The rise of sports analytics brings new meaning to being a 'quant jock'

From: Blog

The eighth annual MIT Sloan Sports Analytics Conference took place in Boston over the weekend, bringing together researchers, statisticians, and analysts on the one hand and the sports establishment, including head coaches, team owners, commissioners, and athletes, on the other. A meeting of the minds, as it were, of the jocks and the geeks.

It’s not the oddball pairing it used to be. The preponderance of data in sports makes it a natural place to take the numbers game. Heck, sabermetrics pioneer Bill James was self-publishing his Baseball Abstract all the way back in the 1970s,when big-data was only a glint in the milkman’s eye.

But all the data in the world don’t matter if the metrics don’t point to a win. And that seems to be what the sports world wants the number crunchers to be clear on. The analyses need to be accessible and digestible. And they need to apply, meaning they need to translate to the scoreboard.

The NHL’s plus-minus value for measuring individual player contribution to the game is a good example of numbers that don’t work—in part because hockey is one sport that doesn’t easily lend itself to data-driven decision making. If baseball is the darling of analytical possibilities, hockey is the stepchild left out in the cold.

That is, until recently. Researchers are determined to crack into hockey data, including Chicago Booth’s Robert B. Gramacy and Matt Taddy, who have set out to find a measure of individual player contribution that does work. Their alternate method revises current notions about who the most valuable players in the NHL are. Gramacy and Taddy maintain a weekly analytics report using their method on their blog, Chicago Hockey Analytics. You can also read more of our coverage about their research here.

Those who dislike data mining in sports, the stubborn few remaining, continue to think it strips the game of its artfulness, of the nuance and intuition of players and coaches. Plus, for a lot of people, it’s just too hard to understand.

Douglas Alden Warshaw in Fortune has his own misgivings about the trend. He fears our obsession with data is subsuming our human inclination to make sense of the world through story. The qualitative losing out to the quantitative in our big-data age.

Warshaw does a good job of capturing the battle in his article about statistician and UChicago alum Nate Silver’s move from the New York Times to ESPN. Warsaw makes the case that Silver might just be able to save sports analytics from itself, by being the numbers guy who brings the stories back into the game.

No question the data can be overwhelming. But they’re worth considering, especially when the numbers could help teams avoid the kinds of blunders that lose games, including, as Silver has called it, “the most statistically unsound tactic in professional sports.”

In the spirit of helping bridge the language gap between quant heads and the sports world, the Capital Ideas team has packed the Spring 2014 issue of Capital Ideas with sports research from Chicago Booth professors and other experts, on everything from referee bias, to going for it on fourth down, to what kinds of teams win championships—all with the idea of putting the numbers to work in building and managing a successful sports team.

Find out what we add to the conversation. After all, we’ve wrestled with our fair share of complicated equations, and we also know a good story when we hear one. 

—Molly Heim 

 

Cat:Business, More,Sub:Statistics,