The Mainstreaming of Analytics

John Hollinger, a long-time ESPN.com columnist and inventor of the Player Efficiency Rating (PER) for evaluating basketball players, is joining the Memphis Grizzlies front office as its Vice President of Basketball Operations.

This is wacky on a number of levels. First, it represents the ongoing rise of the numbers geek in sports, a movement pioneered by Bill James almost 40 years ago, given an identity a decade ago in Michael Lewis’s book, Moneyball, and gaining official acceptance in the NBA five years ago, when the Houston Rockets named Daryl Morey its General Manager. Want to run a professional sports team? These days, an MIT degree seems to give you a better chance than spending years in the business.

Second, Hollinger spent over a decade sharing his thinking and his tools for all to see. Now, all his competition needs to do to understand his thinking is to Google him. Tom Ziller writes:

The major difference between Hollinger and, say, Morey is that we all know Hollinger’s theories. We know his positions, and we’ve learned from his work…. Will his canon hurt his ability to make moves? We can lay out exactly which players he likes based on his public formulas and his writings. Other GMs will know which Memphis players he’ll sell low on. You can anticipate his draft choices if you’re picking behind him. If you’ve got a high-production, low-minutes undersized power forward, you know you can goose the price on him because history indicates that Hollinger values him quite seriously.

This is all a gross simplification: Hollinger’s oeuvre is filled with nuance. He doesn’t rank players solely by PER, and in fact he probably has some adjustments to his myriad metrics up his sleeve. He’s not going to be nearly as predictable as a decision-maker as anyone would be as a writer. The stakes are different, the realities of action are different. But no decision-maker in the NBA has had this much of their brain exposed to the world. Morey isn’t shy, but that big Michael Lewis spread on Shane Battier was as far as we ever got into the GM’s gears. Zarren is notoriously careful about what he says. He might be the only GM or assistant GM in the league more secretive than Petrie.

It’s interesting to consider the implications on the Big Data movement in business (on which Moneyball had a much greater influence than most would probably admit). Business is not a zero sum game like professional sports, so there’s more room for nuance and many positive examples of openness and transparency. Still, for all those who believe that openness and competition do not have to be at odds with each other, this will be fascinating to watch.

Ziller also makes a wonderful point about the importance of communicating meaning from analysis:

In the end, what Hollinger’s hire means is that the ability to do the hard analysis is important, but so is translating that to a language the people on the court can understand. That’s always been a wonderful Hollinger strength: making quant analysis accessible without dumbing it down. Even someone as brilliant as Morey, who has a team of quants, can’t always achieve that.

I’m reminded of a tale from Rick Adelman’s days in Houston. Morey’s team would deliver lengthy scouting reports to the team and coaching staff well before a game. It’d have player tendencies, shooting charts, instructions on match-up advantages — everything you could ask for to prep for a game. And out of all of the coaches and all of the players only two — Shane Battier and Chuck Hayes — would devour the reports. The rest (Adelman included) would leaf through, pretend to care and go play ball. That story might be an exaggeration on the part of the person who told it, but even if that’s the case, it shows how important accessibility is. You can build the world’s greatest performance model. And if you can’t explain what it means to the people using it, it’s worthless.

Moneyball and High-Performance Collaboration

On Friday night, the movie, Moneyball, opens in theaters nation-wide. It’s based on the book by Michael Lewis, which I reviewed on this blog back in 2003. I was pretty shocked that they turned a book about how data is changing baseball into a movie starring Brad Pitt. I’m even more shocked to hear that the movie is pretty good, even by sports fan standards. Regardless, it’s a great excuse to revisit the ideas in this most excellent book and to explore the implications on high-performance collaboration.

In a nutshell, Moneyball is the story of Billy Beane, the general manager of the Oakland A’s, who used (what was then considered) radical new ways of measuring performance in order to stay competitive in a market where other teams (e.g. the Yankees) were spending orders of magnitude more money on talent. It documents the huge, ongoing culture shift in baseball away from old-school, hard-scrabble views on player evaluation to a more data-driven system.

In my book review, I wrote:

How do we measure the effectiveness of collaboration? If we can’t measure this accurately, then how do we know if we’re getting better or worse at it? Baseball has the advantage of having well-defined rules and objectives. The same does not hold with most other areas, including collaboration. Is it even possible to measure anything in these areas in a meaningful way?

I think we’ve made progress in exploring this question. There’s a world-wide trend toward leveraging the tremendous amount of data now available to us in order to try and understand, in real-time, how we behave and why. This is a good thing, and we need to see a lot more of this.

At the same time, we also need to be careful about a potentially false sense of confidence about what all this data actually means. I love what Joe Posnanski wrote about Bill James, the father of sabermetrics:

If there is a guiding principle to all of Bill’s work, it is this: What difference does it make? The world is a complicated place. Baseball is a complicated game. This, more than anything, is what the Bill-as-cartoon people miss. He does not think that there are RIGHT answers and WRONG answers, certainly not to the questions that rage in his head. He just thinks that there are ways to get closer to the truth.

“We will never figure out baseball,” he says. “We will never get close to figuring out baseball.”

This, I think, is the critical final piece. Curiosity might have been the flint, distrust of conventional wisdom might have been the steel, but that only gives you a spark. What turned the work into a raging fire was that Bill James has never really believed that he had figured it out. He never even believed that you COULD figure it out. All he wanted to do was get the conversation going, advance the ball, give people new things to think about, let the discussion evolve and keep evolving.

Replace “baseball” with “life,” and you have a philosophy worth living by.

Picture by pursuethepassion. Licensed: CC-BY-NC-SA 2.0