Who’s going to the Big Dance?

The NCAA Men’s Basketball Tournament tips off this weekend with the announcement of the 65-team field. The tournament, affectionately referred to as “The Big Dance,” is a yearly highlight for college basketball fans and culminates in the crowning of the national champion after 64 games over three weeks of March Madness.

Two of these fans, who also happen to be business professors, have developed an analytical model using SAS® software to predict “at-large” teams – those schools who do not receive an automatic bid to the tournament.

Jay Coleman, an operations management professor at the University of North Florida in Jacksonville, and Allen Lynch, an economics professor at Mercer University in Macon, Georgia, built a model that has achieved an impressive 94% accuracy rate in predicting tournament teams.

The actual selections are made by the NCAA Tournament Selection Committee, and will be announced this weekend. Coleman and Lynch used historical results from this Committee, along with 42 pieces of information to build their model. Interestingly, they found that only 6 items are significant in determining whether a team gets an at-large bid:

1) RPI (Ratings Percentage Index) Rank
2) Conference RPI Rank
3) Number of wins against teams ranked from 1-25 in RPI
4) Difference in number of wins and losses in the conference
5) Difference in number of wins and losses against teams ranked 26-50 in RPI
6) Difference in number of wins and losses against teams ranked 51-100 in RPI

Here a link to their website; and a 2-minute video about their model.

As they mention in the video, predictive models have many applications in the business world. These models can be difficult to build (the DanceCard model has been refined over 14 years) and validate (we don’t have the equivalent of a 10-member committee announcing their results live on CBS). But simplifications may exist (of 42 drivers in the DanceCard model, only 6 are significant) so don’t be afraid of the complexity.

Advances in analytical software, coupled with the increased availability of data, make predictive models a powerful tool to use in optimizing your business. And we have the benefit of a real-life market to test our ability to predict the future.

Today’s burning hoops question: Will Ohio State, who lost to Florida in last year’s championship game, even make it into this year’s field with a 19-12 record and an RPI of 48? DanceCard will have their final prediction later this week.

What’s your burning business question? Have you tried to build a predictive model to answer this question? How well did you do?