Header

UZH-Logo

Maintenance Infos

How to make sense of team sport data: From acquisition to data modeling and research aspects


Stein, Manuel; Janetzko, Halldór; Seebacher, Daniel; Jäger, Alexander; Nagel, Manuel; Hölsch, Jürgen; Kosub, Sven; Schreck, Tobias; Keim, Daniel; Grossniklaus, Michael (2017). How to make sense of team sport data: From acquisition to data modeling and research aspects. Data, 2(1):online.

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.

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.

Statistics

Citations

Dimensions.ai Metrics

Altmetrics

Downloads

30 downloads since deposited on 13 Jan 2017
14 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, not_refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2017
Deposited On:13 Jan 2017 09:14
Last Modified:19 Feb 2018 07:32
Publisher:MDPI Publishing
ISSN:2306-5729
OA Status:Gold
Publisher DOI:https://doi.org/10.3390/data2010002

Download

Download PDF  'How to make sense of team sport data: From acquisition to data modeling and research aspects'.
Preview
Content: Published Version
Language: English
Filetype: PDF
Size: 8MB
View at publisher
Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)