The annual NCAA basketball tournament - now at its height - has long been a magnet of sporting interest for people from all walks of life. Tapping this vein of awareness, a couple of professors have been working in recent years to channel the excitement of March Madness as a tableau for educating students and businesspeople about the way analytic models really work and their power to predict.

Both professors Jay Coleman of the University of North Florida and Allen Lynch of Mercer University had been using SAS-driven analytic engines since their college mainframe computing days. Both happened to be sports fans. Much later on, Coleman, an associate dean, and Lynch, who teaches economics and quantitative methods, got together to collaborate on the Dance Card, a weighted mathematical formula that seeks to predict the tournament choices made by the selection committee. Coleman started the project after coming across a stat-driven site,, run by a fellow named Jerry Palm. "He had a fairly extensive data set covering 1994 to 1999 that showed all the data on teams that got bids to the tournament and a lot of the teams that didn't but were possibilities," Coleman says. "I took one look at that as an academic, to whom a data set is a gold mine, and said we should do something with that." With Palm's cooperation, Coleman wrote code to collate data for subsequent years and, along with Lynch, created a model to predict at-large bids (and later, individual game outcomes). This year, the model correctly predicted 31 of 34 or 91 percent of at-large bids to the tournament, based on a set of variables and filters that can be found at That is as poorly as the model has done; in its best years, it has missed only one pick.

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