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Use of machine learning to automate the identification of basketball strategies using whole team player tracking data

Tian, C, De Silva, V, Caine, M and Swanson, S 2020, Use of machine learning to automate the identification of basketball strategies using whole team player tracking data, Applied Sciences, vol. 10, no. 1, pp. 1-17, doi: 10.3390/app10010024.

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Title Use of machine learning to automate the identification of basketball strategies using whole team player tracking data
Author(s) Tian, C
De Silva, V
Caine, M
Swanson, SORCID iD for Swanson, S orcid.org/0000-0003-4359-2766
Journal name Applied Sciences
Volume number 10
Issue number 1
Article ID 24
Start page 1
End page 17
Total pages 17
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2020
ISSN 2076-3417
Keyword(s) sports analytics
basketball
data-driven strategy learning
multi-agent systems
Summary The use of machine learning to identify and classify offensive and defensive strategies in team sports through spatio-temporal tracking data has received significant interest recently in the literature and the global sport industry. This paper focuses on data-driven defensive strategy learning in basketball. Most research to date on basketball strategy learning has focused on offensive effectiveness and is based on the interaction between the on-ball player and principle on-ball defender, thereby ignoring the contribution of the remaining players. Furthermore, most sports analytical systems that provide play-by-play data is heavily biased towards offensive metrics such as passes, dribbles, and shots. The aim of the current study was to use machine learning to classify the different defensive strategies basketball players adopt when deviating from their initial defensive action. An analytical model was developed to recognise the one-on-one (matched) relationships of the players, which is utilised to automatically identify any change of defensive strategy. A classification model is developed based on a player and ball tracking dataset from National Basketball Association (NBA) game play to classify the adopted defensive strategy against pick-and-roll play. The methodology described is the first to analyse the defensive strategy of all in-game players (both on-ball players and off-ball players). The cross-validation results indicate that the proposed technique for automatic defensive strategy identification can achieve up to 69% accuracy of classification. Machine learning techniques, such as the one adopted here, have the potential to enable a deeper understanding of player decision making and defensive game strategies in basketball and other sports, by leveraging the player and ball tracking data.
Language eng
DOI 10.3390/app10010024
Indigenous content off
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2019, the authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30141571

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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.