Over the past year, I’ve watched a few people I know in digital life sink a fair amount of time into statistical modeling projects that other people might see as “just for fun,” if not downright frivolous. Last April, for example, public-health grad student Brett Keller delivered an epic blog post that used event history models to explore why some competitors survive longer than others in the fictional Hunger Games. More recently, sociology Ph.D. student Alex Hanna has been using the same event history techniques to predict who’ll get booted each week from the reality TV show RuPaul’s Drag Race (see here and here so far). And then there’s Against the Spread, a nascent pro-football forecasting project from sociology Ph.D. candidate Trey Causey, whose dissertation uses natural language processing and agent-based modeling to examine information ecology in authoritarian regimes.
I happen to think these kinds of projects are a great idea, if you can find the time to do them–and if you’re reading this blog post, you probably can. Based on personal experience, I’m a big believer in learning by doing. Concepts don’t stick in my brain when I only read about them; I’ve got to see the concepts in action and attach them to familiar contexts and examples to really see what’s going on. Blog posts like Brett’s and Alex’s are a terrific way to teach yourself new methods by applying them to toy problems where the data sets are small, the domain is familiar and interesting, and the costs of being wrong are negligible.
A bigger project like Trey’s requires you to solve a lot of complex procedural and methodological problems, but all the skills you develop along the way transfer to other domains. If you can build and run a decent forecasting system from scratch for something as complex as pro football, you can do the same for “seriouser” problems, too. I think that demonstrated skill on fun tasks says as much about someone’s ability to execute complex research in the real world as any job talk or publication in a peer-reviewed journal. Done well, these hobby projects can even evolve into rewarding enterprises of their own. Just ask Nate Silver, who kickstarted his now-prodigious career as a statistical forecaster with PECOTA, a baseball forecasting system that he ginned up for fun while working for pay as a consultant.
I suspect that a lot of people in the private sector already get this. Academia, not so much, but then they’re the ones who wind up poorer for it.