These presentations featured hard science at work. Number crunching, lots of it. And not to prove pre-existing notions, but simply to listen to the data and see what they tell us. (More than a few complaints were heard about the "data" vs. "datum" mis-agreement.) Yet, time again ... when a reasoned and ably researched idea was presented, we heard some variation from those in the crowd of "That's interesting, but..." They're the well-worn points of sports shows and bar arguments:

"You say there's no such thing as a 'clutch' shooter, but I know what Kobe Bryant is capable of in the close seconds."

"You say cornerbacks are more valuable than linebackers, but I know I'd rather pay Brian Urlacher more than Darrelle Revis."

"You say certain drugs don't change performance, but I know what cheating is."

Another project designed a "similarity network" to group NBA players by their characteristics, defining 13 different categories of players, as opposed to the traditional 2 guard, 2 forward, 1 center framework. (You can see a version of the presentation here.) Yet, seconds after it ended we heard two guys loudly disagreeing with the presenter's classification of Minnesota's Kevin Love, based on ... what exactly? Muthu Alagappan has charts and data points and a Biomechanical Engineering degree from Stanford University. You have NBA TV on your cable package. Who would you believe?

People love evidence ... when it tells them what they want to hear. Once they hear something that doesn't intuitively make sense to them, they fight back. And what they don't want to hear is that the notion they've come to believe, that theory developed after years of watching SportsCenter from the couch, isn't much of a theory at all. In fact, it may be the exact opposite of the truth. That's not what many attendees paid for.

This stuff is the new Moneyball, a book that had its cover image plastered around the convention center as one of the pillars of the sports analytics movement. Yet some audience members audibly scoffed when Alagappan posited (via a big spreadsheet with lots of decimal points) that Devin Ebanks might be just as valuable to the Lakers as Carmelo Anthony is to the Knicks. As if the idea that a cheap journeyman might be able to provide skills similar to that of a overpaid superstar had not been the premise of a bestselling book and Oscar-nominated movie that gave sports analytics its cultural relevance. Maybe Alagappan's numbers are wrong, but you better bring your own numbers.

Perhaps no one at the conference better demonstrated this conflict than Brian Burke. The General Manager of the Toronto Maple Leafs is a funny and engaging guest who is not afraid to speak his mind, and was routinely cited by many attendees as their favorite panelist. Yet, Burke made it clear from the very beginning of the hockey analtyics session that he's going to trust his own eyes and ears and the instincts honed over a lifetime around the game of hockey before he's going to trust some kid in the back cubicle with a spreadsheet. Burke, as successful and charming as he is, stands for everything that the nerds have been fighting against for a generation — the faith of experience over the proof of evidence. Sauntering into a sports analytics conference and declaring that Moneyball is "horseshit", should be akin to telling a revival meeting that the Gospels could use an editor. Instead of being booed off the stage by the self-proclaimed "geeks" of the revolution, they howled in disappointment when he didn't show up for his last panel. They will howl even louder if he were somehow not invited back next year. (He will be.)

The future is still happening at the MIT Sloan Sports Analytics Conference and because of this weekend (and future weekends like it) there may someday be more Daryl Moreys in sports than Brian Burkes. If only someone could invent a population algorithm to tell us when that will be.

This article is from the archive of our partner The Wire.