As a result, I have developed my own system, using my typical brute-force approach. The objective is to achieve the dual goal of approximating scoring probabilities while also being able to qualitatively describe the type of chances a team gives up. Based on the scoring probability information given in this blog post from Behind the Net, I divided the rink up into 5 zones: Crease Area, Slot, Mid-Range, Point, and Perimeter. I used rink markings to divide the separate zones, which isn't exactly correct based on scoring percentages but becomes somewhat necessary for classifying chances. For the sake of time, everything is eyeballed except for chances from the crease area, which are defined as anything within 15 feet of the net inside the edges of the crease.
Here is the rink diagram:
The primary drawback of this technique is that it does not differentiate between power play and even-strength chances. I could split them out, but obviously it takes a bit of time to go through and count the chances so for now I'm lumping them all together. There are also certain rinks that seem to measure shot distances differently than everyone else (see this article by Alan Ryder), which means that it is not really fair to compare results directly between, say, goalies playing for the New York Rangers and goalies playing for the Tampa Bay Lightning. I'll have to work on some way to adjust for these discrepancies.
Just to be clear, I am not presenting this as an improvement on existing shot quality metrics. I hope the results will be similar, but I would certainly defer to other methods if there is a disagreement because they are much more precise in terms of identifying exact shot distance, shot type, etc. The primary reason for doing this is to get a better sense of the type of shots each goalie is facing. Is he facing a lot of shots from the perimeter? How well does his team cover the point shot? How many close-in chances does he face? And so on.
I don't have a large enough sample size to get high-confidence estimates of scoring likelihood from each area, but based on a sample from last year's playoffs the approximate average save percentages are:
Crease Area: .800
Slot: .850
Mid-Range: .925
Point: .960
Perimeter: .980
I'm planning to use this method to break down this year's playoff results, as well as to take a closer look at Martin Brodeur vs. his teammates in my continuing look at how goalies contribute to shots against. Criticisms and suggestions are welcome in the comments or via email.
8 comments:
I'm kind of thinking that one day the NHL can have RFID chips in each player's helmet and the puck, with the possibility of gloves, skates and sticks too. Then stats could be automatically generated (with a few trial seasons where they're also manually tracked to determine accuracy and refine techniques).
Not that it would help your cause too much, but with the NHL always boasting about being on the forefront of technology, it's a logical advancement that could be made in the next decade or two.
Once stat generation is automated, there'd be no more human error and future goalies could fall under the scrutiny of people such as yourself to determine effectiveness and relative performance. Just an idea, think it might have some legs.
I also believe there is data on those game charts indication the type, time, and situation of the shot.
Awesome. Eyeballing the distances by general area is fine, but it'd be really nice if you differentiated by situation (ES, PP, etc).
Sounds great. It'll be interesting to see the results.
Actually, I'd be interested to see if the situation matters at all once the location of the shot origination is determined.
Does a crease area shot on the PP have a better chance of going on than a crease area shot at even strength?
Or, does having a PP mean you get more crease area shots?
Good idea.
I know that the "Fox Track" (highlighting the puck) was ridiculed a few yrs back, but one interesting use of that technology was the measurement of the speed (& presumably location) of shots.
As the first commenter said, it should be easy for the NHL to "chip" each player as well as the pucks to gather much more accurate data.
But, since the NHL is usually 10-20 yrs behind every other sport, I'm sure we'll be waiting awhile for that.
Who knows, perhaps someday we'll even be able to know exactly how much of the net each goalie fills (e.g. knowing the size of their equipment).
This project does sound really interesting CG. I'm definitely looking forward to the results.
As for the situational differences I tend to think you get more crease and slot chances on the PP than at EV which is the major cause of the drop in SV%. I also think point (and perimeter) shots have a better chance of going in because of there is a much higher liklihood of an intentional screen.
Re: PP vs. EV, Behind the Net also has breakdown of scoring percentages by location for 4 on 5 play. You can compare that to the other chart I linked in the post.
It is pretty clear that scoring percentages are higher on the power play even from the same location. I think this is mainly because the shooters have more time and space to make a better shot. There is also usually some traffic in front of the net, and the goalie is less likely to be set for the shot since quick puck movement is a lot easier on the power play.
It is more correct to split out chances by game situation, so if it doesn't take me too much extra time I'll probably do that as well.
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