IDEAS home Printed from https://ideas.repec.org/a/bpj/jqsprt/v8y2012i3n3.html
   My bibliography  Save this article

The Sensitivity of College Football Rankings to Several Modeling Choices

Author

Listed:
  • Karl Andrew T.

    (Arizona State University)

Abstract

This paper proposes a multiple-membership generalized linear mixed model for ranking college football teams using only their win/loss records. The model results in an intractable, high-dimensional integral due to the random effects structure and nonlinear link function. We use recent data sets to explore the effect of the choice of integral approximation and other modeling assumptions on the rankings. Varying the modeling assumptions sometimes leads to changes in the team rankings that could affect bowl assignments.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:jqsprt:v:8:y:2012:i:3:n:3
    DOI: 10.1515/1559-0410.1471
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/1559-0410.1471
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/1559-0410.1471?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. Dimitris Rizopoulos & Geert Verbeke & Emmanuel Lesaffre, 2009. "Fully exponential Laplace approximations for the joint modelling of survival and longitudinal data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 637-654, June.
    2. Stern H S, 2006. "In Favor of A Quantitative Boycott of the Bowl Championship Series," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 2(1), pages 1-6, January.
    3. 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.
    4. Wang Winnie & Johnston Ron & Jones Kelvyn, 2011. "Home Advantage in American College Football Games: A Multilevel Modelling Approach," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(3), pages 1-20, July.
    5. Gill Ryan & Keating Jerome, 2009. "Assessing Methods for College Football Rankings," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(2), pages 1-21, May.
    6. Frederick Mosteller, 1951. "Remarks on the method of paired comparisons: I. The least squares solution assuming equal standard deviations and equal correlations," Psychometrika, Springer;The Psychometric Society, vol. 16(1), pages 3-9, March.
    7. Stern, Hal S., 2004. "Statistics and the College Football Championship," The American Statistician, American Statistical Association, vol. 58, pages 179-185, August.
    8. West Brady T, 2006. "A Simple and Flexible Rating Method for Predicting Success in the NCAA Basketball Tournament," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 2(3), pages 1-16, July.
    9. 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.
    10. Annis David H. & Craig Bruce A., 2005. "Hybrid Paired Comparison Analysis, with Applications to the Ranking of College Football Teams," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 1(1), pages 1-33, October.
    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. 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.
    2. 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.
    3. Annis David H. & Craig Bruce A., 2005. "Hybrid Paired Comparison Analysis, with Applications to the Ranking of College Football Teams," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 1(1), pages 1-33, October.
    4. Buchman Susan & Kadane Joseph B., 2008. "Reweighting the Bowl Championship Series," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(3), pages 1-13, July.
    5. Karl, Andrew T. & Yang, Yan & Lohr, Sharon L., 2014. "Computation of maximum likelihood estimates for multiresponse generalized linear mixed models with non-nested, correlated random effects," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 146-162.
    6. 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.
    7. Murray Thomas A., 2017. "Ranking ultimate teams using a Bayesian score-augmented win-loss model," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 13(2), pages 63-78, June.
    8. Daniel C. Hickman & Andrew G. Meyer, 2017. "Does Athletic Success Influence Persistence At Higher Education Institutions? New Evidence Using Panel Data," Contemporary Economic Policy, Western Economic Association International, vol. 35(4), pages 658-676, October.
    9. Ludden Ian G. & Khatibi Arash & King Douglas M. & Jacobson Sheldon H., 2020. "Models for generating NCAA men’s basketball tournament bracket pools," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(1), pages 1-15, March.
    10. repec:ebl:ecbull:v:4:y:2007:i:34:p:1-7 is not listed on IDEAS
    11. Bernardi, Mauro & Costola, Michele, 2019. "High-dimensional sparse financial networks through a regularised regression model," SAFE Working Paper Series 244, Leibniz Institute for Financial Research SAFE.
    12. Zhang, Zili & Charalambous, Christiana & Foster, Peter, 2023. "A Gaussian copula joint model for longitudinal and time-to-event data with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
    13. Tang, Nian-Sheng & Tang, An-Min & Pan, Dong-Dong, 2014. "Semiparametric Bayesian joint models of multivariate longitudinal and survival data," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 113-129.
    14. Dimitris Rizopoulos, 2011. "Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data," Biometrics, The International Biometric Society, vol. 67(3), pages 819-829, September.
    15. Philip Yu, 2000. "Bayesian analysis of order-statistics models for ranking data," Psychometrika, Springer;The Psychometric Society, vol. 65(3), pages 281-299, September.
    16. Barrow Daniel & Drayer Ian & Elliott Peter & Gaut Garren & Osting Braxton, 2013. "Ranking rankings: an empirical comparison of the predictive power of sports ranking methods," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(2), pages 187-202, June.
    17. Lenten, Liam J.A., 2011. "The extent to which unbalanced schedules cause distortions in sports league tables," Economic Modelling, Elsevier, vol. 28(1), pages 451-458.
    18. Kwan, Ying Keung & Ip, Wai Cheung & Kwan, Patrick, 2000. "A crime index with Thurstone's scaling of crime severity," Journal of Criminal Justice, Elsevier, vol. 28(3), pages 237-244.
    19. Lundh Torbjörn, 2006. "Which Ball is the Roundest? - A Suggested Tournament Stability Index," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 2(3), pages 1-23, July.
    20. Rui Martins, 2022. "A flexible link for joint modelling longitudinal and survival data accounting for individual longitudinal heterogeneity," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(1), pages 41-61, March.
    21. Michael Fligner & Joseph Verducci, 1990. "Posterior probabilities for a consensus ordering," Psychometrika, Springer;The Psychometric Society, vol. 55(1), pages 53-63, March.

    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:8:y:2012:i:3:n:3. 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.