Machine Learning and the NCAA Men’s Basketball Tournament

Machine Learning (ML) is a powerful technology and many companies rightly guess that they need to begin to leverage ML. Because there are so few successful ML people and projects to learn from, there is a gap between desire and direction. Cabri Group and CAN have teamed up to help. By demonstrating results, we believe more people can give direction to their ML projects. Therefore, we proudly present our ML NCAA lower seed predictions.

Those interested in a fuller description of our analysis methodology can read our accompanying article.

We will be publishing a selection of games in the 2017 NCAA Men’s Basketball Tournament. Our prediction tool estimates games where the lower seed has a better than average chance of winning against the higher seed. We will predict about 16 games from various rounds in the tournament. The historical baseline for lower seeds winning is 26%. Our target will be to get 47% right. Our target is based on the results we would have achieved using our prediction tool for the 2016 tournament. The simulated gambling ROI was 10%.

This analysis isn’t to support gambling, but we will be keeping score with virtual dollars as if we were. We will be “betting” on the lower seed to win. We aren’t taking into consideration the odds in our decisions, only using them to help score our results.

We will be publishing our first games on Wednesday 15th after the first four games are played. We won’t have any selections for the first four games as they are played by teams with identical seeds. Prior to each round, we will publish all games that our tool thinks have the best chance of the lower seed winning. We’ll also publish weekly re-caps with comments on how well our predictions are doing.

The technique that finds a group of winners (or losers) in NCAA data and can be used on any metric. We hope this demonstration opens up people’s minds onto the possibilities to leverage Machine Learning for their businesses. If you would like more on this type of analysis please contact Gordon Summers of Cabri Group (Gordon.Summers@CabriGroup.com) or Nate Watson at CAN (nate@canworksmart.com).
Disclaimer: Any handicapping sports odds information contained herein is for entertainment purposes only. Neither CAN nor Cabri Group condone using this information to contravene any law or statute; it’s up to you to determine whether gambling is legal in your jurisdiction. This information is not associated with nor is it endorsed by any professional or collegiate league, association or team. Machine Learning can be done by anyone, but is done best with professional guidance.