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Identifying NCAA tournament upsets using Balance Optimization Subset Selection

Author

Listed:
  • Dutta Shouvik
  • Jacobson Sheldon H.

    (University of Illinois – Computer Science, Urbana, IL, USA)

  • Sauppe Jason J.

    (University of Wisconsin–La Crosse, Computer Science, La Crosse, WI, USA)

Abstract

The NCAA basketball tournament attracts over 60 million people who fill out a bracket to try to predict the outcome of every tournament game correctly. Predictions are often made on the basis of instinct, statistics, or a combination of the two. This paper proposes a technique to select round-of-64 upsets in the tournament using a Balance Optimization Subset Selection model. The model determines which games feature match-ups that are statistically most similar to the match-ups in historical upsets. The technique is then applied to the tournament in each of the 13 years from 2003 to 2015 in order to select two games as potential upsets each year. Of the 26 selected games, 10 (38.4%) were actual upsets, which is more than twice as many as the expected number of correct selections when using a weighted random selection method.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:jqsprt:v:13:y:2017:i:2:p:79-93:n:1
    DOI: 10.1515/jqas-2016-0062
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    References listed on IDEAS

    as
    1. Alexander G. Nikolaev & Sheldon H. Jacobson & Wendy K. Tam Cho & Jason J. Sauppe & Edward C. Sewell, 2013. "Balance Optimization Subset Selection (BOSS): An Alternative Approach for Causal Inference with Observational Data," Operations Research, INFORMS, vol. 61(2), pages 398-412, April.
    2. 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.
    3. Jason J. Sauppe & Sheldon H. Jacobson & Edward C. Sewell, 2014. "Complexity and Approximation Results for the Balance Optimization Subset Selection Model for Causal Inference in Observational Studies," INFORMS Journal on Computing, INFORMS, vol. 26(3), pages 547-566, August.
    4. Alexis Diamond & Jasjeet S. Sekhon, 2013. "Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 932-945, July.
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