Creating a March Madness bracket with Machine Learning

March 18, 2016

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

March Madness is upon us here in the US. This annual college basketball competition pits 64 teams in a single-elimination tournament, and the team that goes undefeated for all 6 rounds will be named NCAA Champion.

Predicting the winners of the competition, and in particular completing a "bracket" of the teams you predict to make it to the final 32 or 16 and eventually win, is a popular pastime (and foundation for many wagers). Some use their knowledge of the teams or the betting markets to select their bracket. And some, like 47-year-old English data scientist and top-ranked Kaggler Amanda Schierz, use data, models, and R. Watch her story in this (sadly, unembeddable) video from ESPN and FiveThirtyEight.


If you'd like to try your hand at your own predictions based on machine learning, Azure ML (part of the Cortana Analytics suite) provides all the data, algorithms, and R and Python support you need. Here at Microsoft we've run internal March Madness competitions every year, and in the video below last year's winner Damon Hachmeister shares his secrets.


There's even a March Madness Prediction Service published on the Cortana Analytics Gallery which, given the current state of the competition as a Web Service input, will provide predictions for the remaining games as an output.

Prediction service

 Got more tips for predicting a bracket with Machine Learning? Share them in the comments below.

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