The scoreboard for our Machine Learning lower seed winning project:
10 wins out of 20 (50%)
$605 profit on $2,000 worth of simulated bets (30% ROI)
Our Machine Learning lower seed winning project was looking to predict as accurately as we could a lower seeded team winning in the NCAA tournament . Or goals were to get 47% right on the picks and a mere 10% hope for ROI. We beat both of those goals. We practically doubled the 26% baseline of the historic lower seed winning rate and more than doubled the lower seed winning percent in this tournament (21%). We never expected to predict 100% of the upsets, however, 10 out of the 13 lower seed wins were predicted by us.
Our other goal was to show with a simple demonstration how Machine Learning can drive results for almost any business problem. With that in mind, let’s recap what we did and how we did it.
Our Machine Learning Algorithm, which we call Evolutionary Analysis, looked at a comparison of 207 different measures of college basketball teams and their results in prior tournaments. It selected ranges of those 207 measures that best matched up with historic wins by lower seeded teams. We then confirmed that the range was predictive by testing the selected ranges against a “clean” historic data set. This comparison is how we got our goal percent and ROI.
Then we did what any good business person does, we acted. We published our forecasts before each round was played and our results above speak for themselves.
Machine Learning is the power to find patterns in data where previous analysis has found none. Our methodology assured that what we were seeing was predictive. No doubt luck is involved (Wisconsin vs. Florida or many of the other times it came down to the final minute). The overall success, however, speaks for itself.
That is the formula for success on any Machine Learning project: A data set with a large number of characteristics, a measure of success, the expertise to execute an effective project and the courage to succeed. If this sounds like something that your business could use, please contact Gordon Summers of Cabri Group (Gordon.Summers@CabriGroup.com) or Nate Watson of CAN (Nate@CanWorkSmart.com) today.
And for those who are curious. The algorithm indicates that Oregon vs. North Carolina matches the criteria for an upset.
Here are our collected picks (dollar sign indicates correct pick):
East Tennessee St. over Florida
$ Xavier over Maryland
Vermont over Purdue
Florida Gulf Coast over Florida St.
Nevada over Iowa St.
$ Rhode Island over Creighton
$ Wichita St. over Dayton
$ USC over SMU
$ Wisconsin over Villanova
$ Xavier over Florida St.
Rhode Island over Oregon
Middle Tennessee over Butler
Wichita St. over Kentucky
Wisconsin over Florida
$ South Carolina over Baylor
$ Xavier over Arizona
Purdue over Kansas
Butler over North Carolina
$ South Carolina over Florida
$ Oregon over Kansas