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The use of neural network technology to model swimming performance

2007Journal article Open access
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dc.contributor.authorSilva, António
dc.contributor.authorCosta, Aldo M.
dc.contributor.authorOliveira, Paulo Moura
dc.contributor.authorReis, VM
dc.contributor.authorSaavedra, Jose M
dc.contributor.authorPerl, Jurgen
dc.contributor.authorRouboa, Abel I
dc.contributor.authorMarinho, Daniel
dc.date.accessioned2020-02-28T11:33:18Z
dc.date.available2020-02-28T11:33:18Z
dc.date.issued2007
dc.description.abstractto identify the factors which are able to explain the performance in the 200 meters individual medley and 400 meters front crawl events in young swimmers, to model the performance in those events using non-linear mathematic methods through artificial neural networks (multi-layer perceptrons) and to assess the neural network models precision to predict the performance. A sample of 138 young swimmers (65 males and 73 females) of national level was submitted to a test battery comprising four different domains: kinanthropometric evaluation, dry land functional evaluation (strength and flexibility), swimming functional evaluation (hydrodynamics, hydrostatic and bioenergetics characteristics) and swimming technique evaluation. To establish a profile of the young swimmer non-linear combinations between preponderant variables for each gender and swim performance in the 200 meters medley and 400 meters font crawl events were developed. For this purpose a feed forward neural network was used (Multilayer Perceptron) with three neurons in a single hidden layer. The prognosis precision of the model (error lower than 0.8% between true and estimated performances) is supported by recent evidence. Therefore, we consider that the neural network tool can be a good approach in the resolution of complex problems such as performance modeling and the talent identification in swimming and, possibly, in a wide variety of sports. Key pointsThe non-linear analysis resulting from the use of feed forward neural network allowed us the development of four performance models.The mean difference between the true and estimated results performed by each one of the four neural network models constructed was low.The neural network tool can be a good approach in the resolution of the performance modeling as an alternative to the standard statistical models that presume well-defined distributions and independence among all inputs.The use of neural networks for sports sciences application allowed us to create very realistic models for swimming performance prediction based on previous selected criterions that were related with the dependent variable (performance).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.urihttp://hdl.handle.net/10400.6/9623
dc.language.isoengpt_PT
dc.subjectEvaluationpt_PT
dc.subjectAge group swimmerspt_PT
dc.subjectFront crawlpt_PT
dc.subjectIndividual medleypt_PT
dc.titleThe use of neural network technology to model swimming performancept_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage125pt_PT
oaire.citation.issue1pt_PT
oaire.citation.startPage117pt_PT
oaire.citation.titleJournal of Sports Science and Medicinept_PT
oaire.citation.volume6pt_PT
person.familyNameSilva
person.familyNameM. Costa
person.familyNameSaavedra
person.familyNameRouboa
person.familyNameMarinho
person.givenNameAntónio
person.givenNameAldo
person.givenNameJose M
person.givenNameAbel
person.givenNameDaniel
person.identifier896755
person.identifier.ciencia-id8C15-83CB-5566
person.identifier.ciencia-id0A15-F655-DDDE
person.identifier.ciencia-idBF1B-3A8D-1328
person.identifier.ciencia-idA31F-03A9-5CD3
person.identifier.ciencia-id471B-F3CC-479A
person.identifier.orcid0000-0001-5790-5116
person.identifier.orcid0000-0003-0296-9707
person.identifier.orcid0000-0002-4996-1414
person.identifier.orcid0000-0003-2810-6846
person.identifier.orcid0000-0003-2652-8789
person.identifier.orcid0000-0003-2351-3047
person.identifier.scopus-author-id13410238400
person.identifier.scopus-author-id35518382800
person.identifier.scopus-author-id35303373200
person.identifier.scopus-author-id6603461700
person.identifier.scopus-author-id15022568500
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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