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Identifying Team Style in Soccer Using Formations Learned from Spatiotemporal Tracking Data

Bialkowski, Alina and Lucey, Patrick and Carr, Peter and Yue, Yisong and Sridharan, Sridha and Matthews, Iain (2014) Identifying Team Style in Soccer Using Formations Learned from Spatiotemporal Tracking Data. In: 2014 IEEE International Conference on Data Mining Workshop. IEEE , Piscataway, NJ, pp. 9-14. ISBN 978-1-4799-4274-9. https://resolver.caltech.edu/CaltechAUTHORS:20170721-142537932

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Abstract

To the trained-eye, experts can often identify a team based on their unique style of play due to their movement, passing and interactions. In this paper, we present a method which can accurately determine the identity of a team from spatiotemporal player tracking data. We do this by utilizing a formation descriptor which is found by minimizing the entropy of role-specific occupancy maps. We show how our approach is significantly better at identifying different teams compared to standard measures (i.e., Shots, passes etc.). We demonstrate the utility of our approach using an entire season of Prozone player tracking data from a top-tier professional soccer league.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1109/ICDMW.2014.167DOIArticle
http://ieeexplore.ieee.org/document/7022571/PublisherArticle
Additional Information:© 2014 IEEE. The QUT portion of this research was supported by the Qld Govt's Dept. of Employment, Economic Development & Innovation.
Funders:
Funding AgencyGrant Number
Queensland Deptartment of Employment, Economic Development & InnovationUNSPECIFIED
Subject Keywords:Games, Accuracy, Trajectory, Spatiotemporal phenomena, Tracking, Entropy, Vectors
Record Number:CaltechAUTHORS:20170721-142537932
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20170721-142537932
Official Citation:A. Bialkowski, P. Lucey, P. Carr, Y. Yue, S. Sridharan and I. Matthews, "Identifying Team Style in Soccer Using Formations Learned from Spatiotemporal Tracking Data," 2014 IEEE International Conference on Data Mining Workshop, Shenzhen, 2014, pp. 9-14. doi: 10.1109/ICDMW.2014.167 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7022571&isnumber=7022545
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:79272
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:21 Jul 2017 21:56
Last Modified:03 Oct 2019 18:18

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