Integrating machine learning and decision support in tactical decision-making in rugby union
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Rugby union, like many sports, is based around sequences of play, yet this sequential nature is often overlooked, for example in analyses that aggregate performance measures over a fixed time interval. We use recent developments in convolutional and recurrent neural networks to predict the outcomes of sequences of play, based on the ordered sequence of actions they contain and where on the field these actions occur. The outcomes considered are gaining territory, retaining possession, scoring a try, and being awarded or conceding a penalty. We consider several artificial neural network architectures and compare their performance against baseline models. Accounting for sequential data and using field location improved classification accuracy over the baseline for some outcomes. We then investigate how these prediction models can provide tactical decision support to coaches. We demonstrate that tactical insight can be gained by conducting scenario analyses with data visualisations to investigate which strategies yield the highest probability of achieving the desired outcome.
Watson , N , Hendricks , S , Stewart , T & Durbach , I 2020 , ' Integrating machine learning and decision support in tactical decision-making in rugby union ' , Journal of the Operational Research Society , vol. Latest Articles . https://doi.org/10.1080/01605682.2020.1779624
Journal of the Operational Research Society
Copyright © 2020 Operational Research Society. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1080/01605682.2020.1779624.
DescriptionFunding: National Research Foundation of South Africa andthe Department of Higher Education and Training via the Teaching and Development Grant (IRMA:29113).
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