Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter May 29, 2014

Uncovering Formula One driver performances from 1950 to 2013 by adjusting for team and competition effects

  • Andrew J. K. Phillips EMAIL logo


Subjective ratings of the best drivers in the history of Formula One are common, but objective analyses are hampered by the difficulties involved in comparing drivers who raced for different teams and in different eras. Here, we present a new method for comparing performances within and between eras. Using a statistical model, we estimate driver and team contributions to performance, as well as the effects of competition with other drivers. By adjusting for team and competition effects, underlying driver performances are revealed. Using this method, we compute adjusted scoring rates for 1950–2013. Driver performances are then compared using: (i) peak performances for 1-year, 3-year, and 5-year intervals; and (ii) number of championships. Overall, these comparisons rank Clark, Stewart, Fangio, Alonso, and Schumacher as the five greatest drivers. We confirm the model’s accuracy by comparing its performance predictions to 2010–2013 lap-time data. The results of the analysis are generally in good agreement with expert opinions regarding driver performances. However, the model also identifies several undervalued and overvalued driver performances, which are discussed. This is the first objective method for comparing Formula One drivers that has yielded sensible results. The model adds a valuable perspective to previous subjective analyses.

Corresponding author: Andrew J. K. Phillips, Brigham and Women’s Hospital, Harvard Medical School, Division of Sleep Medicine, 221 Longwood Ave, Boston, MA 02148, USA, Phone: +617-278-0057, e-mail:


The author thanks OJ Walch, BM Whiteside, CL Murgo, the two anonymous reviewers, and the journal editor for their helpful comments on earlier versions of this paper.


Albert, J. 2006. “Pitching Statistics, Talent and Luck, and The Best Strikeout Seasons of All Time.” Journal of Quantitative Analysis in Sports 2(1):Article 2.Search in Google Scholar

Aitken, T. 2004. “Statistical Analysis of Top Performers in Sport with Emphasis on the Relevance of Outliers.” Sports Engineering 7:75–88.10.1007/BF02915919Search in Google Scholar

Autosport. 2009. “F1’s Greatest Drivers.” Accessed January 12, 2013 ( in Google Scholar

Berry, S. M., C. S. Reese, and P. D. Larkey. 1999. “Bridging Different eras in Sports.” Journal of the American Statistical Association 94:661–676.10.1080/01621459.1999.10474163Search in Google Scholar

Brown, M. and J. Sokol. 2010. “An Improved LRMC Method for NCAA Basketball Prediction.” Journal of Quantitative Analysis in Sports 6(3).10.2202/1559-0410.1202Search in Google Scholar

Cohea, C. and M. E. Payton. 2011. “Relationships Between Player Actions and Game Outcomes in American FOOTBALL.” Sportscience 15:19–24.Search in Google Scholar

Davis, C. 2000. The Best of the Best: A New Look at the Best Cricketers and their Changing Times. Sydney, Australia: ABC Books.Search in Google Scholar

Eichenberger, R. and D. Stadelmann. 2009. “Who is the Best Formula One Driver? An Economic Approach to Evaluating Driver Talent.” Economic Analysis & Policy 39:389–406.10.1016/S0313-5926(09)50035-5Search in Google Scholar

F1 Racing. 1997. 100 Greatest Drivers of all time. June 1997 issue.Search in Google Scholar

F1 Racing. 2004. 100 greatest Drivers of all time. June 2004 issue.Search in Google Scholar

F1 Racing. 2008. 100 greatest Drivers Ever. May 2008 issue.Search in Google Scholar

F1 Racing. 2012. Man of the Year. December 2012 issue.Search in Google Scholar

Henry, A. 2008. The Top 100 F1 Drivers of All Time. Cambridge, UK: Icon Books Ltd.Search in Google Scholar

Hilton, C. 1991. Ayrton Senna: The Hard Edge of Genius. London, UK: Corgi Books.Search in Google Scholar

Jones, B. 1995. The Ultimate Encyclopedia of Formula One. London, UK: Carlton Books.Search in Google Scholar

Kvam, P. H. 2011. “Comparing Hall of Fame Baseball Players Using Most Valuable Player Ranks.” Journal of Quantitative Analysis in Sports 7(3):Article 19.10.2202/1559-0410.1337Search in Google Scholar

Kvam, P. H. and J. S. Sokol. 2006. “A Logistic Regression/Markov Chain Model for NCAA Basketball.” Naval Research Logistics 53:788–803.10.1002/nav.20170Search in Google Scholar

Oberstone, J. 2011. “Uncovering Europe’s Best Goalscorers from the 2009-2010 Season.” Journal of Quantitative Analysis in Sports 7(1):Article 1.10.2202/1559-0410.1341Search in Google Scholar

Piette, J. and S. T. Jensen. 2012. “Estimating Fielding Ability in Baseball Players Over Time.” Journal of Quantitative Analysis in Sports 8(3).10.1515/1559-0410.1463Search in Google Scholar

Rendall, I. 1991. The Power and The Glory: A Century of Motor Racing. London, UK: BBC Books.Search in Google Scholar

Sonas, J. 2005. “Chessmetrics.” Accessed January 12, 2013 ( in Google Scholar

Smith, R. 2011. Formula 1: All the Races: The World Championship Story Race-by-Race: 1950-2011. Sparkford, Yeovil, Somerset, UK: Haynes Publishing.Search in Google Scholar

West, B. T. 2006. “A Simple and Flexible Rating Method for Predicting Success in the NCAA Basketball Tournament.” Journal of Quantitative Analysis in Sports 2(3).10.2202/1559-0410.1039Search in Google Scholar

West, B. T. and M. Lamsal. 2008. “A New Application of Linear Modeling in the Prediction of College Football Bowl Outcomes and the Development of Team Ratings.” Journal of Quantitative Analysis in Sports 4(3).10.2202/1559-0410.1115Search in Google Scholar

White, C. and S. Berry. 2012. “Tiered Polychotomous Regression: Ranking NFL Quarterbacks.” American Statistician 56: 10–21.10.1198/000313002753631312Search in Google Scholar

Published Online: 2014-5-29
Published in Print: 2014-6-1

©2014 by Walter de Gruyter Berlin/Boston

Downloaded on 23.2.2024 from
Scroll to top button