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
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.