TY - JOUR
T1 - Predicting performance in 4 x 200-m freestyle swimming relay events
AU - Wu, Paul Pao-Yen
AU - Babaei, Toktam
AU - O'Shea, Michael
AU - Mengersen, Kerrie
AU - Drovandi, Christopher
AU - McGibbon, Katie E
AU - Pyne, David B
AU - Mitchell, Lachlan J G
AU - Osborne, Mark A
N1 - Funding Information:
This research was conducted by the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (project number CE140100049) and funded in part by the Australian Government. It was also supported by the Queensland Academy of Sport's Sport Performance Innovation and Knowledge Excellence unit, and by Swimming Australia Limited. Funding was awarded for the project, not to authors Grant numbers - NA URLs: https://acems.org.au/home https://www.qld.gov.au/recreation/sports/ academy/services/spike https://www.swimming. org.au/ NO The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
Copyright © 2021 Wu et al.
PY - 2021/7
Y1 - 2021/7
N2 - AIM: 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.DATA AND METHODS: 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.RESULTS: 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.DISCUSSION: 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.
AB - AIM: 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.DATA AND METHODS: 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.RESULTS: 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.DISCUSSION: 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.
UR - http://www.scopus.com/inward/record.url?scp=85110779237&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0254538
DO - 10.1371/journal.pone.0254538
M3 - Article
C2 - 34265006
SN - 1932-6203
VL - 16
SP - 1
EP - 13
JO - PLoS One
JF - PLoS One
IS - 7
M1 - e0254538
ER -