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ISPRS Int. J. Geo-Inf. 2015, 4(4), 2159-2184; doi:10.3390/ijgi4042159

Visual Soccer Analytics: Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction

1
Department of Computer and Information Science, University of Konstanz, Konstanz 78457, Germany
2
Institute for Computer Graphics and Knowledge Visualization, Graz University of Technology, Graz 8010, Austria
*
Author to whom correspondence should be addressed.
Academic Editors: Emmanuel Stefanakis, Yaolin Liu, Phaedon Kyriakidis and Wolfgang Kainz
Received: 30 May 2015 / Revised: 20 September 2015 / Accepted: 8 October 2015 / Published: 20 October 2015
(This article belongs to the Special Issue Advances in Spatio-Temporal Data Analysis and Mining)
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Abstract

With recent advances in sensor technologies, large amounts of movement data have become available in many application areas. A novel, promising application is the data-driven analysis of team sport. Specifically, soccer matches comprise rich, multivariate movement data at high temporal and geospatial resolution. Capturing and analyzing complex movement patterns and interdependencies between the players with respect to various characteristics is challenging. So far, soccer experts manually post-analyze game situations and depict certain patterns with respect to their experience. We propose a visual analysis system for interactive identification of soccer patterns and situations being of interest to the analyst. Our approach builds on a preliminary system, which is enhanced by semantic features defined together with a soccer domain expert. The system includes a range of useful visualizations to show the ranking of features over time and plots the change of game play situations, both helping the analyst to interpret complex game situations. A novel workflow includes improving the analysis process by a learning stage, taking into account user feedback. We evaluate our approach by analyzing real-world soccer matches, illustrate several use cases and collect additional expert feedback. The resulting findings are discussed with subject matter experts. View Full-Text
Keywords: visual analytics; sport analytics; soccer analysis visual analytics; sport analytics; soccer analysis
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Stein, M.; Häußler, J.; Jäckle, D.; Janetzko, H.; Schreck, T.; Keim, D.A. Visual Soccer Analytics: Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction. ISPRS Int. J. Geo-Inf. 2015, 4, 2159-2184.

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