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Use of Machine Learning to Automate the Identification of Basketball Strategies Using Whole Team Player Tracking Data

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posted on 2020-02-11, 09:15 authored by Changjia Tian, Varuna De-SilvaVaruna De-Silva, Michael Caine, Steve Swanson
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.

Funding

Engineering and Physical Sciences Research Council, grant number EP/T000783/1

History

School

  • Loughborough University London

Published in

Applied Sciences

Volume

10

Issue

1

Pages

24 - 24

Publisher

MDPI AG

Version

  • VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by MDPI under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

2019-12-17

Publication date

2019-12-18

Copyright date

2020

ISSN

2076-3417

eISSN

2076-3417

Language

  • en

Depositor

Dr Varuna De Silva . Deposit date: 9 February 2020

Article number

24

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