Photo: Michael Miller/Wikimedia/CC BY-SA 3.0
The 2012-13 season was to have begun yesterday. It didn’t. That’s mostly extremely annoying, but one of the silver linings is an ability for me and get around to doing some things that we had perhaps intended to finish earlier. One of those things for me is calculating the individual point percentages for the 2011-12 regular season.
Individual point percentage is a calculation of the number of times an individual player gets a point (either a goal or an assist) relative to the number of total goals scored while he’s on the ice. So, for example, if a player is on the ice for fifty goals-for during five-on-five play over the course of the season and he gets a point on forty of them, his individual point percentage would be 80%. I was first introduced to this statistic by Tyler Dellow who figured (quite rightly) that points alone don’t tell us who’s driving the play in the offensive zone, and that this little statistic might help.
A lot of the really good offensive players in the league should find a home near the top of the chart (which is exactly what you’d expect from a statistic that’s trying to tell you who’s driving the play in the offensive zone). But as with most things based on sample size of less than 100 (no one was on the ice for more goals-for at five-on-five than Steven Stamkos at 87), the year-to-year variation is quite high. Over a long period of time, the cream mostly rises to the top, but in any one season, you can get some funky results, similar to the kind of thing we see with shooting percentage.
The average individual point percentage for a forward who was on the ice for at least 25 goals-for (there are 258 players) is 69.1% and the median is 69.5%, while the standard deviation is 8.3%, so that should provide some idea of whether or not a player is doing well (i.e. those players above 77.1% or below 60.8%. The data I’m using is from five-on-five play only and the raw data comes from Gabriel Desjardins’ behindthenet.ca:
As you can see, there are a lot of very good players on this portion of the list. Players like Henrik Sedin, Ilya Kovalchuk, Marian Hossa, Mike Ribeiro, and Ales Hemsky put up these kind of numbers consistently, so we can be pretty confident that they’re driving the play. Others like Logan Couture, Jordan Eberle, and Pierre-Alexandre Parenteau don’t have a long enough track record to say either way, but are obviously off to promising starts (though Parenteau’s number should give Avalanche fans pause about his new deal). Still others like Kyle Brodziak and Matt Halischuk probably just don’t belong here. Really, this isn’t much different than most statistics: a dollop of common sense will go a long way.
There are a few players here that we’d likely expect to be a bit higher. Steven Stamkos just missed the first cut-off, but others like Jarome Iginla, Daniel Sedin, Sidney Crosby, and Alex Ovechkin (to name a few) also feel like they should be in the top group. When I take a look at the longer term trends on Monday (combining the data for the last five years), we’ll have a better idea if this looks like bad luck for some of the league’s superstars.
There are a lot of pretty darn good players on this list, many of whom have teammates on some of the league’s top lines: Joe Pavelski was relying on Joe Thornton to drive the offense, Jaromir Jagr was relying on Claude Giroux, and James Neal was relying on Evgeni Malkin. Some of this has to do with role (Pavelski has generally been quite good by this measure, but wasn’t consistently paired with Thornton until 2011-12), but even with that caveat, I’d prefer to keep (and pay) the guy who’s driving the play.
Even if you’re not a very good player, you’ve had some bad luck to end up at the bottom of ths list. Antoine Vermette was having an awful season by the percentages in Columbus, and it got him traded. Rene Bourque was also moved, but he waited until arriving in his new destination before going into the tank. Dave Bolland didn’t get traded, and in fact, managed to maintain his sterling reputation as a defensive center despite seeing his IPP fall to the very bottom of the list.
This obviously isn’t the be-all end-all for analyzing which players are driving the play (or being impacted by luck), but I do think as one tool among many, it can be very helpful. As I mentioned earlier, I’ll take a look at the longer term trends on Monday.