“It’s time to clam up”.
Moneyball. It's the book that launched a thousand nerds, many of them gathering in Boston last weekend for the tenth Sloan Sports Analytics Conference. But in another world that Michael Lewis book is a magazine article, sitting in a doctor's waiting room like so many other dog-eared periodicals thumbed through in idle distraction.
Billed as the "Decade of Data", a Moneyball reunion saw Michael Lewis, Bill James and Paul DePodesta – Moneyball's author, baseball analytics legend and number-cruncher – meet as a group for the first time.
Lewis wound back the years to speak about how Moneyball almost never was. He'd started work on this as a magazine piece looking at money on the field, Billy Beane's Oakland A's side in particular.
“I noticed that the left fielder was being paid four million bucks and the right fielder was being paid about 150 grand. And salaries were exploding. And I wondered how pissed off the right fielder was when the left fielder dropped a fly ball. I thought there’d be a story about class warfare inside a baseball team.”
The 2002 MLB draft was a key turning point.
, chief protagonist in Lewis’s tome and whose late pullout from the panel made his constant mention in anecdotes feel like a very public wake, had allowed the writer to sit quietly in the back.
“In my 20 years in baseball it was the most emotionally charged draft room I’ve ever been a part of” said DePodesta, then an assistant to Beane.
Afterwards he was in a back room getting some food and asked Lewis what he thought.
“You looked at me right away” said DePodesta, “and you said ‘This is too rich. This isn’t an article, this is a book.’ I literally turned around, went straight to Billy and I said ‘It’s time to clam up, he’s writing a book.’”
The sports statistical landscape has moved on since 2002. With player tracking data set up for MLB, NBA and the NFL attention is turning to something already commonplace in Europe and Australia: wearables and biometric sensors. Data that tells a story about the athletes’ bodies, generated by sensors that NBA and NFL teams are not yet allowed wear in games.
On the sports science panel Stephen Smith of Kitman Labs, an athlete health management outfit with its roots in Irish rugby, said that a big challenge for sports scientists was a misinterpretation of how this kind of data will be used. Some coaches fear their athletes will be shut down whereas it should be about changing things so the athletes can do even more.
Amid the almost 4,000 attendees (1000 students) one can find team analysts and investors, scientists and gamblers. Front office men from pro sides. Get the right person at the right time and you can learn a phenomenal amount, but only up to a point.
"Rufus, there's no chance you're answering this question." said Jeff Ma, part of the MIT blackjack team made famous by Ben Mezrich's book Bringing Down The House.
Rufus Peabody smiled. The audience laughed. He had been asked what sports were currently opportunities, their betting markets inefficient. Peabody, as a Las Vegas sports gambler, would be loathe to reveal his edge. The two are friends; it was taken in jest. But in truth Ma could have said that to many panellists at Sloan.
Too often the learning is far away from the big stages and bright lights. It’s the small rooms, packed for Dan Altman’s analysis of what makes Leicester City’s attack tick (when Leicester pass the ball more often they are less likely to score) and how teams have started to slow it down (hassle
and N’Golo Kante, funnelling them wide).
It’s about listening to the man in charge of putting manners on the NBA’s brutal travel schedule, or lawyers lecturing on the protection of sports data.
Sloan is the big kahuna of sportsnerd conferences. It will always get the students and the vendors showcasing their wares, the businessmen pretending on their tax return that this is a work-related event and not a jolly featuring some talking heads they see on TV.
But is it getting repeat business from analysts and managers, from those really in the field? Sloan needs to be careful. This analytics behemoth perhaps needs reshaping; a sharpening of its statistical claws. It’s time to clam up?