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Predicting Results of March Madness Using Three Different Methods

Author

Listed:
  • Gang Shen
  • Di Gao
  • Qian Wen
  • Rhonda Magel

Abstract

Three methods are used to predict the results for two years of the Men’s NCAA Division1 March Madness Basketball Tournament. These methods include using the machine-learning method of the support vector machine, the data mining method of the random forest, and a newly developed Bayesian model using the property of probability self-consistency as an extension of Shen et al. (2015). The random forest method and the support vector machine method are found to possibly do slightly better than the Bayes model, although the results vary. Possible ideas as to how to extend the Bayes model are given.

Suggested Citation

  • Gang Shen & Di Gao & Qian Wen & Rhonda Magel, 2016. "Predicting Results of March Madness Using Three Different Methods," Journal of Sports Research, Conscientia Beam, vol. 3(1), pages 10-17.
    • Handle: RePEc:pkp:josres:v:3:y:2016:i:1:p:10-17:id:2784
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    File URL: https://archive.conscientiabeam.com/index.php/90/article/view/2784/4348
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    Cited by:

    1. 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.

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