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A logistic regression/Markov chain model for NCAA basketball

Author

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  • Paul Kvam
  • Joel S. Sokol
Abstract
Each year, more than $3 billion is wagered on the NCAA Division 1 men's basketball tournament. Most of that money is wagered in pools where the object is to correctly predict winners of each game, with emphasis on the last four teams remaining (the Final Four). In this paper, we present a combined logistic regression/Markov chain model for predicting the outcome of NCAA tournament games given only basic input data. Over the past 6 years, our model has been significantly more successful than the other common methods such as tournament seedings, the AP and ESPN/USA Today polls, the RPI, and the Sagarin and Massey ratings. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006.

Suggested Citation

  • Paul Kvam & Joel S. Sokol, 2006. "A logistic regression/Markov chain model for NCAA basketball," Naval Research Logistics (NRL), John Wiley & Sons, vol. 53(8), pages 788-803, December.
    • Handle: RePEc:wly:navres:v:53:y:2006:i:8:p:788-803
    DOI: 10.1002/nav.20170
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. Steven Caudill & Norman Godwin, 2002. "Heterogeneous skewness in binary choice models: Predicting outcomes in the men's NCAA basketball tournament," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(7), pages 991-1001.
    5. Edward H. Kaplan & Stanley J. Garstka, 2001. "March Madness and the Office Pool," Management Science, INFORMS, vol. 47(3), pages 369-382, March.
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    Cited by:

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    3. Yiannis Nikolaidis, 2015. "Building a basketball game strategy through statistical analysis of data," Annals of Operations Research, Springer, vol. 227(1), pages 137-159, April.
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    5. S. S. Dabadghao & B. Vaziri, 2022. "The predictive power of popular sports ranking methods in the NFL, NBA, and NHL," Operational Research, Springer, vol. 22(3), pages 2767-2783, July.
    6. Singh, Aaditya & Scarf, Phil & Baker, Rose, 2023. "A unified theory for bivariate scores in possessive ball-sports: The case of handball," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1099-1112.
    7. Nikolaos Stavropoulos & Alexandra Papadopoulou & Pavlos Kolias, 2021. "Evaluating the Efficiency of Off-Ball Screens in Elite Basketball Teams via Second-Order Markov Modelling," Mathematics, MDPI, vol. 9(16), pages 1-13, August.
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    10. 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.

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