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

Author

Listed:
  • Bell Andrew

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

  • Smith James

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

  • Sabel Clive E.

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

  • Jones Kelvyn

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

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.

Suggested Citation

  • Bell Andrew & Smith James & Sabel Clive E. & Jones Kelvyn, 2016. "Formula for success: Multilevel modelling of Formula One Driver and Constructor performance, 1950–2014," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(2), pages 99-112, June.
    • Handle: RePEc:bpj:jqsprt:v:12:y:2016:i:2:p:99-112:n:3
    DOI: 10.1515/jqas-2015-0050
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    References listed on IDEAS

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    1. Anders Skrondal & Sophia Rabe-Hesketh, 2003. "Multilevel logistic regression for polytomous data and rankings," Psychometrika, Springer;The Psychometric Society, vol. 68(2), pages 267-287, June.
    2. Alnaser, W.E. & Probert, S.D. & El-Masri, S. & Al-Khalifa, S.E. & Flanagan, R. & Alnaser, N.W., 2006. "Bahrain's Formula-1 racing circuit: energy and environmental considerations," Applied Energy, Elsevier, vol. 83(4), pages 352-370, April.
    3. Reiner Eichenberger & David Stadelmann, 2009. "Who Is The Best Formula 1 Driver? An Economic Approach to Evaluating Talent," Economic Analysis and Policy, Elsevier, vol. 39(3), pages 389-406, December.
    4. repec:bla:jomstd:v:47:y:2010:i:s1:p:884-910 is not listed on IDEAS
    5. A. Anderson, 2014. "Maximum likelihood ranking in racing sports," Applied Economics, Taylor & Francis Journals, vol. 46(15), pages 1778-1787, May.
    6. Chris Judde & Ross Booth & Robert Brooks, 2013. "Second Place Is First of the Losers," Journal of Sports Economics, , vol. 14(4), pages 411-439, August.
    7. Rose D. Baker & Ian G. McHale, 2015. "Deterministic Evolution of Strength in Multiple Comparisons Models: Who is the Greatest Golfer?," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 180-196, March.
    8. Bell, Andrew & Jones, Kelvyn, 2015. "Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data," Political Science Research and Methods, Cambridge University Press, vol. 3(1), pages 133-153, January.
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    Cited by:

    1. Mourao, Paulo Reis, 2018. "Surviving in the shadows—An economic and empirical discussion about the survival of the non-winning F1 drivers," Economic Analysis and Policy, Elsevier, vol. 59(C), pages 54-68.

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