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Maximum likelihood ranking in racing sports

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  • A. Anderson

Abstract

Most ranking methods used in racing sports are based on the number of points earned in a series of races. In some applications, this method will fail to provide an accurate ranking of competitors based on ability. In particular, rankings will not accurately reflect ability when competitors enter different numbers of races or when the level of competition varies by race. Additionally, point-based rankings are dependent on a subjective points scale. Three alternative models of performance and corresponding maximum likelihood estimation methods are presented that can be used to rank competitors and overcome the shortcomings of point-based rankings. Two methods are based on paired-comparisons among competitors and can be estimated using common binary-choice regression methods; the other is based on the rank-ordered logit model. These methods are valuable tools for stakeholders who need to evaluate the relative abilities of competitors to efficiently allocate resources. Application is demonstrated using results from the 2012 Formula One season, and the results of the maximum likelihood methods are compared to each other and the official point-based rankings.

Suggested Citation

  • A. Anderson, 2014. "Maximum likelihood ranking in racing sports," Applied Economics, Taylor & Francis Journals, vol. 46(15), pages 1778-1787, May.
    • Handle: RePEc:taf:applec:v:46:y:2014:i:15:p:1778-1787
    DOI: 10.1080/00036846.2014.884702
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    Cited by:

    1. D'ora Gr'eta Petr'oczy & L'aszl'o Csat'o, 2019. "Revenue allocation in Formula One: a pairwise comparison approach," Papers 1909.12931, arXiv.org, revised Dec 2020.
    2. 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.
    3. Onur Burak Celik, 2020. "Survival of Formula One Drivers," Social Science Quarterly, Southwestern Social Science Association, vol. 101(4), pages 1271-1281, July.
    4. Ester Gutiérrez & Sebastián Lozano, 2020. "Benchmarking Formula One auto racing circuits: a two stage DEA approach," Operational Research, Springer, vol. 20(4), pages 2059-2083, December.

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