From the pages of Convergence 

Jonathan Erlichman: I got interested in baseball from the analytics side first. My parents gave me Moneyball to read; it came out when I was about 13. Then I started reading a lot of blogs on baseball analysis including Tom Tango’s, and books on Baseball Prospectus. 

After UCC I went to Princeton and majored in math. I spent more time just learning about baseball and doing research and eventually I tried to see if I could break into the field. I ended up getting an internship with the Commissioner's Office one summer, and then with the Blue Jays after I graduated, and then another internship with the Tampa Bay Rays after that. I spent 12 seasons there in total as an analytics coach before transitioning to a front-office position last year. 

 

JE: Yeah, I think the idea of taking a research-oriented viewpoint and trying to figure out the right questions to ask and what questions aren’t being answered is important. Then, how can we answer those better and better understand different layers of the game? That sort of approach has always stuck with me. 

JE: The available data is behind where baseball is at, although it's really starting to catch up. For example, the NHL now has three years of player-tracking data that's available to all the teams, which is similar to the original Statcast systems put out by MLB back in 2015. So that data set’s starting to grow, but in terms of quantifying the game it's obviously a different game with different challenges. There are certain sets of stats in baseball that don't exist in hockey, but that's also the challenge and the opportunity to try to better understand all of it. 

JE: I wouldn't say there’s a typical day, and it’s also evolved over the seasons and years. One part of the role was just to be a resource available to the rest of the coaching staff in terms of the data we had available. And another one was to be able to provide a different and outside perspective on the way that we train, and the way we structure our days. So each day could vary a lot between those aspects of the role. I would spend time in the batting cages and with the pitchers during sides at times and attend the advanced meetings before a series. 

JE: No, you're really piggybacking on a lot of foundations that were laid by people earlier within the baseball research community. Like I said, I learned a lot from Baseball Prospectus, both their books and also the research on Tom Tango's blog, so a lot of the foundational principles are pretty similar when it comes to how to do research and how to set up problems. 

 

JE: (laughs) Math and physics were two of the higher levels. Chemistry and economics were in there somewhere, then I guess English and Spanish were the other two. 

JE: I think the biggest thing would be just to learn about the research process and get your hands dirty. Especially now in baseball, there are such rich data sets available to the public — Baseball Savant in particular — at the play-by-play level, the pitch-by-pitch level, and some of the more player-movement-based Statcast metrics. 

So if you're able to think about questions you have about the game, think about ways to potentially solve and answer those questions. There's the ability for people in the public domain to do research that maybe ten years ago was only available to people working for teams. 

(The full Q-and-A appeared in the November “Changes” edition of Convergence.)