Next-Generation Models for Predicting Winning Times in Elite Swimming Events: Updated Predictions for the Paris 2024 Olympic Games.

Mujika, Iñigo; Pyne, David B; Wu, Paul Pao-Yen; Ng, Kwok; Crowley, Emmet; Powell, Cormac

Mujika, Iñigo; Pyne, David B; Wu, Paul Pao-Yen; Ng, Kwok; Crowley, Emmet; Powell, Cormac.

Afiliação

Mujika I; Department of Physiology, Faculty of Medicine and Nursing, University of the Basque Country, Leioa,Basque Country.
Pyne DB; Exercise Science Laboratory, School of Kinesiology, Faculty of Medicine, Universidad Finis Terrae, Santiago,Chile.
Wu PP; University of Canberra Research Institute for Sport and Exercise, Bruce, ACT,Australia.
Ng K; School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD,Australia.
Crowley E; Centre for Data Science, Brisbane, QLD,Australia.
Powell C; Physical Activity for Health Research Cluster, Health Research Institute, University of Limerick, Limerick,Ireland.

Int J Sports Physiol Perform ; 18(11): 1269-1274, 2023 Nov 01.

Article em En | MEDLINE | ID: mdl-37487585

Assuntos

Atletas; Natação; Humanos; Teorema de Bayes; Paris; Modelos Lineares

Palavras-chave

World Championships; data; modeling; performance analysis

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Natação / Atletas Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans País/Região como assunto: Europa Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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