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Journal of Quantitative Analysis in Sports

An official journal of the American Statistical Association

Editor-in-Chief: Mark Glickman PhD


SCImago Journal Rank (SJR) 2015: 0.288
Source Normalized Impact per Paper (SNIP) 2015: 0.358
Impact per Publication (IPP) 2015: 0.250

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1559-0410
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Formula for success: Multilevel modelling of Formula One Driver and Constructor performance, 1950–2014

1ORCID iD: http://orcid.org/0000-0002-8268-5853 / James Smith2 / Clive E. Sabel2 / Kelvyn Jones2ORCID iD: http://orcid.org/0000-0001-8398-2190

1The University of Sheffield – Sheffield Methods Institute, Sheffield, United Kingdom of Great Britain and Northern Ireland

2University of Bristol – School of Geographical Sciences, Bristol, United Kingdom of Great Britain and Northern Ireland

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, April 2016

Publication History

Published Online:
2016-04-11

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

Abstract

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.

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

Supplementary Article Materials

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