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On modeling player fitness in training for team sports with application to professional rugby

Lookup NU author(s): Dr Kevin Wilson



This is the authors' accepted manuscript of an article that has been published in its final definitive form by Sage, 2016.

For re-use rights please refer to the publisher's terms and conditions.


It is increasingly important for professional sports teams to monitor player fitness in order to optimize performance. Models have been put forward linking fitness in training to performance in competition but rely on regular measurements of player fitness. As formal tests for measuring player fitness are typically time-consuming and inconvenient, measurements are taken infrequently. As such, it may be challenging to accurately predict performance in competition as player fitness is unknown.Alternatively, other data, such as how the players are feeling, may be measured more regularly. This data, however, may be biased as players may answer the questions differently and these differences may dominate the data. Linear Mixed Methods and Support Vector Machines were used to estimate player fitness from available covariates at times when explicit measures of fitness are unavailable. Using data provided by Glasgow Warriors Rugby Club, a case study was used to illustrate the application and value of these models. Both models performed well with R2 values ranging from 60% to 85%, demonstrating that the models largely captured the biases introduced by individual players.

Publication metadata

Author(s): Revie M, Wilson KJ, Holdsworth R, Yule S

Publication type: Article

Publication status: Published

Journal: International Journal of Sports Science and Coaching

Year: 2016

Volume: 12

Issue: 2

Pages: 183-193

Print publication date: 01/04/2017

Online publication date: 01/01/2017

Acceptance date: 29/04/2016

Date deposited: 31/10/2016

ISSN (print): 1747-9541

ISSN (electronic): 2048-397X

Publisher: Sage


DOI: 10.1177/1747954117694736


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