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Data 2017, 2(1), 2; doi:10.3390/data2010002

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

1
Department of Computer and Information Science, University of Konstanz, 78457 Konstanz, Germany
2
Department of Geography, University of Zurich, 8057 Zurich, Switzerland
3
Department of Biology, University of Copenhagen, 2100 Copenhagen, Denmark
4
Institute for Computer Graphics and Knowledge Visualization, Graz University of Technology, 8010 Graz, Austria
*
Author to whom correspondence should be addressed.
Academic Editors: Jamal Jokar Arsanjani, Marco Helbich, Amin Tayyebi and Amit Birenboim
Received: 24 September 2016 / Revised: 22 December 2016 / Accepted: 23 December 2016 / Published: 1 January 2017
(This article belongs to the Special Issue Geospatial Data)
View Full-Text   |   Download PDF [10074 KB, uploaded 1 January 2017]   |  

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. View Full-Text
Keywords: sport analytics; visual analytics; high frequency spatio-temporal data sport analytics; visual analytics; high frequency spatio-temporal data
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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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Stein, M.; Janetzko, H.; Seebacher, D.; Jäger, A.; Nagel, M.; Hölsch, J.; Kosub, S.; Schreck, T.; Keim, D.A.; Grossniklaus, M. How to Make Sense of Team Sport Data: From Acquisition to Data Modeling and Research Aspects. Data 2017, 2, 2.

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