Elite players perceptions of football playing surfaces - an ordinal regression model IR.pdf (1.15 MB)

Elite players’ perceptions of football playing surfaces: a mixed effects ordinal logistic regression model of players’ perceptions

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journal contribution
posted on 15.06.2016 by Alun Owen, Aimee Mears, Paul Osei-Owusu, Andy Harland, Jonathan Roberts
The aim of this study was to determine potential explanatory factors that may be associated with different attitudes amongst the global population of elite footballers to the use of different surfaces for football. A questionnaire was used to capture elite football players’ perceptions of playing surfaces and a mixed effects ordinal logistic regression model was used to explore potential explanatory factors of players’ perceptions. In total, responses from 1129 players from 44 different countries were analysed. The majority of players expressed a strong preference for the use of Natural Turf pitches over alternatives such as Artificial Turf. The regression model, with a players’ country as a random effect, indicated that players were less favourable towards either Natural Turf or Artificial Turf where there was perceived to be greater variability in surface qualities or the surface was perceived to have less desirable properties. Player’s surface experience was also linked to their overall attitudes, with a suggestion that the quality of the Natural Turf surface players experienced dictated players’ support for Artificial Turf.

Funding

The authors would like to thank FIFA, its member associations and FIFPro for their assistance in this project.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Journal of Applied Statistics

Pages

1 - 17

Citation

OWEN, A. ... et al, 2016. Elite players’ perceptions of football playing surfaces: a mixed effects ordinal logistic regression model of players’ perceptions. Journal of Applied Statistics, 44 (3), pp. 554-570.

Publisher

© Taylor and Francis

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2016

Notes

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Applied Statistics on 02 May 2016, available online: http://dx.doi.org/10.1080/02664763.2016.1177500

ISSN

0266-4763

eISSN

1360-0532

Language

en

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