By: Edward Egros

inseason

A New NCAA Tournament

UNADJUSTEDNONRAW_thumb_10d3
There's no doubting the increased awareness of analytics in predicting the NCAA tournament field in college basketball. Instead of just diagnosing a team's record against the Top 50, it's Rating Percentage Index or Ken Pomeroy rankings, that are becoming more commonplace. It has gotten to where data scientists are actually meeting with the NCAA to determine if one metric should be used above all others to pick tournament teams.

Perhaps surprisingly, data scientists want simpler criteria for picking teams: who wins, who loses and who have you played. This is opposed to other explanatory variables used in more advanced metrics, like margin of victory and offensive/defensive efficiency. Coaches, on the other hand, would prefer more complex formulae for determining the tournament field. Logically, this approach makes more sense from their perspective, because of competition. If a coach has figured out a style of play or way to schedule opponents that increases the likelihood of making the tournament, they develop a competitive advantage. Data scientists want to keep it simple for fans, coaches want a figure out a competitive advantage.

Perhaps in this same spirit of transparency, the tournament selection committee released "in-season" projections for the first time ever, one month before Selection Sunday. It only has the top four seeds of every region, but it is added information for where highly ranked teams really sit. As with any analytic project, more data "usually" means more robust forecasts. Already, it is easier to make more accurate assumptions and offer a better glimpse as to what the committee is looking for.

However, these in-season projections do not include the full field of 68, and what usually causes the most consternation is simply who does and does not make the dance. While it makes sense not to include the full field because you have to assume certain conference champions in mid-major conferences, something that would include all "at large" teams would provide even more information as to the criteria for inclusion.

Nothing is easy about picking 68 teams to play in a tournament, and while analytics may be helpful in forecasting a Final Four, easy-to-understand criteria can help teams and fans quell any controversy.