On Thursday January 24th, basketball operations analyst for the San Antonio Spurs, David Kaplan, paid a visit to Davidson’s campus to speak to the ‘Cats Stats group about his work and experience in sports analytics.
Kaplan graduated from Wesleyan University in 2013 with a degree in biological chemistry. He began working for the Portland Trail Blazers in 2014 before working as first a quantitative analyst/systems developer and later manager of basketball analytics for the Charlotte Hornets. He began his current position with the San Antonio Spurs in October of 2018.
We reached out to Kaplan with questions regarding his trade and his valuable experience in an industry in which sports and statistics intersect:
From your own experience, is there a conflict between a scout’s intuition and the data provided from your models, or do these methods work cooperatively? In such a case, which methodology has a higher value to the GM and coaches?
There will always be some type of conflict between scouts and analysts; they use two very different set of tools to evaluate the same thing (players, teams, etc.).
The way in which each organization balances scouting and analytics varies from team to team. A lot of GMs and decision-makers are not inherently technical people. Often times they are former players from an era when statistics were not as readily available and applicable. Some executives are extremely open to numbers and some are a little more hesitant. In my experience, if analysts possess solid communication skills and can explain their models/findings in a manner that is accessible to the decision-makers, analytics will be a large factor in the process.
How does your previous position as a statistical programmer for the Portland Trail Blazers differ from your current position as a basketball operations analyst for the San Antonio Spurs?
When I was in Portland (as an intern), I was supposed to choose a title for my business card. “Statistical programmer” sounded nice; I went with that. Titles for analysts around the league vary greatly. Some are listed as ‘basketball strategy’, ‘quantitative analyst’, or ‘systems developer’, but, often, these titles are only relevant for things like business cards and the media guide.
In what ways did your undergraduate experience prepare you for success in your field, and in what aspects, if any, did you feel unprepared as you entered the job market?
Good question, but this is a difficult one for me personally because my professional career path is so different from my undergraduate one.
I’ve always had an interest in sports, especially the coaching and roster management side. Yet, my high school didn’t offer any programming classes and I wasn’t in any social circles where I was exposed to it. This, combined with my lackluster enthusiasm for calculus, turned me away from trying computer science or advanced math classes in college.
As with most college students, I was 19 when I chose my major; I enjoyed the biology and chemistry classes I took, learned a lot, and tackled some interesting problems. However, somewhere early in my senior year, I came to the realization that it wasn’t what I wanted to do for the rest of my life…or at least the next X years. I began dabbling in code and machine learning during school and realized I could combine that with basketball to obtain a job I was excited to wake up for every morning.
Wesleyan provided me with a lot of the tools necessary to approach problems and figure out the best/most logical solution. In a way, it taught me how to think and work as an adult. Maybe I would be a better analyst if I enrolled in a technical institute and studied programming and stats from day one, but I am a firm believer that college is what you make of it. In my experience, I don’t think it would be fair to say, “liberal arts grads are good at X and lack in Y”.
That being said, I’ve worked with analysts who have PhDs from top universities, a few without a college degree, and everything in between. The PhDs probably stand out a little more when you are going through hundreds of resumes, but, in the end, your educational background is just a jumping off point, it does not dictate future success.
Since I was mostly self-taught in programming and statistics/machine learning, I did feel unprepared in a lot of ways. Many people, both in and out of sports, will talk about experiencing the Imposter Syndrome; I definitely did. For me, it was important to know what I didn’t know and try to learn as much as I could from my bosses.
Based on your own post-graduate experience, what advice do you have to share with students as they search for jobs in the field of sports analytics?
As someone who has gone through both sides of the hiring process a few times, it is extremely important to have some kind of work product. There are so many eager and talented candidates with excellent resumes, but it is no secret some can be generous with the skills section of their resumes. If there is something (a blog, paper, project) to demonstrate your abilities, attach it. You will stand out.
Any other suggestions?
1. It is okay not to know! The ‘stats person’ is frequently supposed to have all the statistics/answers/studies ready to recall from memory. That is unreasonable and will inevitably lead to errors. There is nothing wrong with saying “I don’t know, but I will go find out.”
2. One of the biggest components of the job that is not brought up enough is soft skills. Many of the people you will be working with will not be as technically skilled as you are, it is very important to be able to communicate your findings in a way that those around you can understand and trust. You could produce the best product in the world, but if it can’t be explained to the higher-ups, the effectiveness will be lost.