I've been a little slow in getting around to this, but I want to congratulate "SDSU Fan" on winning the 2016 Machine Madness contest! In real life, SDSU Fan is Peter Calhoun, a graduate student in Statistics at (no surprise) San Diego State University. We had a very large pool of entrants this year (40!) so Peter deserves some congratulations for beating the masses. Peter was trailing by a significant amount after the Round of 32, but strong performances in the later rounds (and especially the FF) resulted in big lead by the end.
Peter's model modified the Logistic Regression/Markov Chain (LRMC) approach proposed by Kvam and Sokol to use random forests. Peter also finished in fiftieth on Kaggle -- a very strong performance all around.
Despite the large number of entries, nobody had Villanova winning it all. I think that makes the Villanova win a "true upset". I know in my model, Villanova played considerably better than predicted.
Speaking of my model, it follows a strategy in pool-based contests of picking some "likely" upsets to try to maximize the chance of winning. (This is probably more important in a larger pool.) This year, it picked Purdue to make it to the Championship Game. Not only didn't that happen, Purdue was upset in the first round by #12 Little Rock. I'm adding a special "Purdue Rule" to the Net Prophet model so that mistake is never again repeated. :-)
Congratulations again to Peter on great performance!
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