Constructing Spaces and Times for Tactical Analysis in Football

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.

[1]  Shashi K. Gadia,et al.  A homogeneous relational model and query languages for temporal databases , 1988, TODS.

[2]  Daniel A. Keim,et al.  Dynamic Visual Abstraction of Soccer Movement , 2017, Comput. Graph. Forum.

[3]  Daniel Memmert,et al.  Revolution im Profifußball , 2017 .

[4]  Tobias Schreck,et al.  Visual-Interactive Search for Soccer Trajectories to Identify Interesting Game Situations , 2016, Visualization and Data Analysis.

[5]  Daniel A. Keim,et al.  Visual Soccer Analytics: Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction , 2015, ISPRS Int. J. Geo Inf..

[6]  Jie Li,et al.  Supporting Story Synthesis: Bridging the Gap between Visual Analytics and Storytelling , 2020, IEEE Transactions on Visualization and Computer Graphics.

[7]  I. Giardina Collective Animal Behavior David J.T. Sumpter Collective Animal Behavior , 2011, Animal Behaviour.

[8]  Ross Maciejewski,et al.  Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions , 2017, IEEE Transactions on Intelligent Transportation Systems.

[9]  Gennady L. Andrienko,et al.  Spatial Generalization and Aggregation of Massive Movement Data , 2011, IEEE Transactions on Visualization and Computer Graphics.

[10]  Joachim Gudmundsson,et al.  Football analysis using spatio-temporal tools , 2012, Comput. Environ. Urban Syst..

[11]  Daniel Link,et al.  Data Analytics in Professional Soccer , 2018, Springer Fachmedien Wiesbaden.

[12]  Andreas Buja,et al.  Interactive data visualization using focusing and linking , 1991, Proceeding Visualization '91.

[13]  Ben Shneiderman,et al.  Dynamic Query Tools for Time Series Data Sets: Timebox Widgets for Interactive Exploration , 2004, Inf. Vis..

[14]  Dieter W. Fellner,et al.  Feature-based automatic identification of interesting data segments in group movement data , 2014, Inf. Vis..

[15]  Jonathan Wilson,et al.  Inverting The Pyramid: The History of Soccer Tactics , 2008 .

[16]  Daniel A. Keim,et al.  Bring It to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis , 2018, IEEE Transactions on Visualization and Computer Graphics.

[17]  Gennady L. Andrienko,et al.  Understanding movement data quality , 2016, J. Locat. Based Serv..

[18]  Joachim Gudmundsson,et al.  Spatio-Temporal Analysis of Team Sports , 2016, ACM Comput. Surv..

[19]  Gennady L. Andrienko,et al.  Clustering Trajectories by Relevant Parts for Air Traffic Analysis , 2018, IEEE Transactions on Visualization and Computer Graphics.

[20]  Charles Perin,et al.  SoccerStories: A Kick-off for Visual Soccer Analysis , 2013, IEEE Transactions on Visualization and Computer Graphics.

[21]  Gennady L. Andrienko,et al.  Coordinate Transformations for Characterization and Cluster Analysis of Spatial Configurations in Football , 2016, ECML/PKDD.

[22]  Daniel A. Keim,et al.  Director's Cut: Analysis and Annotation of Soccer Matches , 2016, IEEE Computer Graphics and Applications.

[23]  Gennady L. Andrienko,et al.  Analysis of Flight Variability: a Systematic Approach , 2019, IEEE Transactions on Visualization and Computer Graphics.

[24]  Cyril Ray,et al.  Visual exploration of movement and event data with interactive time masks , 2017, Vis. Informatics.

[25]  Natalia Adrienko,et al.  Spatial Generalization and Aggregation of Massive Movement Data , 2011 .

[26]  Jason Dykes,et al.  Visual analysis of pressure in football , 2017, Data Mining and Knowledge Discovery.

[27]  Jarke J. van Wijk,et al.  Eurographics/ Ieee-vgtc Symposium on Visualization 2009 Visualization of Vessel Movements , 2022 .

[28]  Daniel A. Keim,et al.  Tackling Similarity Search for Soccer Match Analysis: Multimodal Distance Measure and Interactive Query Definition , 2019, IEEE Computer Graphics and Applications.

[29]  Arie Segev,et al.  A glossary of temporal database concepts , 1992, SGMD.

[30]  Chris Weaver,et al.  Cross-Filtered Views for Multidimensional Visual Analysis , 2010, IEEE Transactions on Visualization and Computer Graphics.

[31]  Wei Chen,et al.  ForVizor: Visualizing Spatio-Temporal Team Formations in Soccer , 2019, IEEE Transactions on Visualization and Computer Graphics.

[32]  Christian Tominski,et al.  Visualization of Trajectory Attributes in Space–Time Cube and Trajectory Wall , 2014 .

[33]  Jürgen Perl,et al.  Tactics Analysis in Soccer - An Advanced Approach , 2013, Int. J. Comput. Sci. Sport.

[34]  Gennady L. Andrienko,et al.  Spatio-temporal aggregation for visual analysis of movements , 2008, 2008 IEEE Symposium on Visual Analytics Science and Technology.

[35]  Louise Barrett,et al.  Space Transformation for Understanding Group Movement , 2013, IEEE Transactions on Visualization and Computer Graphics.

[36]  Daniel A. Keim,et al.  Feature-driven visual analytics of soccer data , 2014, 2014 IEEE Conference on Visual Analytics Science and Technology (VAST).

[37]  Sridha Sridharan,et al.  Large-Scale Analysis of Soccer Matches Using Spatiotemporal Tracking Data , 2014, 2014 IEEE International Conference on Data Mining.

[38]  Sridha Sridharan,et al.  Identifying Team Style in Soccer Using Formations Learned from Spatiotemporal Tracking Data , 2014, 2014 IEEE International Conference on Data Mining Workshop.

[39]  Joachim Gudmundsson,et al.  Automated Classification of Passing in Football , 2015, PAKDD.