Predicting performance in 4 x 200-m freestyle swimming relay events.

<h4>Aim</h4>The aim was to predict and understand variations in swimmer performance between individual and relay events, and develop a predictive model for the 4x200-m swimming freestyle relay event to help inform team selection and strategy.<h4>Data and methods</h4>Race data...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Paul Pao-Yen Wu, Toktam Babaei, Michael O'Shea, Kerrie Mengersen, Christopher Drovandi, Katie E McGibbon, David B Pyne, Lachlan J G Mitchell, Mark A Osborne
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/41d36e2df74540c5aa711ffd99d250a8
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:41d36e2df74540c5aa711ffd99d250a8
record_format	dspace
spelling	oai:doaj.org-article:41d36e2df74540c5aa711ffd99d250a82021-12-02T20:09:11ZPredicting performance in 4 x 200-m freestyle swimming relay events.1932-620310.1371/journal.pone.0254538https://doaj.org/article/41d36e2df74540c5aa711ffd99d250a82021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254538https://doaj.org/toc/1932-6203<h4>Aim</h4>The aim was to predict and understand variations in swimmer performance between individual and relay events, and develop a predictive model for the 4x200-m swimming freestyle relay event to help inform team selection and strategy.<h4>Data and methods</h4>Race data for 716 relay finals (4 x 200-m freestyle) from 14 international competitions between 2010-2018 were analysed. Individual 200-m freestyle season best time for the same year was located for each swimmer. Linear regression and machine learning was applied to 4 x 200-m swimming freestyle relay events.<h4>Results</h4>Compared to the individual event, the lowest ranked swimmer in the team (-0.62 s, CI = [-0.94, -0.30]) and American swimmers (-0.48 s [-0.89, -0.08]) typically swam faster 200-m times in relay events. Random forest models predicted gold, silver, bronze and non-medal with 100%, up to 41%, up to 63%, and 93% sensitivity, respectively.<h4>Discussion</h4>Team finishing position was strongly associated with the differential time to the fastest team (mean decrease in Gini (MDG) when this variable was omitted = 31.3), world rankings of team members (average ranking MDG of 18.9), and the order of swimmers (MDG = 6.9). Differential times are based on the sum of individual swimmer's season's best times, and along with world rankings, reflect team strength. In contrast, the order of swimmers reflects strategy. This type of analysis could assist coaches and support staff in selecting swimmers and team orders for relay events to enhance the likelihood of success.Paul Pao-Yen WuToktam BabaeiMichael O'SheaKerrie MengersenChristopher DrovandiKatie E McGibbonDavid B PyneLachlan J G MitchellMark A OsbornePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254538 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Paul Pao-Yen Wu Toktam Babaei Michael O'Shea Kerrie Mengersen Christopher Drovandi Katie E McGibbon David B Pyne Lachlan J G Mitchell Mark A Osborne Predicting performance in 4 x 200-m freestyle swimming relay events.
description	<h4>Aim</h4>The aim was to predict and understand variations in swimmer performance between individual and relay events, and develop a predictive model for the 4x200-m swimming freestyle relay event to help inform team selection and strategy.<h4>Data and methods</h4>Race data for 716 relay finals (4 x 200-m freestyle) from 14 international competitions between 2010-2018 were analysed. Individual 200-m freestyle season best time for the same year was located for each swimmer. Linear regression and machine learning was applied to 4 x 200-m swimming freestyle relay events.<h4>Results</h4>Compared to the individual event, the lowest ranked swimmer in the team (-0.62 s, CI = [-0.94, -0.30]) and American swimmers (-0.48 s [-0.89, -0.08]) typically swam faster 200-m times in relay events. Random forest models predicted gold, silver, bronze and non-medal with 100%, up to 41%, up to 63%, and 93% sensitivity, respectively.<h4>Discussion</h4>Team finishing position was strongly associated with the differential time to the fastest team (mean decrease in Gini (MDG) when this variable was omitted = 31.3), world rankings of team members (average ranking MDG of 18.9), and the order of swimmers (MDG = 6.9). Differential times are based on the sum of individual swimmer's season's best times, and along with world rankings, reflect team strength. In contrast, the order of swimmers reflects strategy. This type of analysis could assist coaches and support staff in selecting swimmers and team orders for relay events to enhance the likelihood of success.
format	article
author	Paul Pao-Yen Wu Toktam Babaei Michael O'Shea Kerrie Mengersen Christopher Drovandi Katie E McGibbon David B Pyne Lachlan J G Mitchell Mark A Osborne
author_facet	Paul Pao-Yen Wu Toktam Babaei Michael O'Shea Kerrie Mengersen Christopher Drovandi Katie E McGibbon David B Pyne Lachlan J G Mitchell Mark A Osborne
author_sort	Paul Pao-Yen Wu
title	Predicting performance in 4 x 200-m freestyle swimming relay events.
title_short	Predicting performance in 4 x 200-m freestyle swimming relay events.
title_full	Predicting performance in 4 x 200-m freestyle swimming relay events.
title_fullStr	Predicting performance in 4 x 200-m freestyle swimming relay events.
title_full_unstemmed	Predicting performance in 4 x 200-m freestyle swimming relay events.
title_sort	predicting performance in 4 x 200-m freestyle swimming relay events.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/41d36e2df74540c5aa711ffd99d250a8
work_keys_str_mv	AT paulpaoyenwu predictingperformancein4x200mfreestyleswimmingrelayevents AT toktambabaei predictingperformancein4x200mfreestyleswimmingrelayevents AT michaeloshea predictingperformancein4x200mfreestyleswimmingrelayevents AT kerriemengersen predictingperformancein4x200mfreestyleswimmingrelayevents AT christopherdrovandi predictingperformancein4x200mfreestyleswimmingrelayevents AT katieemcgibbon predictingperformancein4x200mfreestyleswimmingrelayevents AT davidbpyne predictingperformancein4x200mfreestyleswimmingrelayevents AT lachlanjgmitchell predictingperformancein4x200mfreestyleswimmingrelayevents AT markaosborne predictingperformancein4x200mfreestyleswimmingrelayevents
_version_	1718375119922921472

Predicting performance in 4 x 200-m freestyle swimming relay events.

Ejemplares similares