Oliver talks about the role his statistical analysis plays in the NBA.
by Kyle Stack / @KyleStack
When most baseball fans think of statistical analysis in their sport, Bill James is usually the person that comes to mind. Yet with statistical analysis growing in basketball, many fans still might not know who Dean Oliver is. You might know him as the author of the well-known book Basketball on Paper, which was released in 2003. He serves now as the Director of Quantitative Analysis for the Denver Nuggets, which is a position he’s held since 2006. Prior to that, he worked for six years as a statistical consultant to the then-Seattle SuperSonics.
Oliver’s path to the NBA isn’t like any you would imagine. Sure, he played college ball. But it was at Caltech, the academic home of NASA’s Jet Propulsion Laboratory. He served as an assistant coach there for three years after graduating, then worked for a basketball scouting service from 1990-94. Once he earned his Ph.D in using statistics to make forecasts from the University of North Carolina in ’94, he had laid down the path to eventually join the NBA.
I’ve wanted to speak with an expert in basketball quantitative analysis for some time. I felt like Oliver would be the ideal person to do that with, and I spoke with him recently about his role in statistical analysis, and how he uses his analysis to help him find the player he thinks the Nuggets needs. 
SLAM: What were your expectations when you started with the Nuggets?
Dean Oliver: I actually had two seasons with the Sonics prior to that. That set my expectations with the Nuggets, a little bit. Between those two years [I was with the Sonics and Nuggets] I had time to take what I had known and done with the book on paper and apply it to more live situations. What does a coach have to answer? What does a GM have to answer? I had that. Coming in to the Nuggets, it was an opportunity to work with a little bit different people, because everyone has a different style. Working with the new group I was definitely hoping for a little bit more say-so. That’s always what you’re looking for. I was able to do that.
SLAM: When you started, did you think that your value would be reporting to the GM and owner, or did you think it might be providing info to players and coaches?
DO: I never thought about providing information directly to players, except little bits and pieces here and there. I definitely felt like there was impact for the coaches. The coaches obviously know their own team very well, and I think you can only provide some information they don’t know. Especially midway through the season. They know their guys pretty well. But they don’t know some of their players, certainly ones who may be coming from other teams. It happens every year. There is some insight that can be gained. And then there is of course the other aspect of coaches dealing with opponents on a regular basis. Any information they can get on those guys, whether it comes from an advanced scout or from numbers can help. I felt like there was significant impact from a coaching side as well as the personnel side.
SLAM: Has the acceptance of quantitative analysis in the League changed much from 2006 to now?
DO: Yes. I would definitely say so. The immediate reflection of that is the rise of the MIT conference from the analytics. I think that was the first year it ran, if I recall correctly — 2006. It was a handful of us. I think all the people who attended were able to go to the Boston Celtics game. Now, they can’t fit people in. If I have to reflect the acceptance, I think some of it is what [the media] has done, just to publicize it a little bit.
SLAM: Is quantitative analysis beyond the stage of it being just a trend?
DO: I think it’s established. It’s not established like it is in Major League Baseball. To my understanding, MLB has a pretty thorough infiltration across the league. Within the NBA, you may have half the teams that dabble a little bit, but I think it’s probably only 7-to-10 teams that have it integrated into their decision-making process. There’s an upward trend. I wouldn’t say it’s fully established.
SLAM: What are your responsibilities?
DO: My responsibilities are to help out with pretty much every aspect that numbers can. I respond to requests to coaches, I respond to requests from Masai and the other members of our management. But I also put things out there to go in certain directions based on my experience of what I think helps. I put things together with the advance scouts. I send things to them and send things to members of the coaching staff. There’s a lot of tasks that are related to numbers. It’s a numbers game. There’s so much that goes on that’s recorded. The fact that you’ve got a lot of games and a lot of history of games over 30 or 40 years, you can make inferences based on a lot of history.
SLAM: Do you have people working under you?
DO: No. Denver’s always been pretty small.
SLAM: Wow, that’s a lot of work.
DO: Yeah, there are definitely times during the season when I feel like I’m trying to do too much. It’s one of those things…you get more efficient every year but you also find more things to do. Some of those things that historically may have taken more time can now be done faster, but that just means you find new things to take more time. There’s always a bit of a push-pull on that.
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Now this can be accomplished without the use of stats but the fact of the matter is this, if we were to replace all the decisions made in the draft as a result of “intuition” with the selections a computer model projects as the superior prospect, chances are your team will be better off. I dont know what gives you the impression that stat geeks are “loud” or that they represent a majority movement when the article you just read contradicts that very statement.
I understand Dean O doesnt represent every stat head out there but in my experience we are well aware of where we stand among the masses. All you have to do is visit truehoop to see how often they refer to themselves as outsiders or geeks. Still the movement is happening, you can either get with the program and optimize your decision making process with tons of useful information, or you could be like the Clippers and be left behind.
