Integrating machine learning and decision support in tactical decision-making in rugby union
MetadataShow full item record
Altmetrics Handle Statistics
Altmetrics DOI Statistics
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).
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Showing items related by title, author, creator and subject.
Roles, rights, and responsibilities in the sustainable management of red deer populations in Scotland Witta, Lorin E. (University of St Andrews, 2018-06-29) - ThesisThe aim of the project was to explore the acquisition and dissemination of knowledge amongst decision-makers involved in the management of red deer in Scotland. While research exists on the ecology of red deer habitat, no ...
Water for all : towards an integrated approach to wetland conservation and flood risk reduction in a lowland catchment in Scotland Vinten, Andrew; Kuhfuss, Laure; Shortall, Orla; Stockan, Jenni; Ibiyemi, Adekunle; Pohle, Ina; Gabriel, Marjorie; Gunn, Iain; May, Linda (2019-09-15) - Journal articleStrategies for sustainable water resources management require integration of hydrological, ecological and socio-economic concerns. The “Water for all” project has sought to develop a multi-disciplinary science case for ...
Perceived success of hybrid microorganizations in a contested category Bicho, Marta; Nikolaeva, Ralitza; Ferreira, Fernando; Lages, Carmen (2022) - Journal articleThe organizational literature privileges objective performance indicators often selected by researchers. There is scarce research focusing on legitimacy-challenged hybrid and microorganizations and on perceived success ...