Bring It to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis

Manuel Stein, Halldor Janetzko, Andreas Lamprecht, Thorsten Breitkreutz, Philipp Zimmermann, Bastian Goldlucke, Tobias Schreck, Gennady Andrienko, Michael Grossniklaus, Daniel A. Keim
2018 IEEE Transactions on Visualization and Computer Graphics  
This is the accepted version of the paper. This version of the publication may differ from the final published version. Permanent repository link: http://openaccess.city.ac.uk/18380/ Link to published version: http://dx.Fig. 1: Our system integrates analytical visualizations of soccer player movement data extracted from video into the same video, relying on appropriate computer vision techniques. Movement visualization techniques like interaction spaces (left) and free spaces (right) allow to
more » ... alyze and explore soccer movement data in context of the original video. As our studies conducted with expert soccer analysts show, the combination of video and abstract visualization supports effective contextualized analysis and can foster user trust regarding data and analytical visualization. Abstract-Analysts in professional team sport regularly perform analysis to gain strategic and tactical insights into player and team behavior. Goals of team sport analysis regularly include identification of weaknesses of opposing teams, or assessing performance and improvement potential of a coached team. Current analysis workflows are typically based on the analysis of team videos. Also, analysts can rely on techniques from Information Visualization, to depict e.g., player or ball trajectories. However, video analysis is typically a time-consuming process, where the analyst needs to memorize and annotate scenes. In contrast, visualization typically relies on an abstract data model, often using abstract visual mappings, and is not directly linked to the observed movement context anymore. We propose a visual analytics system that tightly integrates team sport video recordings with abstract visualization of underlying trajectory data. We apply appropriate computer vision techniques to extract trajectory data from video input. Furthermore, we apply advanced trajectory and movement analysis techniques to derive relevant team sport analytic measures for region, event and player analysis in the case of soccer analysis. Our system seamlessly integrates video and visualization modalities, enabling analysts to draw on the advantages of both analysis forms. Several expert studies conducted with team sport analysts indicate the effectiveness of our integrated approach.
doi:10.1109/tvcg.2017.2745181 pmid:28866578 fatcat:yqbnasxv4zbjjojq7wao7xxf7e