Machine learning enabled team performance analysis in the dynamical environment of soccer Kusmakar, S., Shelyag, S., Zhu, Y., Dwyer, D. B., Gastin, P. B. and Angelova, M. 2020, Machine learning enabled team performance analysis in the dynamical environment of soccer, IEEE Access, pp. 1-15, doi: 10.1109/access.2020.2992025. Attached Files Name Description MIMEType Size Downloads Title Machine learning enabled team performance analysis in the dynamical environment of soccer Author(s) Kusmakar, S. Shelyag, S. orcid.org/0000-0002-6436-9347 Zhu, Y. orcid.org/0000-0003-4776-4932 Dwyer, D. B. orcid.org/0000-0002-8177-7262 Gastin, P. B. Angelova, M. orcid.org/0000-0002-0931-0916 Journal name IEEE Access Start page 1 End page 15 Total pages 15 Publisher Institute of Electrical and Electronics Engineers (IEEE) Place of publication Piscataway, N.J. Publication date 2020 ISSN 2169-3536 Keyword(s) dynamical systems network science distribution entropy football Kolmogorov complexity machine learning performance analysis Shannon entropy support vector machines soccer Summary Team sports can be viewed as dynamical systems unfolding in time and thus require tools and approaches congruent to the analysis of dynamical systems. The analysis of the pattern-forming dynamics of player interactions can uncover the clues to underlying tactical behaviour. This study aims to propose quantitative measures of a team’s performance derived only using player interactions. Concretely, we segment the data into events ending with a goal attempt, that is, “Shot”. Using the acquired sequences of events, we develop a coarse-grain activity model representing a player-to-player interaction network. We derive measures based on information theory and total interaction activity, to demonstrate an association with an attempt to score. In addition, we developed a novel machine learning approach to predict the likelihood of a team making an attempt to score during a segment of the match. Our developed prediction models showed an overall accuracy of 75.2% in predicting the correct segmental outcome from 13 matches in our dataset. The overall predicted winner of a match correlated with the true match outcome in 66.6% of the matches that ended in a result. Furthermore, the algorithm was evaluated on the largest available open collection of soccer logs. The algorithm showed an accuracy of 0.84 in the classification of the 42,860 segments from 1,941 matches and correctly predicted the match outcome in 81.9% of matches that ended in a result. The proposed measures of performance offer an insight into the underlying performance characteristics. Language eng DOI 10.1109/access.2020.2992025 Indigenous content off HERDC Research category C1 Refereed article in a scholarly journal Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30136511 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Link to full-text (open access)     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Wed, 06 May 2020, 09:44:23 EST Thu, 07 May 2020, 05:00:23 EST Thu, 07 May 2020, 06:04:01 EST Fri, 08 May 2020, 09:12:53 EST Fri, 08 May 2020, 09:16:01 EST Fri, 08 May 2020, 09:34:26 EST Sat, 13 Jun 2020, 02:32:56 EST Sat, 04 Jul 2020, 02:31:07 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 135 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Wed, 06 May 2020, 09:44:23 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.