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Journal of Quantitative Analysis in Sports

An official journal of the American Statistical Association

Editor-in-Chief: Mark Glickman PhD

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

Andrew BellORCID iD: http://orcid.org/0000-0002-8268-5853
  • Corresponding author
  • The University of Sheffield – Sheffield Methods Institute, Sheffield, United Kingdom of Great Britain and Northern Ireland
  • ORCID iD: http://orcid.org/0000-0002-8268-5853
  • Email:
/ James Smith
  • University of Bristol – School of Geographical Sciences, Bristol, United Kingdom of Great Britain and Northern Ireland
/ Clive E. Sabel
  • University of Bristol – School of Geographical Sciences, Bristol, United Kingdom of Great Britain and Northern Ireland
/ Kelvyn JonesORCID iD: http://orcid.org/0000-0001-8398-2190
Published Online: 2016-04-11 | DOI: https://doi.org/10.1515/jqas-2015-0050

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.

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

References

  • Allen, J. 2000. Michael Schumacher: Driven to Extremes. London: Bantam Books.

  • Allen, J. 2011. James Allen on F1: 2011 – Vettel Steals the Show. London: Speed Merchants Ltd.

  • Allison, P. D. and N. A. Christakis. 1994. “Logit Models for Sets of Ranked Items.” Sociological Methodology 24:199–228.

  • Alnaser, W. E., S. D. Probert, S. El-Masri, S. E. Al-Khalifa, R. Flanagan, and N. W. Alnaser. 2006. “Bahrain’s Formula-1 Racing Circuit: Energy and Environmental Considerations.” Applied Energy 83(4):352–370.

  • Anderson, A. 2014. “Maximum Likelihood Ranking in Racing Sports.” Applied Economics 46(15):1778–1787.

  • Baker, R. D. and I. G. McHale. 2015. “Deterministic Evolution of Strength in Multiple Comparisons Models: Who Is the Greatest Golfer?” Scandinavian Journal of Statistics 42(1):180–196. [Web of Science]

  • Bekker, J. and W. Lotz. 2009. “Planning Formula One Race Strategies using Discrete-Event Simulation.” Journal of the Operational Research Society 60(7):952–961. [Web of Science]

  • Bell, A. and K. Jones. 2015. “Explaining Fixed Effects: Random Effects Modelling of Time-Series-Cross-Sectional and Panel Data.” Political Science Research and Methods 3(1):133–153.

  • Browne, W. J. 2009. MCMC estimation in MLwiN, Version 2.25. University of Bristol: Centre for Multilevel Modelling.

  • Bullen, N., K. Jones, and C. Duncan. 1997. “Modelling Complexity: Analysing between-Individual and between-Place Variation – A Multilevel Tutorial.” Environment and Planning A 29(4):585–609.

  • Chambers, J., W. Cleveland, B. Kleiner, and P. Tukey. 1983. Graphical Methods for Data Analysis. Boston: Duxbury Press.

  • Collings, T. and S. Edworthy. 2004. “The Formula 1 Years. London: Carlton Books Ltd.

  • Dominy, J. and R. Dominy. 1984. “Aerodynamic Influences on the Performance of the Grand Prix Racing Car.” Journal of Automobile Engineering 198:87–93.

  • Dominy, R. 1992. “Aerodynamics of Grand Prix Cars.” Journal of Automobile Engineering 206:267–274.

  • Eichenberger, R. and D. Stadelmann. 2009. “Who is the Best Formula 1 Driver? An Economic Approach to Evaluating Talent.” Economic Analysis & Policy 39(3):289–406.

  • Gelfand, A. and A. F. M. Smith. 1990. “Sample-Based Approaches to Calculating Marginal Densities.” Journal of the American Statistical Association 85(410):398–409.

  • Gelman, A. 2006. “Prior Distributions for Variance Parameters in Hierarchical Models.” Bayesian Analysis 1(3):515–533. [Web of Science]

  • Glickman, M. E. and J. Hennessy. 2015. “A Stochastic Rank Ordered Logit Model for Rating Multi-Competitor Games and Sports.” Journal of Quantitative Analysis in Sports 11(3):131–144.

