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Predicting the Outcomes of NCAA Basketball Championship Games

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Author Info

  • Stekler Herman O.

    (George Washington University)

  • Klein Andrew

    (George Washington University)

Abstract

This paper uses the difference in seeding ranks to predict the outcome of March Madness games. It updates the Boulier-Stekler method by predicting the outcomes by rounds. We also use the consensus rankings obtained from individuals, systems and poll. We conclude that the consensus rankings were slightly better predictors in the early rounds but had the same limitations as the seedings in the later rounds.

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File URL: http://www.degruyter.com/view/j/jqas.2012.8.issue-1/1559-0410.1373/1559-0410.1373.xml?format=INT
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Bibliographic Info

Article provided by De Gruyter in its journal Journal of Quantitative Analysis in Sports.

Volume (Year): 8 (2012)
Issue (Month): 1 (March)
Pages: 1-10

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Handle: RePEc:bpj:jqsprt:v:8:y:2012:i:1:n:3

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  1. Edward H. Kaplan & Stanley J. Garstka, 2001. "March Madness and the Office Pool," Management Science, INFORMS, vol. 47(3), pages 369-382, March.
  2. Caudill, Steven B., 2003. "Predicting discrete outcomes with the maximum score estimator: the case of the NCAA men's basketball tournament," International Journal of Forecasting, Elsevier, vol. 19(2), pages 313-317.
  3. Boulier, Bryan L. & Stekler, H. O., 1999. "Are sports seedings good predictors?: an evaluation," International Journal of Forecasting, Elsevier, vol. 15(1), pages 83-91, February.
  4. Metrick, Andrew, 1996. "March madness? Strategic behavior in NCAA basketball tournament betting pools," Journal of Economic Behavior & Organization, Elsevier, vol. 30(2), pages 159-172, August.
  5. Harville D.A., 2003. "The Selection or Seeding of College Basketball or Football Teams for Postseason Competition," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 17-27, January.
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