A podcast fueled by professionals bridging the gap between Sports and Business. Enjoy as guests share stories & experiences from the playing field to the board room.
016 - Risk is Opportunity: Measuring Analytics in The NBA - with Aaron Blackshear
Aaron serves as the Director of Analytics for the NBA's Minnesota Timberwolves, and he has done so since September of 2020. Before joining the Timberwolves, Aaron broke into the NBA as the Analytic Systems Coordinator of the Detroit Pistons, ultimately being promoted to the Director of Research and Analytics of the Pistons. Aaron has previously held positions as an Actuary Consultant for Milliman and Optim Insights and he resides in Minneapolis with his wife and his two-year-old son.
What is it like for you to be working for a Basketball Association in the state of hockey?
This is a weird season because there are no fans at the games, but it seems like people are really into the team here. I see timberwolves gear around town when going out shopping and stuff. So it doesn't feel like they're second fiddle to the wild, it feels like you know, people are pretty into all the teams here.
Can you talk a little bit about the journey that ultimately landed you with the Detroit Pistons back in 2016 and how that has made you who and where you are today?
My first 15 years of professional work as an actuary, of course, where I worked for a health insurance company and a couple of consulting companies. For people who don't know, actuaries are sort of the math and statistics people that that work for insurance companies or hospitals or auto insurance companies, life insurance companies and usually are applying math and statistics to the quantification of risk. I specialized in the health insurance industry, but also throughout my time working as an actuary, I tended towards the more technical side of things like doing a lot of programming work, you know, being sort of the tech support guy amongst, the teams of actuaries I worked with. That was really what I enjoyed doing was more of the technical side of things. I built up a pretty solid skill set of programming skills, database skills and I was always really interested in sports analytics. It started out with baseball, you know, the Moneyball revolution that kind of took off in the early 2000s. Even before the movie, in the early day back in the old message boards, back in the day where people are discussing this stuff it was just really intriguing to me. I actually had a boss at my first job, who was pretty into it, as well and he kind of got me, you know, digging deeper into those rabbit holes of what some of the really cool work was being done in the industry. So I mostly applied it to trying to prove that the Cubs were the best team and the Cubs had the best players and winning my fantasy baseball leagues. There was a lot of really great public work going on in baseball at the time so it was nice, you didn't have to do a lot of your own work. It was really when the other sports started to take off when a lot of the opportunity popped up when, when basketball and football started to realize like, "Hey, we should be using data to make better-informed decisions as well."
How did the Detroit Pistons ultimately come into the picture?
As more teams started to hire people, I started to realize that this is something I could actually do for a job, I started exploring what I have to do to do that. How many teams are hiring, what are they looking for, and what employees? At the time I actually was living in Colorado, I met someone who worked for the Colorado Rockies. He agreed to have a beer one night, and you know, I just picked his brain about it. It was a bit of a discouraging conversation at the time, because he said, "Your best bet is to have a friend or family member who owns a team or already works for a team," and that’s not really anything I can control. But I still kept exploring, and I was still focused mostly on baseball at the time, but starting to get really interested in the basketball side of things as that was really starting to take off. So then in 2013, when I was starting on the process of leaving Optim Insight, I said, "Hey, if I'm going to look for something new, why not try this sports thing?" So that summer I started searching online and looking for any team that was hiring and just applying for everything. Anything that was posted, I would just apply for it just to see what would happen. Sometimes I heard nothing, sometimes I got an immediate canned rejection letter that was clearly just generated by a computer. But a couple of the teams wrote back and they would write some follow-up questions. Some of them have some technical tests, they would have you do some pretty basic stuff. Ultimately, It didn't work out, but I did gain a little bit of insight at least into what they might be looking for. I also realized that I wasn't quite prepared and I didn't have any of the requirements so I had no shot.
What were some of those requirements?
At the time, a lot of people were listing that they wanted an advanced degree. They wanted a lot of experience doing this actual work. It's one of those situations where you have to have the experience before you get the job. I also realized that despite having some decent technical skills, I wasn't quite on the level of what they were looking for. Seeing even some of the public work that was going on out there I thought that's really good stuff that I'm not quite capable of doing yet so I need to get to that level if a team is actually going to give me a shot. So then that was 2013 and ended up getting a job at Milliman then later in 2014, that fall, I got a phone call one day from Gabe Farkas with the San Antonio Spurs. He mentioned that "Hey, you'd applied for this job with us last year. It wasn't the right fit for the role we had, but we have this new and we're hiring for and I remember your resume, I think you might be an okay fit for it." I ended up doing a phone interview, a technical screen where they sent me some work to do and I had to send back, going out for an on-site interview. I ended up not getting that job, but it made me think that this could happen if I keep pushing for it. I was totally thrilled, like by the fact that he even remembered me from a year before and reached out to me unsolicited.
So you were realizing that you were getting closer. What did you do to ultimately bring yourself over that mountain top?
