There’s a new way of looking at save percentage available at War-On-Ice that I find interesting. They break save percentage down by shot location as coded in the NHL play-by-play reports from each game. Whenever a shot is officially scored, it’s given an X,Y coordinate to map its location on the ice surface. Shots from some areas have a better chance of becoming goals than shots from other areas. This seems obvious, but it’s never before been systematically incorporated into goaltender analysis on a large scale.

It’s an attempt to get closer to turning shot quality into to something we can analyze objectively (as opposed to subjectively). Shot quality may not have a strong effect on skater performance, but I can’t imagine that it doesn’t have an effect on goaltender performance, a claim being investigated currently by Chris Boyle. And if we can ever get a real objective measure of shot quality, we may find it has a larger than expected effect on skater performance as well.

What War on Ice does is scrape the X,Y data from the NHL reports and figure save percentages for three zones: a high scoring zone, a medium scoring zone, and a low scoring zone. They report those on the website, but they also combine the three into an Adjusted Save Percentage, which normalizes each goaltenders workload using league shot distributions. Thus if a goalie sees the same proportion of shots from the various areas as the league average, his save percentage will be the same. If he sees a higher proportion of tougher shots, he’ll be rewarded, and so on.

Last week Puck Daddy’s Jen Lute Costella gave a very thorough examination of how the zone breakdowns look over time and talked about the kind of context that this information can potentially provide. As she says, this is a good start at giving us a way to check our biases about a goaltender against something a bit more objective.

There are still some questions about exactly what this kind of data is telling us. Does this give us better information about goalies than traditional save percentage? How much better? In what way? How much does this tell us about goaltender talent and how much about randomness? How much does it tell us about team effects? Is there repeatability? (Yes, some.) Does this measure stabilize faster? If you have a goalie who’s better than average at close shots, for instance, does that change anything about how you play defense?

And it is important to note that shot location does not equal shot quality. It’s one factor in shot quality, but it is far from the whole picture. Add in the fact that there are a lot of errors in the RTSS shot records and you have what amounts to a step towards a measure of shot quality, not a true measure of shot quality. We’re further along the road than we were before. We’re just not where we need to be yet.

Still the concept of zone-based evaluation of goaltender performance has merit. Goalies know that some shots are harder to stop than others and that shots from the doorstep are tougher than shots from the perimeter. In other words, shot location is one element of difficulty and we haven’t even been looking at difficulty at all to this point.

Being in the middle of a visual analysis of Evgeni Nabokov for Raw Charge, and being curious as to Nabokov’s workload, I applied the zones that War on Ice drew up and mapped all the shot attempts (corsi) he faced in the four games he played. As I’ve noted before, I think corsi gives a better understanding of true workload for goalies than shots alone. Green dots are 5v5 saves, blue dots are 4v4 saves, red dots are goals, gray dots are misses, and purple dots are blocked attempts. Circles are even strength attempts and squares are power play attempts.

[My numbers are going to differ from War on Ice’s numbers because I tracked location manually and they took the data from the RTSS reports.]

New Jersey and Columbus were noticeably better at getting attempts in the middle of the ice than either Calgary or Minnesota, at least in these particular games. New Jersey also got a lot of blocker-side attempts. The Columbus game was the toughest of the four in terms of corsi locations, if not in some other components of shot quality, and the high number of blocked shots in that game are a factor mostly of how the Blue Jackets played the Lightning, although there is some rink bias there as well.

Another thing to note was that the Devils rarely took attempts without offensive support of some kind (Jaromir Jagr was the exception to that.) In other words, whenever the Devils took one shot attempt, they were likely to get another. In the Calgary game, the Flames had a greater proportion of one and done type situations, which accounts for both the perimeter shot attempts being a greater proportion of the whole and fewer center lane shot attempts. For instance, 15 of the Devils 43 attempts (35%) were taken from above the faceoff circles, and half of those from the more dangerous center point. The Flames took 16 of 40 attempts from above the circles, and only four from center point.

