How to Make Sense of Team Sport Data: From Acquisition to Data Modeling and Research Aspects

Manuel Stein, Halldór Janetzko, Daniel Seebacher, Alexander Jäger, Manuel Nagel, Jürgen Hölsch, Sven Kosub, Tobias Schreck, Daniel A. Keim and Michael Grossniklaus
Additional contact information
Manuel Stein: Department of Computer and Information Science, University of Konstanz, 78457 Konstanz, Germany
Halldór Janetzko: Department of Geography, University of Zurich, 8057 Zurich, Switzerland
Daniel Seebacher: Department of Computer and Information Science, University of Konstanz, 78457 Konstanz, Germany
Alexander Jäger: Department of Computer and Information Science, University of Konstanz, 78457 Konstanz, Germany
Manuel Nagel: Department of Biology, University of Copenhagen, 2100 Copenhagen, Denmark
Jürgen Hölsch: Department of Computer and Information Science, University of Konstanz, 78457 Konstanz, Germany
Sven Kosub: Department of Computer and Information Science, University of Konstanz, 78457 Konstanz, Germany
Tobias Schreck: Institute for Computer Graphics and Knowledge Visualization, Graz University of Technology, 8010 Graz, Austria
Daniel A. Keim: Department of Computer and Information Science, University of Konstanz, 78457 Konstanz, Germany
Michael Grossniklaus: Department of Computer and Information Science, University of Konstanz, 78457 Konstanz, Germany

Data, 2017, vol. 2, issue 1, 1-23

Abstract: Automatic and interactive data analysis is instrumental in making use of increasing amounts of complex data. Owing to novel sensor modalities, analysis of data generated in professional team sport leagues such as soccer, baseball, and basketball has recently become of concern, with potentially high commercial and research interest. The analysis of team ball games can serve many goals, e.g., in coaching to understand effects of strategies and tactics, or to derive insights improving performance. Also, it is often decisive to trainers and analysts to understand why a certain movement of a player or groups of players happened, and what the respective influencing factors are. We consider team sport as group movement including collaboration and competition of individuals following specific rule sets. Analyzing team sports is a challenging problem as it involves joint understanding of heterogeneous data perspectives, including high-dimensional, video, and movement data, as well as considering team behavior and rules (constraints) given in the particular team sport. We identify important components of team sport data, exemplified by the soccer case, and explain how to analyze team sport data in general. We identify challenges arising when facing these data sets and we propose a multi-facet view and analysis including pattern detection, context-aware analysis, and visual explanation. We also present applicable methods and technologies covering the heterogeneous aspects in team sport data.

Keywords: sport analytics; visual analytics; high frequency spatio-temporal data (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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