I just kept working on my technical skills on the side, building up a pretty good database of basketball data at home that I could work with so I could do some projects to be able to show people some work that I'd done. I just kept trying. Typically teams would hire in the offseason so jobs would get posted in the July-August timeframe. So that following summer in 2015 I just applied for everything. Again, I got some rejection letters, got some technical screens, didn't get hired again that year, but just kept plugging away, kept plugging away. I started to do more networking as well, just connecting with other people that were doing public work. A lot of the guys at the time were writing for Nylon Calculus, and have since gone on to work for teams, just connecting with those guys online as much as I could. I didn't go to any of the industry conferences. They're expensive and once you're with a team and they're paying for it it's worth it, but it's hard to justify, you know, a $600 ticket when it's out of pocket. Then next summer 2016, the Pistons posted a job. This is what I got really excited about because, for the first time, it was a job that seemed right in my wheelhouse, as far as the specific things they were looking for. So I was in the process of planning a wedding as well and I applied for the job on a Sunday evening and got a call on Tuesday. They gave me an assignment on Wednesday, and I had to turn in by Friday. The following week I got the second round and another technical screen with another two-day turnaround. So I went through those couple of rounds and just kept progressing and they called me for an in-person interview. I got married on Saturday, then I flew out on Tuesday for my in-person interview to Detroit, got a call on Friday that I had the job offer, accepted right then and there, and two weeks later had packed up all my stuff into my car was driving across the country.
What were you doing for the Pistons and what was their need that you filled?
I was the Analytic Systems Coordinator so essentially there was a new data vendor they were going to be using and they were going to have this massive influx of new data, and they needed someone to come in and set up some back end infrastructure for that, and then start to make sense of the data so they could use it in the analysis they were doing. That just fits really well with some of the experiences I had in healthcare. I'm just setting up easy-to-use, efficient back-end systems, and then building out our whole reporting infrastructure from there. Having done all this basketball work on the side like setting up my own database at home for personal use, and, building some of my own tools and reporting infrastructure, all fit perfectly with what they were looking to do. I was fortunate in that a lot of the jobs that teams are hiring for more heavy on the statistics side, like the predictive modeling which are the guys with PHDs and stats. While I've done a lot of statistical modeling as an actuary, that wasn't my biggest strength. My forte was more like infrastructure and programming so for them to be looking for that more than the modeling side was really fortunate for me.
What is the future of analytics in the NBA from your perspective?
The future is happening right now with player tracking data, and all the additional insights we've gained from that. It's been in the league less than 10 years now and we've learned a lot from it, but it's still a largely untapped resource and I think there's still a lot to be learned from there. Getting that type of player tracking data for leagues other than the NBA because right now, it's mostly just an NBA product and we've got players are coming out of college player, and we're coming from overseas. So just learning how to best make use of that player tracking data is a big part of it. As we talked about before, the sports science side is going to be a big part of it. You're trying to better quantify and predict injury risk and fatigue and how that impacts performance.
Are you guys doing any analytics around fans in the stands and homecourt advantage against away and how that's changed during the current world that we live in?
I've seen some stuff being done publicly with that. When they did the bubble last year, it was interesting because it was a natural experiment to see how much does travel matter and how much does actual home court matter. It's weird though because it was such a unique situation that it wasn't going to be necessarily replicable to a normal season and there weren't that many games to truly learn, like, the variance from game to game for just the bubble sample might be higher than the actual effect of those things. I think everyone is predicting the edge of homecourt advantage, and predicting how much travel matters. Like we're on the second game with back to back and the other team has been off for three days, how will that affect our team. I think every team is doing, you know, the basic stuff there. Obviously, teams are pretty close to the vest with what they're doing there, but it's definitely something that has to be considered just even to evaluate how good all the teams are. Because us in Minnesota, being in the Western Conference, despite being in Minnesota, we have one of the worst travel schedules in the league. I think Portland is actually the worse as far as the number of miles traveled, but for teams like Detroit, and Cleveland, their schedules aren't so bad. They have a lot of short flights, and they have a decent cluster of cities, all in the same geographic region. So yeah, there definitely is a disparate impact across the league and it's important to quantify that.
What's one of your favorite data points or pieces of analytics that you like to collect and gather and review?
On the data, I have to be a little more coy, but I will just say I am a person who finds efficiency, very important. I can't get into specific stats or anything like that, but along a very similar line, one of the most enjoyable parts of the role for me and it's something that's related to what we do, though not directly all the time is the draft workout process. The league has pretty strict rules about how it's set up, you have six guys max, they can't play against professional players, and there's a time limit to what you can have them do. But seeing these kids come into what is almost like a tryout for an NBA team is very cool. Some of these guys are guys who are projected to be picked in the top five, some of them are projected to be undrafted and have to go overseas, but like seeing these guys chase their dream, it's a really cool experience.
So risk is always a part of this, tell me about your relationship with risk.
I forget the actual slogan of the professional actuarial society because I've been out of the game for a little while. But I think risk is opportunity is the site of actuaries slogan. Everything that we learn about being an actuary is about managing risk and quantifying risk. So I think you would find that most actuaries temperamentally tend to be more on the risk-averse side because it's what they do for a living and so much of their job is about, you know, not having a company go out of business, because of the decision, they may say that they're a little conservative on those types of things.
Have you ever looked at your analytics before a game, and said to yourself after reviewing that we're gonna have a good game tonight, or I really think we're gonna win tonight's game based on what I'm seeing?
There definitely have been some instances like that. It's generally just driven off of either a specific matchup or a trend that you see in the way a certain team is playing and you think the coaching staff has come up with a fantastic strategy for attacking that.