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Building an NCAA men’s basketball predictive model and quantifying its success

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  • Lopez Michael J.

    (Skidmore College – Mathematics and Computer Science, 815 N. Broadway Harder Hall, Saratoga Springs, New York 12866, USA)

  • Matthews Gregory J.

    (Loyola University Chicago – Mathematics and Statistics, Chicago, Illinois, USA)

Abstract

Computing and machine learning advancements have led to the creation of many cutting-edge predictive algorithms, some of which have been demonstrated to provide more accurate forecasts than traditional statistical tools. In this manuscript, we provide evidence that the combination of modest statistical methods with informative data can meet or exceed the accuracy of more complex models when it comes to predicting the NCAA men’s basketball tournament. First, we describe a prediction model that merges the point spreads set by Las Vegas sportsbooks with possession based team efficiency metrics by using logistic regressions. The set of probabilities generated from this model most accurately predicted the 2014 tournament, relative to approximately 400 competing submissions, as judged by the log loss function. Next, we attempt to quantify the degree to which luck played a role in the success of this model by simulating tournament outcomes under different sets of true underlying game probabilities. We estimate that under the most optimistic of game probability scenarios, our entry had roughly a 12% chance of outscoring all competing submissions and just less than a 50% chance of finishing with one of the ten best scores.

Suggested Citation

  • Lopez Michael J. & Matthews Gregory J., 2015. "Building an NCAA men’s basketball predictive model and quantifying its success," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(1), pages 5-12, March.
    • Handle: RePEc:bpj:jqsprt:v:11:y:2015:i:1:p:5-12:n:5
    DOI: 10.1515/jqas-2014-0058
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    References listed on IDEAS

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    1. Mark W. Nichols, 2014. "The Impact of Visiting Team Travel on Game Outcome and Biases in NFL Betting Markets," Journal of Sports Economics, , vol. 15(1), pages 78-96, February.
    2. L. Lee Colquitt & Norman H. Godwin & Steven B. Caudill, 2001. "Testing Efficiency Across Markets: Evidence from the NCAA Basketball Betting Market," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 28(1‐2), pages 231-248, January.
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    5. L. Lee Colquitt & Norman H. Godwin & Steven B. Caudill, 2001. "Testing Efficiency Across Markets: Evidence from the NCAA Basketball Betting Market," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 28(1-2), pages 231-248.
    6. Kenneth Linna & Evan Moore & Rodney Paul & Andrew Weinbach, 2014. "The Effects of the Clock and Kickoff Rule Changes on Actual and Market-Based Expected Scoring in NCAA Football," IJFS, MDPI, vol. 2(2), pages 1-14, April.
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    Cited by:

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    3. Ge, Qi, 2018. "Sports sentiment and tipping behavior," Journal of Economic Behavior & Organization, Elsevier, vol. 145(C), pages 95-113.
    4. Dutta Shouvik & Jacobson Sheldon H. & Sauppe Jason J., 2017. "Identifying NCAA tournament upsets using Balance Optimization Subset Selection," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 13(2), pages 79-93, June.

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