Dominant interaction group detection in team sports

Behzad BOZORGTABAR, Roland GOECKE

Research output: A Conference proceeding or a Chapter in BookConference contribution

Abstract

With sport performance analysis shifting towards a team’s overall behaviour, the need for automatically discovering formations, such as players temporarily forming a common group of interactions, emerges to complement the coach’s observations. We propose a novel framework to detect a structured group of players, encoded in the form of context-dependent team players’ interactions. In real scenarios, considering a tendency among the players’ movements to occupy a common interested region on a sport field, we predict the future candidate area of group interactions (tendentious zone) before the group formations occur. Consequently, the tendentious zone guides the future players’ movements and provides prior information about their future positions on the field. Building a graph of all players’ positions and considering their motion stability towards the tendentious zone, we aim to discover an optimal subgraph indicating a dominant group of players by maximising the similarity among them. To quantify the similarity of any two players, we
consider their relative proximity as well as the common social attention model. Experiments on new sports datasets consistently show the superiority and effectiveness of the proposed approach over existing group detection methods.
Original languageEnglish
Title of host publication21st ACM SIGKDD Conference on knowledge discovery and data mining
Subtitle of host publicationProceedings of the largescale sports analysis workshop
EditorsThornsten Joachims, Geoff Webb
Place of PublicationSydney
PublisherAssociation for Computing Machinery (ACM)
Pages1-4
Number of pages4
ISBN (Print)9781450336642
Publication statusPublished - 2015
Event21st ACM SIGKDD Conference on knowledge discovery and data mining - Sydney, Sydney, Australia
Duration: 10 Aug 201513 Aug 2015
http://www.kdd.org/kdd2015/ (Conference detail)

Conference

Conference21st ACM SIGKDD Conference on knowledge discovery and data mining
Abbreviated titleKDD 2015
CountryAustralia
CitySydney
Period10/08/1513/08/15
Internet address

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Sports
Experiments

Cite this

BOZORGTABAR, B., & GOECKE, R. (2015). Dominant interaction group detection in team sports. In T. Joachims, & G. Webb (Eds.), 21st ACM SIGKDD Conference on knowledge discovery and data mining: Proceedings of the largescale sports analysis workshop (pp. 1-4). Sydney: Association for Computing Machinery (ACM).
BOZORGTABAR, Behzad ; GOECKE, Roland. / Dominant interaction group detection in team sports. 21st ACM SIGKDD Conference on knowledge discovery and data mining: Proceedings of the largescale sports analysis workshop. editor / Thornsten Joachims ; Geoff Webb. Sydney : Association for Computing Machinery (ACM), 2015. pp. 1-4
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BOZORGTABAR, B & GOECKE, R 2015, Dominant interaction group detection in team sports. in T Joachims & G Webb (eds), 21st ACM SIGKDD Conference on knowledge discovery and data mining: Proceedings of the largescale sports analysis workshop. Association for Computing Machinery (ACM), Sydney, pp. 1-4, 21st ACM SIGKDD Conference on knowledge discovery and data mining, Sydney, Australia, 10/08/15.

Dominant interaction group detection in team sports. / BOZORGTABAR, Behzad; GOECKE, Roland.

21st ACM SIGKDD Conference on knowledge discovery and data mining: Proceedings of the largescale sports analysis workshop. ed. / Thornsten Joachims; Geoff Webb. Sydney : Association for Computing Machinery (ACM), 2015. p. 1-4.

Research output: A Conference proceeding or a Chapter in BookConference contribution

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BOZORGTABAR B, GOECKE R. Dominant interaction group detection in team sports. In Joachims T, Webb G, editors, 21st ACM SIGKDD Conference on knowledge discovery and data mining: Proceedings of the largescale sports analysis workshop. Sydney: Association for Computing Machinery (ACM). 2015. p. 1-4