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

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

doi:10.1371/journal.pone.0254538

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

PLoS One. 2021 Jul 15;16(7):e0254538. doi: 10.1371/journal.pone.0254538. eCollection 2021.

Authors

Paul Pao-Yen Wu^{1

2}, Toktam Babaei^{1

2}, Michael O'Shea^{1

2}, Kerrie Mengersen^{1

2}, Christopher Drovandi^{1

2}, Katie E McGibbon³, David B Pyne⁴, Lachlan J G Mitchell³, Mark A Osborne⁵

Affiliations

¹ School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.
² ARC Centre of Excellence in Mathematical and Statistical Frontiers (ACEMS), Melbourne, VIC, Australia.
³ Queensland Academy of Sport, Nathan, QLD, Australia.
⁴ University of Canberra Research Institute for Sport and Exercise, Bruce, ACT, Australia.
⁵ School of Human Movement & Nutrition Sciences, University of Queensland, Brisbane, QLD, Australia.

Abstract

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Athletic Performance
Competitive Behavior*
Swimming*

Grants and funding

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.