The Blue Jackets were able to maintain zone time by limiting space and they were quick to get shot attempts off, even when there was traffic. Full half of Nabokov’s workload in that game came from the “home plate” area, although many of these shots were blocked. Only 19 of their 60 attempts came from above the circles, a majority of those from the right point.

Here are the breakdowns by zone for all four games. The colors in the charts correspond to the colors in War on Ice’s zone map above.

Shots:

Zone ESSVS PPSVS ESGA ESSV% PPGA PPSV% All Shots by zone Rt Pt 4 2 0 1.0000 0 1.0000 6 C Pt 3 2 0 1.0000 0 1.0000 5 L Pt 4 1 1 0.8000 0 1.0000 6 R1 7 1 1 0.8750 0 1.0000 9 R2 5 1 0 1.0000 0 1.0000 6 High Slot 2 0 0 1.0000 0 2 L2 3 2 0 1.0000 0 1.0000 5 L1 4 1 0 1.0000 0 1.0000 5 R Low 4 0 1 0.8000 0 5 R Slot 1 0 0 1.0000 0 1 Slot 0 0 1 0.0000 0 1 Low Slot 10 3 2 0.8333 5 0.3750 20 Left Slot 2 1 0 1.0000 0 1.0000 3 Left Low 8 0 0 1.0000 0 8 Down Low 1 0 0 1.0000 0 1 All 58 14 6 0.9063 2 0.8750 83

Corsi:

Zone ES MISSED ES BLOCK ESSVS ESGA ES CorsiSV% Corsi by zone Rt Pt 5 6 4 0 1.0000 15 C Pt 5 4 3 0 1.0000 12 L Pt 3 6 4 1 0.9286 14 R1 0 3 7 1 0.9091 11 R2 2 2 5 0 1.0000 9 High Slot 2 5 2 0 1.0000 9 L2 1 2 3 0 1.0000 6 L1 2 2 4 0 1.0000 8 R Low 1 4 4 1 0.9000 10 R Slot 1 2 1 0 1.0000 4 Slot 3 1 0 1 0.8000 5 Low Slot 3 0 10 2 0.8667 15 Left Slot 0 0 2 0 1.0000 2 Left Low 3 0 8 0 1.0000 11 Down Low 0 0 1 0 1.0000 1 All 31 37 58 6 0.9545 132

So where did he see the greatest workload?

Jen Lute Costella noted that the highest scoring areas of the ice are almost always the areas where goalies see the fewest shots. Nabokov saw 12 ES shots and 8 power play shots from the low slot, right in front of the crease. It was the busiest of the 15 zones in terms of both shots and corsi. It would be interesting to know how this plays out league-wide. It seems logical that with goal-mouth scrambles accounting for a lot of action that this kind of result would not be uncommon.

However, if instead of fifteen smaller zones, we look at three larger ones—the high, medium, and low scoring zones as a whole—Nabokov saw the bulk of his workload come from the low scoring zone, just as most goaltenders do.

At 5v5 High Medium Low Total Shots 13 16 35 64 Corsi 20 42 70 132

This is much more in line with overall patterns. About 20% of the on-goal shots Nabokov has faced came from the high scoring area, while the medium and low scoring areas constituted 25% and 55%, respectively. In fact, he saw perhaps fewer shots from the more dangerous areas than is typical for larger samples.

There’s not enough data to tell us anything conclusive or predictive about Nabokov’s performance, but it does give some context to the kind of workload he has been asked to contend with. There have been some tough parts but the overall weight of the workload has been in line with league tendencies. It’s also important to note that the one game where he had a meltdown (Minnesota) saw the least difficult mix of shot distribution of the three games. So there’s no real way to blame that performance on shot quality. Which ought to tell us something about the nature of goaltending if nothing else.

What this does show, I hope, is how shot location can give us a different kind of objective information about goaltender performance than we can get otherwise, even if it doesn’t give us magic-bullet-type answers. Context, as they say, is everything.