Shot Types and Shot Targets

by Hawerchuk on Aug 26, 2011 8:44 AM EDT in Shot Quality

There aren't a whole lot of interesting outcomes from the shot target analysis I started earlier, so I thought I'd do a data dump for posterity. First, the distribution of shots by shot type in our database over the last four season:

	Slap	Snap	Wrist	Wrap	Tip-In	Backhand
Total	108291	24188	120451	3379	12415	16553
Pct	38.0	8.5	42.2	1.2	4.4	5.8

Here we have the percentage of shot targets by shot type. Recall that location 1 is high/right (from the goalie's perspective), 2 is high/left, 3 is low/right, 4 is low/left and 5 is the five-hole.

%	Slap	Snap	Wrist	Wrap	Tip-In	Backhand
1	8.7	9.3	8.4	0.6	3.7	5.6
2	11.3	14.8	11.7	0.8	5.3	10.6
3	31.1	34.3	30.6	39.4	31.2	29.3
4	19.7	21.5	21.7	49.3	26.3	35.5
5	29.1	20.2	27.7	9.9	33.5	19.0

You can see that there are more shots directed at the goalie's right hand; not surprising given the preponderance of left-handed shots in the NHL. Exceptions to this rule are wrap-arounds (where a left-handed shot is going around to the left side of the net) and backhands (which we'd expect to be opposite.) Players rarely get wrap-arounds off the ice (no surprise) but deflections are primarily low too, which I wouldn't necessarily have expected.

Shot%	Slap	Snap	Wrist	Wrap	Tip-In	Backhand
1	8.9	23.5	15.8	31.6	57.1	27.7
2	8.7	19.1	12.7	7.1	49.0	21.2
3	4.9	10.5	7.6	8.9	30.0	13.3
4	5.3	11.3	8.4	7.9	28.4	11.8
5	5.1	9.4	6.1	12.3	24.5	12.8

The lesson here is that if you're close to the net and you get the puck up, you're likely to score - the goalie is probably down. This is less true of slap and wrist shots, where the goalie is more likely to be covering the upper half of the net.

Wrist shots look like they're much more effective than slap shots, but they're also taken closer to the net. If we normalize the set of all wrist shots as though they were taken from the same locations as slap shots (ie - use wrist shot shooting percentage and slap shot totals), we actually find that wrist shots are no more effective than slappers:

	# Slap	Slap G	%	Wrist G	%
1	9428	841	8.9	938.6	10.0
2	12279	1074	8.7	927.2	7.6
3	33670	1642	4.9	1780.8	5.3
4	21379	1129	5.3	1060.7	5.0
5	31535	1618	5.1	1380.1	4.4
Total	108291	6304	5.8	6087.4	5.6

We can also see that wrist shots and slap shots are roughly as likely to hit the upper half of the net if we normalize locations:

	# Slap	Slap Up	Up%	Wrist Up	Up%
Zone 1-2	108291	21707	20.0	24438	22.6

Anyways, I'm all ears if you have any ideas for what we can do with this data. I was thinking of comparing outcomes by handedness and wing, but I fear that we won't get much in the way of exciting results.

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Comments

organize by player?
Maybe take a couple years data, see who is the best (highest goal %), at tip-ins? Maybe wrist, slap, and snap as well?
Maybe separate this data by forwards vs. defense?

by Ahmad Bradshaw on Aug 26, 2011 11:43 AM EDT reply actions

It might be interesting to look at the performance of road teams in each arena over the last four years. That would only use a quarter of the data, but you’d probably be able to bring out some of the more obvious rink biases, which would help greatly if using the data to evaluate individual players.

The biggest fanana of the Havana Bananas.

by Scott Reynolds on Aug 26, 2011 12:32 PM EDT reply actions

Lots of interest here.

One assumes that you also have x,y coordinates with the data. The you can do something like what we did below

Link to slides

If not it is still worth looking at things like: Is the distribution of shot target the same for each goalie (plus or minus noise)? How about the save percentage for each goalie by target? Or for goalies with more than 1000 shots faced? How about distribution of shot target by even strength v powerplay?

I do think the handedness would be interesting.

I’d be interested in knowing how you “normalize the set of all wrist shots as though they were taken from the same locations as slap shots.”

by schuckers on Aug 26, 2011 1:23 PM EDT reply actions