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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.

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Title Machine learning enabled team performance analysis in the dynamical environment of soccer
Author(s) Kusmakar, S.
Shelyag, S.ORCID iD for Shelyag, S. orcid.org/0000-0002-6436-9347
Zhu, Y.ORCID iD for Zhu, Y. orcid.org/0000-0003-4776-4932
Dwyer, D. B.ORCID iD for Dwyer, D. B. orcid.org/0000-0002-8177-7262
Gastin, P. B.
Angelova, M.ORCID iD for 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

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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.