Hogging the Puck: A Follow-Up (All That You Wanted & More)

Updated: November 15, 2012 at 7:45 am by Ben Wendorf

After initially publishing the piece on players who “hog” the puck (players who take a high percentage of the Fenwick attempts-for {shots + missed shots} when they’re on the ice), I received a lot of helpful feedback and queries about the metric, which I called “percentage of attempted shots,” or %AttSh. Some of the questions revolved around, “What if the player is playing with someone with a high %AttSh, like Rick Nash or Jeff Carter?” I had another question wondering if the %AttSh had a normal distribution like all of us stats folks love. And our own Eric T. wondered aloud what a chart of the player’s shooting percentage minus his linemates’ shooting percentage (x) would look compared to the player’s %AttSh (y). Some of these questions I’d been wondering about myself, but some were angles I hadn’t considered, so I figured I ought to put together a follow-up post to tie up some of those loose ends. Enter if you dare…

Dicing the data

So, initially I think the distribution question should be addressed first. Those of you who follow my Twitting will have already seen this, but here is the distribution of forward %AttSh performances, with one percentage-point intervals.

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The next question, Eric T.’s, is more about level of effect. That being, there is a minimal amount of effect of teammates on a person’s %AttSh, but there is an effect, so what does it look like? Taking the player’s shooting percentage minus their linemates’ shooting percentage on the x axis, and the player’s %AttSh on the y, our chart looked like this:

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As you can see, it doesn’t seem to follow that a better shooting percentage than your teammates means much at all in regards to how much you shoot; if anything, it means you shoot less. I’m not willing to admit that latter point, though…I think the balance is tipped a little due to shot volume, that the highest-volume shooters are not necessarily the best shooters.

When puck hogs collide

The final question gained some steam off this interesting post from Canucks Army’s Thomas Drance, and was further suggested by Cam Charron, that teammate %AttSh could be having an effect on the player’s %AttSh. This was something that I had though about before, and wanted to explore, but I also knew that putting together the data-set would be kind of a pain in the ass. Thankfully, Gabe Desjardins’ Behind the Net data once again made everything easier (he’s made this whole thing easier, people; seriously, if you are interested in this stuff, Gabe’s Behind the Net site should be your #1 bookmark with a bullet). I was able to compile a list of forwards over the past five years who had a drastic change in linemates – basically, any forward who went from spending 50%+ of their ice time with a primary forward and 30%+ with a secondary forward, then spent 50%+ and 30%+ of their ice time the following year with different forwards, would make my list. I could then compare the change in the %AttSh of linemates to the change in %AttSh of the players in question.

I ended up with a data-set of 293 forwards. Among that data-set, the correlation between the change in linemates’ %AttSh from one year to the next to the player’s %AttSh one year to the next was 0.267, suggestive of a slight positive (though very weak) correlation. Since the differential took the previous year’s %AttSh’s and subtracted the current year’s %AttSh, this mean’s that when your linemates’ %AttSh went up, yours has a slight possibility of going down. In a chart, it looks like this:

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To build this chart, I ordered the “Tm %AttSh Diff” from lowest to highest, then diced them into consecutive buckets of 10 (the last two buckets were 12 and 11) and took the average “Plyr %AttSh Diff” related to those buckets. This sharpens our focus on the trend a bit better than the scatterplot, by bring our variance into line a bit. As you can see, the change in linemate %AttSh doesn’t seem to swing player %AttSh much further than 2.5%, which doesn’t move beyond the normal variance of a player’s %AttSh from one year to the next. So it’s not an expected effect, though I speculated to Cam that at the extreme ends of the spectrum (the Rick Nashs or, on the flip side, Kyle Wellwoods) you’ll probably see some effect due to the fact that we’re comparing two things (Tm %AttSh and Plyr %AttSh) that are closely related.

The last thing I want to say relates to some of the things Eric T. mentioned in the first post. If this is behavior, how might it be changed? What is the ideal combination of %AttSh linemates? Does simply taking a low %AttSh with a high shooting percentage and getting them to increase their %AttSh equal success?

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