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A mixture-of-modelers approach to forecasting NCAA tournament outcomes

Author

Listed:
  • Yuan Lo-Hua

    (Harvard University – Statistics, Cambridge, Massachusetts, USA)

  • Liu Anthony

    (Harvard University – Statistics, Cambridge, Massachusetts, USA)

  • Yeh Alec

    (Harvard University – Statistics, Cambridge, Massachusetts, USA)

  • Kaufman Aaron

    (Harvard University – Government, Cambridge, Massachusetts, USA)

  • Reece Andrew

    (Harvard University – Psychology, Cambridge, Massachusetts, USA)

  • Bull Peter

    (Harvard University – Institute for Applied Computational Science, Cambridge, Massachusetts, USA)

  • Franks Alex

    (Harvard University – Statistics, Cambridge, Massachusetts, USA)

  • Wang Sherrie

    (Harvard University – Statistics, Cambridge, Massachusetts, USA)

  • Illushin Dmitri

    (Harvard University – Statistics, Cambridge, Massachusetts, USA)

  • Bornn Luke

    (Harvard University – Statistics, Cambridge, Massachusetts, USA)

Abstract

Predicting the outcome of a single sporting event is difficult; predicting all of the outcomes for an entire tournament is a monumental challenge. Despite the difficulties, millions of people compete each year to forecast the outcome of the NCAA men’s basketball tournament, which spans 63 games over 3 weeks. Statistical prediction of game outcomes involves a multitude of possible covariates and information sources, large performance variations from game to game, and a scarcity of detailed historical data. In this paper, we present the results of a team of modelers working together to forecast the 2014 NCAA men’s basketball tournament. We present not only the methods and data used, but also several novel ideas for post-processing statistical forecasts and decontaminating data sources. In particular, we highlight the difficulties in using publicly available data and suggest techniques for improving their relevance.

Suggested Citation

  • Yuan Lo-Hua & Liu Anthony & Yeh Alec & Kaufman Aaron & Reece Andrew & Bull Peter & Franks Alex & Wang Sherrie & Illushin Dmitri & Bornn Luke, 2015. "A mixture-of-modelers approach to forecasting NCAA tournament outcomes," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(1), pages 13-27, March.
    • Handle: RePEc:bpj:jqsprt:v:11:y:2015:i:1:p:13-27:n:4
    DOI: 10.1515/jqas-2014-0056
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    References listed on IDEAS

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    1. Nicolò Cesa Bianchi & Gábor Lugosi, 1999. "Worst-case bounds for the logarithmic loss of predictors," Economics Working Papers 418, Department of Economics and Business, Universitat Pompeu Fabra.
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    Cited by:

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    2. 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.
    3. Paola Zuccolotto & Marco Sandri & Marica Manisera, 2023. "Spatial performance analysis in basketball with CART, random forest and extremely randomized trees," Annals of Operations Research, Springer, vol. 325(1), pages 495-519, June.
    4. Jun Woo Kim & Mar Magnusen & Seunghoon Jeong, 2023. "March Madness prediction: Different machine learning approaches with non‐box score statistics," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(4), pages 2223-2236, June.
    5. Kovalchik, Stephanie & Reid, Machar, 2019. "A calibration method with dynamic updates for within-match forecasting of wins in tennis," International Journal of Forecasting, Elsevier, vol. 35(2), pages 756-766.

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