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dc.contributor.authorWatson, Neil
dc.contributor.authorHendricks, Sharief
dc.contributor.authorStewart, Theodor
dc.contributor.authorDurbach, Ian
dc.date.accessioned2021-08-02T23:38:21Z
dc.date.available2021-08-02T23:38:21Z
dc.date.issued2020-08-03
dc.identifier.citationWatson , 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/10.1080/01605682.2020.1779624en
dc.identifier.issn0160-5682
dc.identifier.otherPURE: 269562787
dc.identifier.otherPURE UUID: 837ae336-4b2d-4332-bac0-ad7293027722
dc.identifier.otherScopus: 85089009050
dc.identifier.otherORCID: /0000-0003-0769-2153/work/78892098
dc.identifier.otherWOS: 000555245200001
dc.identifier.urihttp://hdl.handle.net/10023/23700
dc.descriptionFunding: National Research Foundation of South Africa andthe Department of Higher Education and Training via the Teaching and Development Grant (IRMA:29113).en
dc.description.abstractRugby 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.
dc.language.isoeng
dc.relation.ispartofJournal of the Operational Research Societyen
dc.rightsCopyright © 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.en
dc.subjectClassificationen
dc.subjectDecision supporten
dc.subjectMachine learningen
dc.subjectNeural networksen
dc.subjectPerformance analysisen
dc.subjectRugby unionen
dc.subjectGV Recreation Leisureen
dc.subjectHD28 Management. Industrial Managementen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectManagement Information Systemsen
dc.subjectMarketingen
dc.subjectStrategy and Managementen
dc.subjectManagement Science and Operations Researchen
dc.subject3rd-DASen
dc.subject.lccGVen
dc.subject.lccHD28en
dc.subject.lccQA75en
dc.titleIntegrating machine learning and decision support in tactical decision-making in rugby unionen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Mathematics and Statisticsen
dc.identifier.doihttps://doi.org/10.1080/01605682.2020.1779624
dc.description.statusPeer revieweden
dc.date.embargoedUntil2021-08-03


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