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A machine learning approach to finding the fastest race course for professional athletes competing in Ironman® 70.3 races between 2004 and 2020

2023Journal article Open access
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dc.contributor.authorThuany, Mabliny
dc.contributor.authorValero, David
dc.contributor.authorVilliger, Elias
dc.contributor.authorForte, Pedro
dc.contributor.authorWeiss, Katja
dc.contributor.authorNikolaidis, Pantelis Theo
dc.contributor.authorAndrade, Marília S.
dc.contributor.authorCuk, Ivan
dc.contributor.authorSousa, Caio Victor
dc.contributor.authorKnechtle, Beat
dc.date.accessioned2024-01-04T16:50:41Z
dc.date.available2024-01-04T16:50:41Z
dc.date.issued2023
dc.description.abstractOur purpose was to find the fastest race courses for elite Ironman® 70.3 athletes, using machine learning (ML) algorithms. We collected the data of all professional triathletes competing between 2004 and 2020 in Ironman 70.3 races held worldwide. A sample of 16,611 professional athletes originating from 97 different countries and competing in 163 different races was thus obtained. Four different ML regression models were built, with gender, country of origin, and event location considered as independent variables to predict the final race time. For all the models, gender was the most important variable in predicting finish times. Attending to the single decision tree model, the fastest race times in the Ironman® 70.3 World Championship of around ~4 h 03 min would be achieved by men from Austria, Australia, Belgium, Brazil, Switzerland, Germany, France, the United Kingdom, South Africa, Canada, and New Zealand. Considering the World Championship is the target event for most professional athletes, it is expected that training is planned so that they attain their best performance in this event.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationThuany, Mabliny; Valero, David; Villiger, Elias; Forte, Pedro; Weiss, Katja; Nikolaidis, Pantelis Theo; Andrade, Marília S.; Cuk, Ivan; Sousa, Caio Victor; Knechtle, Beat (2023). A machine learning approach to finding the fastest race course for professional athletes competing in Ironman® 70.3 races between 2004 and 2020. International Journal of Environmental Research and Public Health. ISSN 1661-7827. 20:4, 1-12pt_PT
dc.identifier.doi10.3390/ijerph20043619pt_PT
dc.identifier.eissn1660-4601
dc.identifier.issn1661-7827
dc.identifier.urihttp://hdl.handle.net/10198/29098
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectEndurancept_PT
dc.subjectCyclingpt_PT
dc.subjectHalf-distance Ironmanpt_PT
dc.subjectSwimmingpt_PT
dc.subjectTriathlonpt_PT
dc.subjectRunningpt_PT
dc.titleA machine learning approach to finding the fastest race course for professional athletes competing in Ironman® 70.3 races between 2004 and 2020pt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage12pt_PT
oaire.citation.issue4pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleInternational Journal of Environmental Research and Public Healthpt_PT
oaire.citation.volume20pt_PT
person.familyNameForte
person.givenNamePedro
person.identifier.ciencia-id351B-B16B-79C7
person.identifier.orcid0000-0003-0184-6780
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication3ecc6d1b-07a4-40d7-81f4-df6fd7b3d5b0
relation.isAuthorOfPublication.latestForDiscovery3ecc6d1b-07a4-40d7-81f4-df6fd7b3d5b0

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