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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0254538 |
| _version_ | 1818990468794417152 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-20T19:54:51Z |
| format | Article |
| id | doaj.art-41d36e2df74540c5aa711ffd99d250a8 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-20T19:54:51Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-41d36e2df74540c5aa711ffd99d250a82022-12-21T19:28:11ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01167e025453810.1371/journal.pone.0254538Predicting performance in 4 x 200-m freestyle swimming relay events.Paul Pao-Yen WuToktam BabaeiMichael O'SheaKerrie MengersenChristopher DrovandiKatie E McGibbonDavid B PyneLachlan J G MitchellMark A Osborne<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.https://doi.org/10.1371/journal.pone.0254538 |
| spellingShingle | 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. PLoS ONE |
| title | 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_short | Predicting performance in 4 x 200-m freestyle swimming relay events. |
| title_sort | predicting performance in 4 x 200 m freestyle swimming relay events |
| url | https://doi.org/10.1371/journal.pone.0254538 |
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