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Journal of Quantitative Analysis in Sports

An official journal of the American Statistical Association

Editor-in-Chief: Steve Rigdon, PhD

CiteScore 2017: 0.67

SCImago Journal Rank (SJR) 2017: 0.290
Source Normalized Impact per Paper (SNIP) 2017: 0.853

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Volume 12, Issue 2


Volume 1 (2005)

Formula for success: Multilevel modelling of Formula One Driver and Constructor performance, 1950–2014

Andrew BellORCID iD: http://orcid.org/0000-0002-8268-5853 / James Smith
  • University of Bristol – School of Geographical Sciences, Bristol, United Kingdom of Great Britain and Northern Ireland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Clive E. Sabel
  • University of Bristol – School of Geographical Sciences, Bristol, United Kingdom of Great Britain and Northern Ireland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Kelvyn JonesORCID iD: http://orcid.org/0000-0001-8398-2190
Published Online: 2016-04-11 | DOI: https://doi.org/10.1515/jqas-2015-0050


This paper uses random-coefficient models and (a) finds rankings of who are the best formula 1 (F1) drivers of all time, conditional on team performance; (b) quantifies how much teams and drivers matter; and (c) quantifies how team and driver effects vary over time and under different racing conditions. The points scored by drivers in a race (standardised across seasons and Normalised) is used as the response variable in a cross-classified multilevel model that partitions variance into team, team-year and driver levels. These effects are then allowed to vary by year, track type and weather conditions using complex variance functions. Juan Manuel Fangio is found to be the greatest driver of all time. Team effects are shown to be more important than driver effects (and increasingly so over time), although their importance may be reduced in wet weather and on street tracks. A sensitivity analysis was undertaken with various forms of the dependent variable; this did not lead to substantively different conclusions. We argue that the approach can be applied more widely across the social sciences, to examine individual and team performance under changing conditions.

This article offers supplementary material which is provided at the end of the article.

Keywords: cross-classified models; formula 1; MCMC; multilevel models; performance; sport


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About the article

Published Online: 2016-04-11

Published in Print: 2016-06-01

Citation Information: Journal of Quantitative Analysis in Sports, Volume 12, Issue 2, Pages 99–112, ISSN (Online) 1559-0410, ISSN (Print) 2194-6388, DOI: https://doi.org/10.1515/jqas-2015-0050.

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