Elsevier

Computers & Graphics

Volume 84, November 2019, Pages 122-133
Computers & Graphics

Special Section on SIBGRAPI 2019
How do soccer teams coordinate consecutive passes? A visual analytics system for analysing the complexity of passing sequences using soccer flow motifs

https://doi.org/10.1016/j.cag.2019.08.010Get rights and content

Highlights

  • A Visual Analytics system that supports the interactive analysis of passing sequences using soccer flow motifs.

  • An unsupervised approach to discover trajectory patterns in soccer flow motifs based on clustering by trajectory similarity.

  • A case study of passing strategy analysis using data from the Brazilian Serie A 2015 dataset.

  • A second case study using data from Turkish Super League 2016, with feedback from soccer analysts and training staff.

Abstract

The analysis of passing strategies plays a major role in soccer. Soccer managers use scouting, video footage, and soccer data feed to collect information about tactics and player performance. However, the nature of passing strategies is complex enough to reflect what is happening in the match and makes it hard to understand its dynamics. Furthermore, there exists a growing demand for pattern detection and passing analysis popularized by FC Barcelona’s tiki-taka. In this paper, we describe a visual analytics system to analyze the sequence and trajectory of consecutive passing sequences. We describe a two-phase clustering algorithm that extracts typical trajectory clusters in passing sequences, which result in eight predominant clusters. The combined analysis of the sequence and trajectory clusters allow experts to perform multi or single-game analysis in various ways. We show the potential of our approach in case studies using data from the Brazilian and Turkish leagues and report feedback from soccer experts.

Introduction

Soccer is a widely popular sport and the one with more revenues in the global sports events market. Managers, advertisers, and club owners follow the performance of a team in detail. To gain competitive advantage and success in local matches and international tournaments, the use of data in soccer has seen a huge growth in recent years [1]. In contrast with other sports, the low probabilities of scoring in soccer and the team strategies add to the complexity of game analysis. The complexity increases due to external variables like weather, home advantage, team formations, among others. The analysis of soccer matches allows a team to learn about its errors and to study the adversary.

The analysis of different formations has been widely studied since the beginning of soccer [2]. Match statistics benefit both coaches and players by adding performance information to their knowledge. However, apart from key events in a soccer match such as shots, goals, fouls, and number of passes, there exists an interest from the research community to understand the dynamic aspect of the game. To deal with the complexity of the game events, previous works [3], [4], [5] study particular sequences of events that might result in real scoring opportunities. These events could be player positions, shots or the positioning of the ball during a passing sequence. Hughes and Franks [6] show that, for successful teams, longer ball possessions were confirmed to produce more goals than shorter passing sequences. Simultaneously, the prevention of ball loss reduces the probability of taking a goal because of counter-attacks. Recent studies are revealing a growing interest in studying the complexity structure of passing strategies [5], [7] and their impact in crucial plays in matches.

The goal of our work is to complement the analysis of sequences of four consecutive passes proposed by Gyarmati and Anguera [5], also called soccer flow motifs, which allowed to confirm the FC Barcelona as having a unique style of play. In addition to their combinatorial analysis of soccer flows motifs, we propose to consider the position of players (trajectory) in passing sequences. As it is well known, soccer passing benefits from triangular formations to create opportunities for offensive and defensive plays, as well as spread out players on the pitch allows a team to take advantage of space efficiently and move the ball throughout the length of the field. We propose a Visual Analitcs (VA) system to allow the exploration of the trajectory of passing sequences and their relationship with the players involved, and validate our proposal with case studies using soccer data from premier soccer leagues. In summary, we outline the main research contributions of this paper as follows:

  • A Visual Analytics system that supports the interactive analysis of passing sequences using soccer flow motifs.

  • An unsupervised approach to discover trajectory patterns in soccer flow motifs based on clustering by trajectory similarity.

  • A case study of passing strategy analysis using data from the Brazilian Serie A 2015 dataset.

  • A second case study using data from Turkish Super League 2016, with feedback from soccer analysts and training staff.

Section snippets

Related work

We report related work focused on the statistical properties of soccer matches and visual analytics of sports data.

Soccer flow motifs

In this Section, we introduce the notion of soccer flow motifs used in the visual analytics system to perform pass analysis.

Clustering the trajectories of soccer flow motifs

The proposal of Gyarmati et al. [17] is fascinating, but it does not take into account the trajectory shape of soccer flow motifs. Clustering passing sequences by trajectory shapes can help identify passing strategies, specially for soccer where shapes as triangles and diamonds are very common in the sport language. In particular, we posed ourselves the question: is it possible to cluster the shape of passing trajectories formed by the position of players in the order that the passes take

Visual analytics system

We designed an interactive visual analytics prototype to support the analysis of soccer flow motifs in single and multiple matches. In the first version of the prototype, we used soccer analysis research questions to formulate preliminary tasks to drive the visualization design and interaction techniques. The goal was to build a user interface that supported multiple filtering capabilities couple with interactive visualization of the passing sequences. This prototype considered only the

Brazilian Serie a 2015 case study

The first case study to validate our approach uses the 2015 Brazilian Serie A dataset. We present three types of analysis: team characterization based on cluster frequency, a comparison between teams based on the passing clusters displayed on the pitch, and an evaluation of individual players based on motifs.

Turkish Super League 2016–2017 case study

In the second case study, we use the data from the first half of the 2016/17 Turkish Super League season. The accompanying video illustrates our system in action for this dataset. The analysis was carried out together with professional soccer analysts from four different Turkish teams. They expressed their interest in knowing which players are more involved in the structured passes execution and in which regions of the pitch these passes are performed. Therefore, we structured our analysis

Discussion

Understanding soccer strategies is a challenging task due to the complexity of the game. The approach discussed in this paper moves one step closer to understanding the key aspect of passing strategies. The work of Gyarmati et al. [17] brought attention to the study the combinatorial structure of passing sequences. In this work, we improved their work by adding the analysis of soccer trajectories. We believe that our visual designs can be helpful to understand global and individual aspects of

Conclusions

In this work, we proposed a visual analytics system to support the analysis of soccer flow motifs. The combined analysis of soccer flow motifs and trajectory clusters support the exploration of different passing patterns. We combine the notion of a soccer flow motif with passing trajectories. We describe a two-phase trajectory clustering technique which leads us to discover we 8 representative types of passing trajectories.

We designed a Visual Analytics system that supports filtering and

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The authors wish to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. We are especially grateful to Opta Sports and Sentio Sports for generously providing us with the soccer data set of the Brazilian 2015 league and the Turkish Super league 2016–2017. This study was partially supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001, CNPq 308851/2015-3, CNPq 426397/2018-5, TUBITAK

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