The most basic unit of offence in hockey is the shot and so I have made a great many charts showing how efficiently teams generate shots in a wide variety of situations. For the purposes of making shot maps, I use "shot" to mean any unblocked shot; that is, a shot which is missed, saved, or scored. If I had access to shot location data for blocked shots I would almost certainly include them also, but I do not.

- Regions from which a team generates
*more*than the league average rate of shots are shown in**red**; - Regions from which a team generates
*less*than the league average rate of shots are shown in**blue**; - Regions from which a team generates shots
*at*the league average rate of shots are shown in**white**;

On the one hand, the Sharks had one of that season's better 5v5, offenses but in fact the number of excess shots is not very many. For instance, the red area in the slot is perhaps around 200 square feet (notice the reference circle for area in the bottom right) and so the excess shots from that area is about one per hour of 5v5 play. Such an advantage is non-trivial, of course, since such shots are dangerous, but one must also keep in mind that the spread of performance results in the NHL is quite small.

Because some of the later charts can be based on relatively few minutes, I decorate every shot
map with the number of minutes shown; for a team in a full season the number is very large.
For maps like this, the natural unit of measurement is the rate of shots. However, as we know,
not every shot from a given location is equally dangerous, even before we arrive at the question
of how talented the shooter or the goalie may be. Any number of factors may affect the danger of a
shot, like the shot type, if it was or was not a rebound or on the rush, and so on. Carefully
weighting these factors results in an "expected goal" probability for every shot; I have created
such an expected goal model, described here. However, these non-location
factors are not so easy to display in a simple two-dimensional map. To accomplish this, I have
created *another* expected goals model, *without* these non-location factors; including
only the skater strength state and the shot location. I call this "simplified" expected goals
model "xG_{0}". For every shot, I compute the xG and also the xG_{0} and compute
their ratio. A relatively dangerous shot from a given location (a rebound, say) will have a high
ratio (perhaps as high as 1.5 or so), and a relatively less dangerous shot from the same location
will have a lower ratio, perhaps as low as 0.7 or thereabouts. I use these ratios to weight the
shots in the map, so that teams who consistently take fewer but more dangerous shots can be
properly compared with teams that take more but less dangerous shots.

The total expected goal rate is shown in the neutral zone, together with the relative change from league average xG rate for the league that season. Here, the Sharks offence produced shots with an average danger of 2.55 goals per hour, which was three percent more goals per hour than the league average rate.

I also make charts to show power-play shot rates; they are easy to distinguish from the 5v5 charts because of their different colour schemes: here orange means "more than average", while purple means "less than average". The notion of "average" here of course is 5v4 average, and the scale is also doubled, since the variance in power-play shot rates is larger than in 5v5 shot rates.

In this example, Washington's extremely Ovechkin-dependent powerplay
from 2017-2018 shows a heavy asymmetry, with a big hotspot from his usual "spot". Crucially,
the expected goal values are computed *assuming league average shooting and goaltending talent*.
The xG value here of 6.48, a little **worse** than league average. In point of
fact very nearly all of the shots in the left-circle blob are taken by Ovechkin himself, one of the
best shooters in the league, which explains why the Washington observed goal rates considerably
exceed the computed xG.

Finally, I also make charts for the shots *allowed* by a team while short-handed, with the same
colourscheme as the power-play offence charts above: orange for "more than average", purple
for "less than average". Here, New Jersey's penalty-kill from 2017-2018 allowed fewer shots than the
league-average penalty-kill did from immediately in front of their net, with a small increase in shots
in the high slot.