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Models for Third Down Conversion in the National Football League

Author

Listed:
  • Cafarelli Ryan

    (Southern Illinois University Edwardsville)

  • Rigdon Christopher J.

    (Southern Illinois UniversityEdwardsville)

  • Rigdon Steven E.

    (Saint Louis University)

Abstract

Several models are proposed for the probability of converting a third down attempt in the National Football League. The probability, which can depend on the number of yards to go, the strength of the offense, and the strength of the defense, leads to a logistic regression. We approach the problem through a hierarchical Bayes model and estimate parameters by using Markov chain Monte Carlo (MCMC). This MCMC estimation in the context of a hierarchical Bayes model may be relevant in other sports situations where a probability depends on the difference of strengths of the two teams. We find that the statistic "third-down conversion rate" to be a nearly meaningless measure of the efficiency of an offense. Even when this is adjusted for yards to go for a first down, there is little evidence that teams differ in their ability to achieve a first down on a third down conversion.

Suggested Citation

  • Cafarelli Ryan & Rigdon Christopher J. & Rigdon Steven E., 2012. "Models for Third Down Conversion in the National Football League," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(3), pages 1-26, October.
    • Handle: RePEc:bpj:jqsprt:v:8:y:2012:i:3:n:1
    DOI: 10.1515/1559-0410.1383
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    References listed on IDEAS

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    1. Steven Rigdon & Robert Tsutakawa, 1983. "Parameter estimation in latent trait models," Psychometrika, Springer;The Psychometric Society, vol. 48(4), pages 567-574, December.
    2. R. Bock & Murray Aitkin, 1981. "Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm," Psychometrika, Springer;The Psychometric Society, vol. 46(4), pages 443-459, December.
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    Cited by:

    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. Santos-Fernandez Edgar & Wu Paul & Mengersen Kerrie L., 2019. "Bayesian statistics meets sports: a comprehensive review," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(4), pages 289-312, December.

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