IDEAS home Printed from https://ideas.repec.org/a/bpj/jqsprt/v10y2014i2p18n1.html
   My bibliography  Save this article

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

Author

Listed:
  • Phillips Andrew J. K.

    (Brigham and Women’s Hospital, Harvard Medical School, Division of Sleep Medicine, 221 Longwood Ave, Boston, MA 02148, USA)

Abstract

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.

Suggested Citation

  • Phillips Andrew J. K., 2014. "Uncovering Formula One driver performances from 1950 to 2013 by adjusting for team and competition effects," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(2), pages 261-278, June.
    • Handle: RePEc:bpj:jqsprt:v:10:y:2014:i:2:p:18:n:1
    DOI: 10.1515/jqas-2013-0031
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jqas-2013-0031
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/jqas-2013-0031?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Albert James, 2006. "Pitching Statistics, Talent and Luck, and the Best Strikeout Seasons of All-Time," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 2(1), pages 1-32, January.
    2. Brown Mark & Sokol Joel, 2010. "An Improved LRMC Method for NCAA Basketball Prediction," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(3), pages 1-23, July.
    3. Kvam Paul H, 2011. "Comparing Hall of Fame Baseball Players Using Most Valuable Player Ranks," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(3), pages 1-22, July.
    4. Piette James & Jensen Shane T., 2012. "Estimating Fielding Ability in Baseball Players Over Time," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(3), pages 1-36, October.
    5. 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.
    6. Oberstone Joel, 2011. "Uncovering Europe's Best Goalscorers from the 2009-2010 Season," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(2), pages 1-26, May.
    7. West Brady T & Lamsal Madhur, 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, De Gruyter, vol. 4(3), pages 1-21, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yurko Ronald & Ventura Samuel & Horowitz Maksim, 2019. "nflWAR: a reproducible method for offensive player evaluation in football," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(3), pages 163-183, September.
    2. Vock David Michael & Vock Laura Frances Boehm, 2018. "Estimating the effect of plate discipline using a causal inference framework: an application of the G-computation algorithm," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 14(2), pages 37-56, June.
    3. van Kesteren Erik-Jan & Bergkamp Tom, 2023. "Bayesian analysis of Formula One race results: disentangling driver skill and constructor advantage," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 19(4), pages 273-293, December.
    4. 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.
    5. Karl Andrew T., 2012. "The Sensitivity of College Football Rankings to Several Modeling Choices," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(3), pages 1-44, October.
    6. Aldo Enrietti & Aldo Geuna & Consuelo R Nava & Pier Paolo Patrucco, 2022. "The birth and development of the Italian automotive industry (1894–2015) and the Turin car cluster [Istruzione tecnica e professionale e progresso industriale dalla fine dell’Ottocento al fascismo]," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 31(1), pages 161-185.
    7. Albert Jim, 2013. "Looking at spacings to assess streakiness," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(2), pages 151-163, June.
    8. Albert Jim, 2016. "Improved component predictions of batting and pitching measures," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(2), pages 73-85, June.
    9. Onur Burak Celik, 2020. "Survival of Formula One Drivers," Social Science Quarterly, Southwestern Social Science Association, vol. 101(4), pages 1271-1281, July.
    10. Delen, Dursun & Cogdell, Douglas & Kasap, Nihat, 2012. "A comparative analysis of data mining methods in predicting NCAA bowl outcomes," International Journal of Forecasting, Elsevier, vol. 28(2), pages 543-552.
    11. Michael A. Lapré & Candace Cravey, 2022. "When Success Is Rare and Competitive: Learning from Others’ Success and My Failure at the Speed of Formula One," Management Science, INFORMS, vol. 68(12), pages 8741-8756, December.
    12. Duane W. Rockerbie & Stephen T. Easton, 2022. "Race to the podium: separating and conjoining the car and driver in F1 racing," Applied Economics, Taylor & Francis Journals, vol. 54(54), pages 6272-6285, November.
    13. Baumer Ben S. & Piette James & Null Brad, 2012. "Parsing the Relationship between Baserunning and Batting Abilities within Lineups," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(2), pages 1-19, June.
    14. Albert Jim, 2010. "Using the Count to Measure Pitching Performance," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(4), pages 1-30, October.
    15. B. Jay Coleman, 2014. "Minimum violations and predictive meta‐rankings for college football," Naval Research Logistics (NRL), John Wiley & Sons, vol. 61(1), pages 17-33, February.
    16. Wigness Maggie B & Williams Chadd C & Rowell Michael J, 2010. "A New Iterative Method for Ranking College Football Teams," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(2), pages 1-15, April.
    17. Leman Scotland C. & House Leanna & Szarka John & Nelson Hayley, 2014. "Life on the bubble: Who’s in and who’s out of March Madness?," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(3), pages 315-328, September.
    18. Fry, John & Brighton, Tom & Fanzon, Silvio, 2024. "Faster identification of faster Formula 1 drivers via time-rank duality," Economics Letters, Elsevier, vol. 237(C).
    19. Yurko Ronald & Ventura Samuel & Horowitz Maksim, 2019. "nflWAR: a reproducible method for offensive player evaluation in football," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(3), pages 163-183, September.
    20. Sumit Sarkar & Sooraj Kamath, 2023. "Does luck play a role in the determination of the rank positions in football leagues? A study of Europe’s ‘big five’," Annals of Operations Research, Springer, vol. 325(1), pages 245-260, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:jqsprt:v:10:y:2014:i:2:p:18:n:1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.