IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v237y2024ics016517652400154x.html
   My bibliography  Save this article

Faster identification of faster Formula 1 drivers via time-rank duality

Author

Listed:
  • Fry, John
  • Brighton, Tom
  • Fanzon, Silvio

Abstract

Two natural ways of modelling Formula 1 race outcomes are a probabilistic approach, based on the exponential distribution, and econometric modelling of the ranks. Both approaches lead to exactly soluble race-winning probabilities. Equating race-winning probabilities leads to a set of equivalent parametrisations. This time-rank duality is attractive theoretically and leads to quicker ways of dis-entangling driver and car level effects.

Suggested Citation

  • Fry, John & Brighton, Tom & Fanzon, Silvio, 2024. "Faster identification of faster Formula 1 drivers via time-rank duality," Economics Letters, Elsevier, vol. 237(C).
  • Handle: RePEc:eee:ecolet:v:237:y:2024:i:c:s016517652400154x
    DOI: 10.1016/j.econlet.2024.111671
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S016517652400154X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.econlet.2024.111671?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. Singh, Aaditya & Scarf, Phil & Baker, Rose, 2023. "A unified theory for bivariate scores in possessive ball-sports: The case of handball," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1099-1112.
    2. 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.
    3. Scarf, Phil & Parma, Rishikesh & McHale, Ian, 2019. "On outcome uncertainty and scoring rates in sport: The case of international rugby union," European Journal of Operational Research, Elsevier, vol. 273(2), pages 721-730.
    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. 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.
    2. Craig, J. Dean & Winchester, Niven, 2021. "Predicting the national football league potential of college quarterbacks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 733-743.
    3. Michal Friesl & Jan Libich & Petr Stehlík, 2020. "Fixing ice hockey’s low scoring flip side? Just flip the sides," Annals of Operations Research, Springer, vol. 292(1), pages 27-45, September.
    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. Buraimo, Babatunde & Forrest, David & McHale, Ian G. & Tena, J.D., 2022. "Armchair fans: Modelling audience size for televised football matches," European Journal of Operational Research, Elsevier, vol. 298(2), pages 644-655.
    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. Federico Fioravanti & Fernando Tohmé & Fernando Delbianco & Alejandro Neme, 2021. "Effort of rugby teams according to the bonus point system: a theoretical and empirical analysis," International Journal of Game Theory, Springer;Game Theory Society, vol. 50(2), pages 447-474, June.
    8. Onur Burak Celik, 2020. "Survival of Formula One Drivers," Social Science Quarterly, Southwestern Social Science Association, vol. 101(4), pages 1271-1281, July.
    9. 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.
    10. 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.
    11. László Csató & Dóra Gréta Petróczy, 2024. "Bibliometric indices as a measure of performance and competitive balance in the knockout stage of the UEFA Champions League," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 32(4), pages 961-988, December.
    12. Avila-Cano, Antonio & Owen, P. Dorian & Triguero-Ruiz, Francisco, 2023. "Measuring competitive balance in sports leagues that award bonus points, with an application to rugby union," European Journal of Operational Research, Elsevier, vol. 309(2), pages 939-952.
    13. Fry John & Smart Oliver & Serbera Jean-Philippe & Klar Bernhard, 2021. "A Variance Gamma model for Rugby Union matches," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(1), pages 67-75, March.
    14. Federico Fioravanti & Fernando Delbianco & Fernando Tohmé, 2023. "The relative importance of ability, luck and motivation in team sports: a Bayesian model of performance in the English Rugby Premiership," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 715-731, September.
    15. 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.
    16. Encarnación Algaba & Stefano Moretti & Eric Rémila & Philippe Solal, 2021. "Lexicographic solutions for coalitional rankings," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 57(4), pages 817-849, November.
    17. Collingwood, James A.P. & Wright, Michael & Brooks, Roger J., 2023. "Simulating the progression of a professional snooker frame," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1286-1299.
    18. 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.
    19. Singh, Aaditya & Scarf, Phil & Baker, Rose, 2023. "A unified theory for bivariate scores in possessive ball-sports: The case of handball," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1099-1112.
    20. Gyimesi, András, 2021. "Hosszú távú versenyegyensúly egy csapatsportliga közgazdasági modelljében [Long-term competitive balance in an economic model of a team sports league]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(6), pages 585-616.

    More about this item

    Keywords

    Exponential distribution; Formula 1; Regression; Time-rank duality;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • L8 - Industrial Organization - - Industry Studies: Services
    • Z2 - Other Special Topics - - Sports Economics

    Statistics

    Access and download statistics

    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:eee:ecolet:v:237:y:2024:i:c:s016517652400154x. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    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.