Seed Distributions for March Madness 2018: A Tool for Bracketologists

Round of 64 Upsets

In 28 of the past 33 tournaments (1985 through 2017), one or more teams seeded 13, 14 or 15 have won a game in the Round of 64. Our research on causal inference using observational data ([1],[2]) has been adapted to look at key statistics that may predict upsets for these seeds. From 2003 through 2017, we used the Balance Optimization Subset Selection model (BOSS) to select two teams each year. Of the resulting 30 teams selected (out of a possible 180 teams that could have been selected), 10 ended up being upsets. During this time period, there were a total of 23 upsets. Below is a table, constructed using the BOSS model, which contains the two teams selected each years (from 2003 to 2017) that share the characteristics of previous upsets. We also report the actual upsets that occurred during this time period.

Year Upsets Chosen by BOSS Actual Upsets
2003 (13)Tulsa, (14)Troy (13)Tulsa
2004 (13)UI-Chicago, (13)Texas - El Paso None
2005 (13)Vermont, (14)Niagara (13)Vermont, (14)Bucknell
2006 (13)Bradley, (14)Xavier (13)Bradley, (14)Northwestern State
2007 (13)Albany, (13)Holy Cross None
2008 (13)Sienna, (13)Oral Roberts (13)Siena, (13)San Diego
2009 (13)Cleveland State, (13)Portland State (13)Cleveland State
2010 (14)Ohio, (15)Morgan State (13)Murray State, (14)Ohio
2011 (15)Akron, (15)LIU-Brooklyn (13)Morehead State
2012 (14)Belmont, (15)Lehigh (13)Ohio, (15)Lehigh, (15)Norfolk State
2013 (13)Lasalle, (14)Davidson (13)Lasalle, (14)Harvard, (15)FGCU
2014 (13)New Mexico State, (13)Delaware (14)Mercer
2015 (14)Georgia State, (14)UAB (14)Georgia State, (14)UAB
2016 (13)Iona, (14)Buffalo (13)Hawaii, (14)Stephen F. Austin, (15)Middle Tennesse State
2017 (13)Vermont, (14)New Mexico State None

The two upsets for the 2018 tournaments will be posted shortly after Selection Sunday in 2018.

References:

[1] Nikolaev, A.G., Jacobson, S.H., Cho, W.K.T., Sauppe, J.J., Sewell, E.C., 2013 “Balance Optimization Subset Selection (BOSS): An Alternative Approach for Causal Inference with Observational Data,” Operations Research, 61(2), 398-412.

[2] Sauppe, J. J., S. H. Jacobson, and E. C. Sewell, 2014, “Complexity and Approximation Results for the Balance Optimization Subset Selection Model for Causal Inference in Observational Studies,” INFORMS Journal on Computing, 26(3), 547–566.

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