Identifying NCAA tournament upsets using Balance Optimization Subset Selection

Dutta Shouvik, Jacobson Sheldon H. () and Sauppe Jason J.
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Dutta Shouvik: University of Illinois – Computer Science, Urbana, IL, USA
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

Journal of Quantitative Analysis in Sports, 2017, vol. 13, issue 2, 79-93

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.

Keywords: basketball; optimization; predictive modeling (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1515/jqas-2016-0062

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