Over the past year I’ve increasingly been hearing words such as Corsi, Fenwick, PDO, and Zone Starts when assessing NHL player performance. As someone interested in hockey as well as being a professor of sports economics I increasingly thought I should take the time to learn what these things are all about.
As many of you know, wanting to learn something, and finding the time to do it, can be two different things. I started teaching a class in sports economics a few years back as a way to get myself to finally learn about this area of the economics literature, and so I figured if I put a few classes of hockey analytics on my sports economics syllabus, then I’d have to teach it (and hence learn it) or else risk embarrassment when I didn’t deliver.
This isn’t a blog about responding to incentives though, it’s about highlighting a few of the interesting things I came across in my reading. First thing is that there are a lot of sites available with data for these different hockey metrics. These sites are a great way to learn what the main measures are that people are using, and they usually contain a description of the measures somewhere on the site. Sites such as Behind the Net, Progressive Hockey, and Hockey Analysis are great places to start. As well, the NHL on its NHL.COM website will be adding a range of hockey analytics data (around 30 different measures) soon, and so is something to keep an eye on as well.
In terms of readings for hockey analytics, I didn’t rely on academic articles so much as what the hockey press and related bloggers have written in this area. Benjamin Wendorf at the Hockey News has a short but informative introduction to hockey analytics, as does Sean McIndoe and Steve Burtch . Coming from these articles you get a sense of what measures people use, how they have changed the way fans and team officials view player and team performance, and give a sense of the things to come in hockey analytics.
The Burtch article is especially interesting as it spends some time discussing the psychology of how humans make decisions, and how mental short cuts and small sample sizes can all lead to poorly made judgements and decisions, leading the reader to better understand how the new approach to hockey analytics can help partially overcome some of these problems.
Something mentioned in McIndoe’s article and seen recently with the NHL All-Star Game is the use of microchip technology in the sweaters of players as well as puck to provide real time data on things like player speed, how fast the puck is shot, possession time, or whether the player enters the zone with possession. As well through the reliance on microchips, we should see increased standardization of the data itself. For more, Alex Prewitt of the Washington Post has a great article on what microchip technology might mean for player tracking.
I’m still a bit of a beginner with these things but I will be teaching a 3 week course on sports economics in May and so will be sure to update this blog with what’s new at the time in this evolving area of hockey assessment.