  • Goldstein, H. 2010. “Multilevel Statistical Models. 4th ed. Chichester: Wiley.

  • Goldstein, H. and M. Healy. 1995. “The Graphical Presentation of a Collection of Means.” Journal of the Royal Statistical Society Series A-Statistics in Society 158(1):175–177.

  • Hassan, D. 2012. “The History of Motor Sport. New York: Routledge.

  • Henderson, J., K. Foo, H. Lim, and S. Yip. 2010. “Sports Events and Tourism: The Singapore Formula One Grand Prix.” International Journal of Event and Festival Management 1:60–73.

  • Horlock, I. 2009. “Prediction of Formula One Race Results using Driver Characteristics. Project for Masters Degree in Engineering, University of Bristol”.

  • Jenkins, M. 2010. “Technological Discontinuities and Competitive Advantage: A Historical Perspective on Formula 1 Motor Racing 1950–2006.” Journal of Management Studies 47(5):884–910. [Web of Science]

  • Jenkins, M. and S. Floyd. 2001. “Trajectories in the Evolution of Technology: A Multi-Level Study of Competition in Formula 1 Racing.” Organization Studies 22(6):945–969.

  • Jenkins, M. and S. Tallman. 2016. “The Geography of Learning: Ferrari Gestione Sportiva 1929–2008.” Journal of Economic Geography 16(2):447–470. [Web of Science]

  • Judde, C., R. Booth, and R. Brooks. 2013. “Second Place is First of the Losers: An Analysis of Competitive Balance in Formula One.” Journal of Sports Economics 14(4):411–439.

  • King, G. and L. C. Zeng. 2006. “The Dangers of Extreme Counterfactuals.” Political Analysis 14(2):131–159.

  • Loiacono, D., P. L. Lanzi, J. Togelius, E. Onieva, D. A. Pelta, M. V. Butz, T. D. Lonneker, L. Cardamone, D. Perez, Y. Saez, M. Preuss, and J. Quadflieg. 2010. “The 2009 Simulated Car Racing Championship.” IEEE Transactions on Computational Intelligence and AI in Games 2(2):131–147.

  • Muehlbauer, T. 2010. “Relationship between Starting and Finishing Position in Formula One Car Races.” International Journal of Performance Analysis in Sport 10(2):98–102.

  • Phillips, A. J. 2014. “Uncovering Formula One Driver Performances from 1950 to 2013 by Adjusting for Team and Competition Effects.” Journal of Quantitative Analysis in Sports 10(2):261–278.

  • Rasbash, J., C. Charlton, W. J. Browne, M. Healy, and B. Cameron. 2013. “MLwiN version 2.28”. Centre for Multilevel Modelling, University of Bristol.

  • Skrondal, A. and S. Rabe-Hesketh. 2003. “Multilevel Logistic Regression for Polytomous Data and Rankings.” Psychometrika 68(2):267–287.

  • Spiegelhalter, D. J., N. G. Best, B. R. Carlin, and A. van der Linde. 2002. “Bayesian Measures of Model Complexity and Fit.” Journal of the Royal Statistical Society Series B-Statistical Methodology 64:583–616.

  • Spurgeon, B. 2009. “Age Old Question of Whether it is the Car or the Driver that Counts.” http://formula1.about.com/od/formula1101/a/Age-Old-Question-Of-Whether-It-Is-The-Car-Or-The-Driver-That-Counts.htm (accessed 17th March 2015).

  • Spurgeon, B. 2011. “The Art of Racing in the Wet in Formula 1.” http://formula1.about.com/od/howaraceworks/a/The-Art-Of-Racing-In-The-Wet-In-Formula-1.htm (accessed 17th March 2015).

  • Taylor, P. 2013. “Standardized Mortality Ratios.” International Journal of Epidemiology 42(6):1882–1890. [Web of Science]

About the article

Published Online: 2016-04-11

Published in Print: 2016-06-01


Citation Information: Journal of Quantitative Analysis in Sports, ISSN (Online) 1559-0410, ISSN (Print) 2194-6388, DOI: https://doi.org/10.1515/jqas-2015-0050. Export Citation

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