Last year I went into significant depth in developing a method for optimizing March Madness brackets. I'm now focused on other projects, but since the code is easily reused, I decided to run it again this year. Aside from some under-the-hood improvements, I used the same method, incorporating one tweak from my baseball prediction model: accounting for home field advantage. This lumps together the effect of players, referees, and fans collectively tilting the odds slightly in favor of the home team. In the 2018-2019 NCAA season, the win rate for home teams was 51.22%, so the home field confers a 1.22% win probability advantage to the home team. While minor, this does slightly affect bracket outcomes this year as deep as the Final Four. This also demonstrates how famously unpredictable this tournament is, as a small tweak can ripple outward with significant effects. For the sake of expedience, I skipped bracket polling and used the "centrist" parameter set from last year.
Here's a view of the regular season:
And a view of the postseason:
Like last year, I built my bracket in reverse, selecting the winner of each game as the team that has the highest probability of eventually winning the championship:
Round of 32 Round of 16 Round of 8 ...of 4 ...of 2 ...of 1
Duke Duke
VCU Duke
Liberty Liberty
Virginia Tech Duke
Belmont LSU
LSU Michigan State
Minnesota Michigan State
Michigan State Duke
Gonzaga Gonzaga
Syracuse Gonzaga
Murray State Florida State
Florida State Gonzaga
Buffalo Buffalo
Texas Tech Buffalo
Nevada Michigan
Michigan Duke
Virginia Virginia
Oklahoma Virginia
Wisconsin UC Irvine
UC Irvine Virginia
Villanova Villanova
Old Dominion Tennessee
Cincinnati Tennessee
Tennessee Houston
UNC UNC
Utah State UNC
New Mexico State New Mexico State
Kansas Houston
Iowa State Houston
Houston Houston
Wofford Kentucky
Kentucky
Source code is stored on my GitHub.
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