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A Comparison of the Autocorrelation and Variance of NFL Team Strengths Over Time using a Bayesian State-Space Model

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  • Koopmeiners Joseph S.

    (University of Minnesota - Twin Cities)

Abstract

Professional sports leagues are motivated to promote competitive balance in order to maintain fan interest. The National Football League (NFL) has taken several steps to promote competitive balance, most notably free agency and the salary cap, which were instituted prior to the 1994 season. Previous research into competitive balance in sports focused on the variability of team strengths but ignored the year-to-year autocorrelation in team strengths. We present a Bayesian state-space model for paired comparisons that allows regression on the variance parameters. By modeling the variance parameters in a regression framework, we are able to simultaneously compare the variance and autocorrelation in team strengths over time. The autocorrelation of NFL team strengths has decreased over time while there has been little change in the variance in teams strengths since the 1970s.

Suggested Citation

  • Koopmeiners Joseph S., 2012. "A Comparison of the Autocorrelation and Variance of NFL Team Strengths Over Time using a Bayesian State-Space Model," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(3), pages 1-19, October.
    • Handle: RePEc:bpj:jqsprt:v:8:y:2012:i:3:n:2
    DOI: 10.1515/1559-0410.1422
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    References listed on IDEAS

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    1. Mease D., 2003. "A Penalized Maximum Likelihood Approach for the Ranking of College Football Teams Independent of Victory Margins," The American Statistician, American Statistical Association, vol. 57, pages 241-248, November.
    2. Gajewski Byron J, 2006. "There's No Place Like Home: Estimating Intra-Conference Home Field Advantage in College Football Using a Bayesian Piecewise Linear Model," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 2(1), pages 1-26, January.
    3. Andrew Larsen & Aju J. Fenn & Erin Leanne Spenner, 2006. "The Impact of Free Agency and the Salary Cap on Competitive Balance in the National Football League," Journal of Sports Economics, , vol. 7(4), pages 374-390, November.
    4. Mark Glickman, 2001. "Dynamic paired comparison models with stochastic variances," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(6), pages 673-689.
    5. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
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