement across players within teams and the establishment of stable collective states within game play. This study draws inspiration from the analysis of collective movement in nature, such as that observed within schools of fish and flocking birds, to explore the existence of collective states within the phases of play in soccer. Order parameters and metrics describing group motion and shape are derived from player movement tracks to uncover the nature of the team’s collective states and transitions. This represents a unique addition to the current body of work around the analysis of player movement in team sports. The results from this study demonstrate that sequences of ordered collective behaviours exist with relatively rapid transitions between highly aligned polar and un-ordered swarm behaviours (and vice-versa). Defensive phases of play have a higher proportion of ordered team movement than attacking phases, indicating that movements linked with attacking tactics, such as player dispersion to generate passing and shooting opportunities leads to lower overall collective order. Exploration within this study suggests that defensive tactics, such as reducing the depth or width to close passing opportunities, allows for higher team movement speeds and increased levels of collective order. This study provides a novel view of player movement by visualising the collective states present across the phases of play in football.

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Section: Introductionmentioning

confidence: 99%

Collective states and their transitions in football

2021

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“…Event data is produced by human annotators in live during the match, and is another modality from which we can extract valuable information. While our method uses this type of data for summarization, several approaches use it for other tasks like analyzing advantage of playing on the home field [55], recognizing teams [9], automatically discovering patterns in offensive strategies [85], [35], predicting passes [86], detecting tactics [22], predicting the chance to score the next goal [53], evaluating the performance or contributions of the players [62], [21], [10] and modeling ball possession [14]. These metadata can now more and more be found either on websites directly managed by the companies producing them (Prozone, GeniusSports, Opta, WyScout, and others) or through open data sources [63], [7].…”

Section: Related Workmentioning

confidence: 99%

A Multi-stage deep architecture for summary generation of soccer videos

Sanabria¹,

Precioso²,

Mattei³

et al. 2022

Preprint

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Video content is present in an ever-increasing number of fields, both scientific and commercial. Sports, particularly soccer, is one of the industries that has invested the most in the field of video analytics, due to the massive popularity of the game and the emergence of new markets (such as sport betting markets). Previous state-of-the-art methods on soccer matches video summarization rely on handcrafted heuristics to generate summaries which is poorly generalizable, but these works have yet proven that multiple modalities help detect the best actions of the game. On the other hand, machine learning models with higher generalization potential have enter the field of summarization of general-purpose videos, offering several deep learning approaches. However, most of them exploit content specificities that are not appropriate for sport wholematch videos. Although video content has been for many years the main source for automatizing knowledge extraction in soccer, the data that records all the events happening on the field has become lately very important in sports analytics, since this event data provides richer context information and requires less processing. Considering that in automatic sports summarization, the goal is not only to show the most important actions of the game, but also to reproduce the storytelling of the whole match with as much emotion as the one evoked by human editors, we propose a method to generate the summary of a soccer match video exploiting both the audio and the event metadata of the entire match. The results show that our method can detect the actions of the match, identify which of these actions should belong to the summary and then propose multiple candidate summaries which are similar enough but with relevant variability to provide different options to the final editor. Furthermore, we show the generalization capability of our work since it can transfer knowledge between datasets from different broadcasting companies, from different competitions, acquired in different conditions, and corresponding to summaries of different lengths.

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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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Modeling ball possession dynamics in the game of football

Cited by 18 publications

References 31 publications

Collective states and their transitions in football

Collective states and their transitions in football

A Multi-stage deep architecture for summary generation of soccer videos

Stochastic model for football's collective dynamics

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