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StreamTeam-Football: Analyzing Football Matches in Real-Time on the Basis of Position Streams

Probst, Lukas and Schuldt, Heiko and Seidenschwarz, Philipp and Rumo, Martin. (2020) StreamTeam-Football: Analyzing Football Matches in Real-Time on the Basis of Position Streams. In: Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2020). pp. 637-644.

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Official URL: https://edoc.unibas.ch/81641/

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Abstract

In recent years, Big Data has become an important topic in many areas of our daily lives, including sports. Almost all professional clubs analyze matches to improve the performance of their teams. However, events are still predominantly captured manually, although many sensor-based and video-based tracking systems exist which provide the positions of the players and the ball in real-time. This manual process is tedious and errorprone. In this paper, we propose STREAMTEAM-FOOTBALL, an open source football analysis application, to fill this gap. STREAMTEAM-FOOTBALL allows to analyze football matches fully automatically and in real-time on the basis of tracked position data using a data stream analysis approach. Our evaluations confirm the effectiveness of our automated analysis and further show the scalability of STREAMTEAM-FOOTBALL by its ability to analyze multiple football matches in parallel.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Datenbanken (Schuldt)
UniBasel Contributors:Schuldt, Heiko and Probst, Lukas and Seidenschwarz, Philipp German
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Publisher:IEEE
ISBN:978-1-7281-6252-2
e-ISBN:978-1-7281-6251-5
Note:Publication type according to Uni Basel Research Database: Conference paper
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Last Modified:27 Jul 2021 13:14
Deposited On:27 Jul 2021 13:14

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