In his life as a self-made billionaire at a private-equity firm, Tom Gores has proven that he is a man who values information and analysis. As a basketball owner he is proving the same thing. The Detroit Pistons have hired noted NBA statistics pioneer Ken Catanella as Director of Basketball Operations, the team announced Monday.
In his role with the Pistons, Catanella will serve as the team’s salary cap specialist and direct Detroit’s analytics efforts, the release states.
Catanella has spent the last few years working in the league office, and prior to that he worked for head coach Lawrence Frank in New Jersey from 2006-08 where he “managed the Nets’ analytics and spearheaded the creation and implementation of their statistical scouting systems.”
A graduate of Amherst College where he played collegiately, Catanella has worked on Wall Street providing analytics on stadium/arena financings for professional teams and valuing publicly traded companies. While earning his MBA at Duke University, Catanella served as a graduate assistant to the Blue Devils Men’s Basketball team from 2004-05. Catanella has also played professionally for the Bundesliga’s Cologne 99ers of the German League and later served as the assistant general manager of the 99ers.
But that short biography doesn’t really do him justice. I’m sure Pistons diehards like Dan Feldman at Piston Powered or Ben Gulker at Pistons by the Numbers and Detroit Bad Boys can explain the significance of this move far better than I could, but one thing that it makes clear is that Detroit is going to take advanced statistics seriously. Statistical analysis will be a driving force as Gores, Dumars and company look to remake this team.
And while this isn’t exactly the stone-age of dismissing statisticians as nerds playing simulated games on the computers in their mothers’ basements anymore, it is still rare for a team to so fully embrace the still-developing NBA advanced metric movement. If this were a basketball version of Moneyball, Catanella’s hiring would be analogous to hiring Paul DePodesta or J.P. Riccardi. In that analogy, I’m not sure Joe Dumars is the Billy Beane character, the driving force behind embracing statistics. But I sure hope he’s much more of a Beane character and a lot less of an Art Howe type.
When I was trying to get more information on Catanella’s work and background I stumbled on this truly excellent article on TrueHoop by Henry Abbott. It is an interview with Catanella prior to the 2008 NBA Draft when he worked for the Nets. The 2008 draft is, of course, the infamous “Sleepy” draft, when the Pistons stunned everybody by picking Walter Sharpe of UAB, a player who wasn’t even in the media guide.
Although Sharpe battled narcolepsy, Dumars rolled the dice on the premise, thinking his medical issues were a thing of the past. We all know how that worked out. As for Catanella’s New Jersey Nets? They had three picks in the draft, 10, 21 and 40 where they picked up Brook Lopez, Ryan Anderson and Chris Douglas-Roberts, respectively. Not a bad haul at all, I would say. In fact, you could argue that save for a few projects centers that the jury remains out on, the Nets picked the absolute best player available in ever spot.
While the interview is a must read for any Pistons fan, I’ll excerpt a few choice sections after the jump.
Here Catanella demystifies his stats work into the absolute essentials: trying to understand who can contribute to the future success of his team.
So, I know a lot of teams wrestle with how to integrate new-breed analytical work into their decision making process. Can you give me glimpse of how that works for the Nets?
Taking a step back — my background is in valuing companies for investment banks or mutual fund companies. It’s making a projection based on past performance, trying to answer the question “what here really indicates likely future success?”
Figuring that out really starts in conversations with the coaching staff, and trying to get a sense of what kinds of stats could be important. I did a lot of that when I was on Coach K’s staff, and I’m lucky enough to work with Lawrence Frank.
Then, based on those insights, we do a bit of regression, which helps to project how different players’ careers might play out in the NBA.
What goes into the mix is not any one magic number, though.
There are typical efficiency stats. There are anthropometric ratings like wing span, body fat, vertical leap, standing reach and the like. There are strength of schedule ratings. There is looking at who they were on the court against, and who they were on the court with. There are +/- numbers. The numbers really run the whole gamut. You try to find anything that you can measure that might be helpful, and put it all in the mix.
And here he talks about the marriage between stats analysis and traditional scouting:
Sometimes some statistical assessments will recommend players that are very different than traditional scouts. For instance, John Hollinger’s new projections don’t rank Derrick Rose so highly as a point guard prospect. How do you handle that?
I read everything. I read John Hollinger’s stuff all the time. I got a chance to talk to him in Boston at that MIT conference, and learned that we use a lot of the things he uses.
Those kinds of things can be indicators, negative or positive.
We never look at any of these things and make a final decision. The traditional scouts have another take, which is often more valuable.
I try to relate what I see in the numbers to what I have learned from my basketball background. So when the numbers show something, it’s normal to ask: Is this really important? Will this help us win a playoff game? We’re always trying to figure out, how will this thing we have discovered affect our team when we run this play.
That specific kind of thinking is invaluable.
When you’re just going through numbers, you’re left with a lot to learn. So when the numbers turn something up, before taking it to the scouts we’ll go to the film, and see what’s happening. After you watch whatever it is playing out over ten games, you can get an idea — do these numbers make sense? Are they relevant?
Sometimes you find there is not a whole lot there.
And sometimes you’ll find that there’s a player who seems to be really effective, but is not on anybody’s draft board, or is very low on people’s draft board. There were a couple of guys like that last draft, and now I think other teams are starting to notice, too.