This week is the third week of the Sabermetrics 101: Introduction to Baseball Analytics course at edX. So far I’m really enjoying it. The course is about being a data scientist as that discipline pertains to baseball. They talk about how being a data scientist is about the convergence of three areas: domain expertise (knowledge of the subject matter you are analyzing), computer skills & hacking, and math & statistical knowledge. (The actual slide presented is this one, developed by Drew Conway.) I’m enjoying learning SQL, why certain metrics are better than others, and in general how to approach baseball analysis scientifically.
I knew a small bit about sabermetrics going into the course. Sites like FanGraphs are invaluable for not only providing lots of data, but leading you to data that are meaningful by highlighting certain ones in their blog posts. Because of these and other sites I have gone from using OPS+ to wOBA/wRC+ as my go-to hitting metrics. I have seen the importance of walk rate, strikeout rate, and home run to fly ball ratio for both pitchers and hitters (but primarily pitchers). I’ve had experience using BrooksBaseball.net to see (literally, using the wonderful graphs on the site) how a pitcher’s pitch selection has changed over time as they age and how they attack certain hitters over time. I’ve seen how BABIP is deployed and used in analysis and am starting to get a sense of its limitations. I have started to understand concepts like correlation/causation, linear weights, regression to the mean, win probability, leverage, and others that help me sift through the noise and find a signal, to coin a phrase from Nate Silver. Speaking of Silver, it’s through his book that I came to find Bayesian inference as a tool that I both understand and find useful.
I’ve even had a chance to develop some Python skills. I am not a programmer but I play one on TV, having taken some classes in high school/college and worked in technical writing for software engineering companies for 10 years now. I’ve also been lucky to have the chance to put a lot of this research/knowledge in writing.
So when I came across the Sabermetrics 101 course I was intrigued. My scattershot approach was good from an autodidactic point of view but the straight line of a class is appealing to me. I came to the class hoping to get a foundational knowledge of the principles and concepts used in sabermetrics so I could really understand why they are used and not just that they are used. I feel this knowledge will help inform my writing, not only helping me write about interesting/informative things but also making sure I write about them accurately and precisely.
I also think that the principles of sabermetrics are applicable in other areas, which is another reason I a pretty gung-ho about getting deep into the class. The whole idea of using objective analysis to find out what metric(s) are important, the situations in which they are important or can be de-emphasized, and what factors contribute most to these metrics is fascinating to me. So is learning to become a better writer. And so is learning to become a more informed baseball fan, which helps me enjoy the game. An analogy: when I was a techno DJ, many people asked me if knowing how to spin records made it harder to listen to other DJs, since I could pick up on their mistakes. I always thought this was true, but I preferred to focus on the fact that I could also appreciate when another DJ was doing well and explain that to others (should they be interested in it).
I feel the same way about learning sabermetrics … I can now feel the impact of seeing someone’s wRC+ compared to another player’s, because I know what that means. This information informs the way I watch games and, as a big Orioles fan, how I feel when something happens. Like when Nelson Cruz faces a left-handed pitcher this year, I get excited, because he is destroying them in 2014, and I can help quantify by how much he is tearing them apart. I can also understand just how meaningful it is when Adam Jones walks because, well, he hardly ever does that. I can also do things like estimate how much he is impacting the team by not walking more often.
I guess it all comes down to mastery of skills, a desire that everybody has. Who doesn’t enjoy mastering a skill?
Speaking of which, as I talk more and more with friends and acquaintances about learning sabermetrics, they question they often ask me is — what will I do with this skillset? Most bring up gambling, but am not a risk-taker by nature (especially not with money) so that would be tough for me to swallow. I don’t have a desire to work in an major-league front office but I’d be curious what that’s like. At this point I’d say, I want to use sabermetrics along with writing to learn how to explain the game better to others, to help them feel what I feel when I look at Chris Davis’s heat map, to help them understand what it means when Ubaldo Jimenez relies on his fastball instead of his sinker. I’m currently doing this at CamdenChat, which is awesome because the site combines my fan interest in the Orioles with writing and sabermetrics.
Someday I’d love to get a job at FanGraphs, The Hardball Times, and/or another site/publication, or start my own site. Who knows. Until I figure that out, I have Module 3 of the course to complete this week!