Yes, like I said from the start. It doesnt take a genius or any sort of intuition to know building around Kobe will lead to success. But to try and say there is only 1 successful franchise per year is nonsensical IMO. Only 1 franchise obtains the ultimate level of success sure, but there are various levels of success. I guess this comes down to how you want to evaluate success. —– Fans (people who buy tickets) are only going to accept the notion of, “Our team had a great season” for so long. Based on? People buy tickets for a variety of reasons, just look at the Clippers. Theyve been turning a profit for decades and it has nothing to do with their success so a blanket statement like this is meaningless. —– it can be argued that in the case of the Celtics, analytics had almost nothing to do with it.
Not really, Rondo was a stud by draft measurements —– As for the Cavs, they had LeBron James. The real test (and value) of their analytics department will come this season. Will they be like Morey’s Rockets and not have their ship sink totally with their marquee and franchise guy gone? That remains to be seen. And what about the Nets? Analytics didn’t help them. They won 12 games last season.
So let me get this straight, you can credit the success to players but all the blame gos on stats for them losing? Thats pretty poor system man. I based success on whats expected of the team and what they control. For example, if a team drafts a solid prospect late in the draft due to analytics then its made a successful decision ala Ty Lawson. I dont look at raw W-Ls because there are alot of factors that go into that. —– OKC’s analytics department seems to be doing a great job in constructing their team, but even with a great player like Kevin Durant, can they really win a title?
I dont know, nor do I care. All I know is that awhile back they were nowhere near where they are now. Thats a SUCCESSFUL TURNAROUND. One that actually only happened because Portland actually went against what the stats and models said to do and went with conventional wisdom of taking big over small when every metric out there said to take Durant. —— And the rest of the other teams are really just middle of the pack. They have great regular seasons, but are no real threat to win a title. Not even the Spurs anymore. I just think that analytics in basketball makes mediocrity acceptable and teams that integrate it into their decision making process will be just that and the teams that rely more on traditional talent evaluation of players and assess value that way will continue to outshine them.
I dont see a shred of proof to promote this opinion but ok —– The notion that a computer model can build a better team than good, old-fashioned basketball “intuition” is silly, IMO and I ask you, are these computer modeled teams being built to win something tangible — like an NBA Championship trophy — or just a good amount of regular season games?
They are meant to be an upgrade, whether you win a title or not is dependent on so many external factors that trying to pin them down to a single aspect is laughable, that you find it silly is again irrelevant when you look at the historical results of draft rating models this decade, it is a quantifiable fact that they have done a better job of landing impact talent.
Yes, like I said from the start. It doesnt take a genius or any sort of intuition to know building around Kobe will lead to success. But to try and say there is only 1 successful franchise per year is nonsensical IMO. Only 1 franchise obtains the ultimate level of success sure, but there are various levels of success. I guess this comes down to how you want to evaluate success On Consumer Tendencies
Based on? People buy tickets for a variety of reasons, just look at the Clippers. Theyve been turning a profit for decades and it has nothing to do with their success so a blanket statement like this is meaningless. On the C’s title
Not really, Rondo was a stud by draft measurements and Perks signing screams moneyball tactics On your system for evaluating success
So let me get this straight, you can credit the success to players but all the blame gos on stats for them losing? Thats pretty poor system man. I based success on whats expected of the team and what they control. For example, if a team drafts a solid prospect late in the draft due to analytics then its made a successful decision ala Ty Lawson. I dont look at raw W-Ls because there are alot of factors that go into that On OKC Building Plan
I dont know, nor do I care. All I know is that awhile back they were nowhere near where they are now. Thats a SUCCESSFUL TURNAROUND. One that actually only happened because Portland actually went against what the stats and models said to do and went with conventional wisdom of taking big over small when every metric out there said to take Durant On Draft Models being better indicators of talent vs intuition
They are meant to be an upgrade, whether you win a title or not is dependent on so many external factors that trying to pin them down to a single aspect is laughable, that you find it silly is again irrelevant when you look at the historical results of draft rating models this decade, it is a quantifiable fact that they have done a better job of landing impact talent.
@Tom: Sometimes, it’s not always about the best fit that gets it done. Even though they won, was Ron Artest really a better “fit” in LA than Trevor Ariza? Sure, it works now, but are the guys surrounding Kevin Durant really championship caliber pieces? Sometimes, you have to go away from what the numbers say that works and put a squad together that you know is going to be capable of winning it all when the time comes. That’s all I’m saying. Sure, certain systems fit certain players better, but if you’re always looking for the “right fit,” your team is not going to be very good. Sometimes, you have to be unconventional if you want to compete for something other than just regular season victories and whether it suits a player or not, these guys are pros. They’ll adjust. Analytics are what help you decide whats the best fit, intuition isnt going to tell you how often playerA made a shot from zone B when open. Because you are incapable of watching every possession from every player, going by gut instinct instead of factual data is counter productive. Your eyes can lie just as much as the data. So there is no SINGLE WAY of building a team, simply put if you ignore the stats it will eventually catch up to you
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