Despite the popularity of the MIT Sloan Sports Analytics Conference, there's still a major disconnect between the rigorous data mining being presented and the people who can only believe what they see with their own eyes. This past weekend in Boston saw some fantastic presentations from brilliant young minds, backed up by massive undertakings of data gathering and mathematics. It also saw its fair share of head-in-the-sand denialism from those who are paid a lot of money to sit there and not really listen.
The Hynes Convention Center (which symbolically sits within view of both MIT and Fenway Park) was literally divided into two halves. On one end was the massive ballrooms where the stars of sport — ex-coaches like Eric Magnini and Jeff Van Gundy, and current general managers like Toronto's Brian Burke and Cleveland's Mark Shapiro — pontificated, talking head-style, from comfortable lounge chairs on a raised dais. The conversations were candid and revealing, with the best panelists sharing anecdotes of life behind the scenes. The were many great insights, some were more valuable than others, but for the pure fly-on-the-wall sport fan moments there was plenty to enjoy.
Then at the far end of the hallway — past the empty media room and the "game room" where Houston Rockets GM (and Sloan grad) Daryl Morey was busy shooting hoops on a pop-a-shot machine; past the drinking fountains; the bathrooms; the stage for the ESPN TV show, and behind the escalators — were the two smallest rooms that hosted the research presentations and "Evolution of Sports" talks where serious minds presented fresh ideas that may some day change the game.
In perhaps the most charitable reading of the disconnect between the two halves of the Sloan Conference, you might say that the smaller rooms were where the ideas come from and hard thinking about sports is being done. The bigger rooms are where those ideas are supposed to be put into practice: where forward-thinking owners like Dallas's Mark Cuban use analytics to build championship teams.
But in reality this past weekend, the crowds on the two sides of the Hynes were separate with neither one terribly interested in what the other had to say. Bill James, the grandfather of sabermetrics dropped by a talk about pitch prediction, and that may have been Dallas Cowboys head coach Jason Garrett sneaking in late to a discussion of the Wonderlic personality test.
To be fair, analytics were not entirely absent from the ballroom. Cuban and Morey are the most prominent big leaguers to incorporate high-level stats into their day-to-day decision making. The guys on the gambling panel clearly believe in the power of numbers, since they're the only ones who have figured out how it can make them rich. Same goes for the ticket sellers, who had no fewer than three panels dedicated to how to put more butts in the seats at the right prices. Soccer seems to be the one sport most open to new ideas, though they are playing catch up; essentially starting from scratch in at attempt to figure out how the "beautiful game" goes from Lionel Messi running through empty space to a data point that can help a coach win.
One of the solutions for that problem was also the basis for the most interesting talk we saw this weekend — and the paper that won the Research Competition — is optical tracking data that uses cameras to follow and record the real-time movements of every player on a field or court, as well as the three dimensional movements of the ball they might be using. It even measures the height of a basketball as it bounces off the rim and down to the floor. A team of computer science students at USC broke down years worth of NBA tracking data to discover the "non-linear relationship between shot location and its impact on offensive rebound rates." In other words, based on where you're standing and where the shot was taken from, you might be able to predict with some degree of accuracy where you need to go on the floor to get the rebound. The technology is in its infancy, but it's easy to see how all stat gathering and analysis will someday feed off these hyperspeed visualizations.
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.)
This article is from the archive of our partner The Wire.
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