We’re used to looking at what goalies do in terms of only two outcomes: did the puck go in or not? Every save is a victory and every goal a defeat. That’s too simplistic because there are things that a goalie can do to either prevent additional shots once a shot has been taken or to punish offensive teams for taking shots. It may even be possible to reduce the quality of shots before they are taken.
In this article, we will consider the first two cases: mitigation and counterpasses.
Preliminaries
From the Sportlogiq event data of ~4000 events/game (passes, receptions,
loose puck recoveries (LPRs), zone entries, etc.) we derive ~450 possessions/game based on the markers 'isPossessionEvent' and 'isDefensiveEvent' already in the event data. If a team performs a possession event, that team has possession; if a team performs a defensive event, we know that the other team has possession. Then we look at the first and last events from each possession, and mark each one as a transition. We're specifically interested in 'shottoX' transitions.
We count a defensive rebound as a shottolpr transition  where one team ended their possession with a shot, and the other team started their possession with a loose puck recovery (LPR).
LPRs count as possession events. At the recovery event is when we start the clock on possession length.
Whistles. Any stoppage in play is a break in possession. We count shottofaceoff transitions as goalie stoppages. (goaltofaceoff is counted separately)
We count offensive rebounds as any rebound that keeps possession in the offense. So that's any time there are multiple shots for the offense in a possession, and any time that a shot happened but was not the final event of a possession.Also apart from either rebound is the counterpass, which is where the goalie definitively gains control of the puck and passes it to a teammate. (i.e. shottopass transitions between possessions)
The Five Outcomes
First, let’s consider some preliminaries about shooting.
In the 3,000 games of the 201718, 201819, and start of 201920 regular seasons in regulation time, teams experienced the following (including empty netters, excluding blocked and missed shots)
Table 1: Shots, shot attempts, and goals.

Home

Away

Total

Shots on Net / game

32.82

31.68

64.50

Shot Attempts / game

63.49

61.20

124.69

Goals / game

3.09

2.81

5.90

Goals / Shot

9.41%

8.87%

9.15%

Goals / Attempt

4.87%

4.59%

4.73%

Shots / Attempt

51.69%

51.77%

51.72%

We know that about 52% of shot attempts become actual shots on net,
whereas the rest are either deflected by other players or misses (as in, they wouldn’t
have been goals even without a goalie). We also know that 9.15% of shots score,
but otherwise we’ve simplified the other 90.85% of shots as ‘not goals’. The
event data also has records of things like loosepuckrecoveries (lpr), passes,
and zone entries which we use to break down those saves into four categories
based on what happened after the save. Loosely best to worst, these are:
 counterpass: The goalie immediately takes control of the puck and passes it to a teammate.
 Stoppage: The goalie immediately covers the puck or otherwise creates a stoppage in play.
 Rebound to defense: The goalie never controls the puck, but deflects it such that offensive possession ends.
 Rebound to offense: The goalie never controls the puck, and it is deflected back into offensive possession.
Using these goalie outcomes, there are three natural ways to count "rebound share" without including goals.
Method 1: (def rebounds) / (def rebounds + off rebounds)
Method 2: (def rebounds + counterpasses) / (def rebounds + off rebounds + counterpasses)
Method 3: (def rebounds + counterpasses + 0.5*stoppages) / (def rebounds + off rebounds + counterpasses + stoppages)
Method 1 only considers shots for which the puck was never considered to be in possession of the goalie, and takes the proportion of those that were recovered by teammates.
Method 2 also includes counterpasses as if they were good rebounds. The outcome of counterpass is similar to the defensive rebound in that the defending team receives the puck, the goalie has a more deliberate contribution as well as tactical choice of the receiving player.
Method 3 takes Method 2 and also includes stoppages as half a ‘good’ rebound and half a ‘bad’ rebound, based on the assumption that each faceoff is a 5050 coin toss. Even for cases where one team is significantly better or worse at faceoffs than the other, that ability shouldn’t be attributed to the goalie when they stop play.
For the 64 goalies that have faced at least 1000 shots in our data the measures from all three of these methods are strongly correlated with each other (Pearson r = 0.934 to 0.999), and all three have negligible correlation with save percentage (Pearson r = 0.060 to 0.068). Unless otherwise specified, rebound share is calculated using Method 3.
Examining shot outcomes as five possibilities instead of two gives us the following table. Notice that we’re not considering shot attempts throughout most of what follows, that’s because we’re trying to focus on events that can be directly attributed to goaltending.
Table 2: The five outcomes, home and away
Per Game \ Goalie

Home

Away

Total

% of Total

Shots on Net

31.68

32.82

64.50

100%

counterpasses

1.66

1.57

3.24

5.01%

Stoppages

8.90

9.13

18.04

27.97%

Rebound Defence

8.58

8.84

17.41

26.99%

Rebound Offence

9.72

10.19

19.90

30.85%

Goals

2.81

3.09

5.90

9.15%

What do we learn from this?
* The home team faces 3.5% fewer shots than the away team. Similarly, they produce 2.5% fewer stoppages and 3.9% fewer rebounds.
* The home team produces 5.7% more counterpasses, or about 9% more counterpasses per shot, than the visitors. That can be explained in breakdown by manpower situation in the next table.
* The home team also scores 10% more goals than the visitors, but that’s covered in plenty of places elsewhere. Why not 3.5% more? Probably because they’re ahead more, and teams that are ahead take fewer, but higher quality shots. That part of shooting behaviour is covered in Hockey Abstract 2014.
* Counterpasses are rare. They’re rarer than goals, and a typical netminder manages to pull off an average of about 1.6 of them per game.
* Ignoring counterpasses, rebounds are split 5347 in favour of the attacking team. That’s not dominance, but it’s not a statistical artifact either; there are more than 40,000 rebounds per season.
* Assuming faceoffs are 50/50, the average shot rewards the attacking team with 9.15% of a goal and costs them (27.97/2 + 9.15/2 + 26.99 + 5.01) = 50.56% of a possession.
What we did NOT learn is the context in which goalie actions like counterpasses and rebounds happen.
Context could be numerical advantage or disadvantage (i.e. the manpower situation), score situation, or distance to the net. We’ll slice the data each of those three ways in order. Other parts of context we’ll ignore for this analysis include whether attacks are part of a top line, whether there was a defender near enough to apply pressure, and whether Carey Price is using his Jedi powers tonight.

Some say Price can see around corners and into the future. 
Numerical advantage or disadvantage
Let’s break down rebounds and counterpasses by numerical advantage.
Intuitively, we might expect that teams on the power play should be able to collect a greater share of the rebounds because they have more players to pick up the puck after a shot. We might also expect the defending team to collect more of their rebounds because shorthanded teams on the attack is a rarity; it usually means something has gone wrong like a breakaway, but Table 3 tells a different story.
Table 3: The five outcomes by number of skaters
Defender Situation

Neutral

PKill

PPlay

Total Shots

158766

28628

5972

counterpasses

7372 (4.64% of shots)

426 (1.49%)

1907 (31.93%)

Stoppages

46348 (29.19%)

6897 (24.09%)

841 (14.08%)

Goals

13320 (8.39%)

3796 (13.26%)

564 (9.44%)





Total Rebounds

91706

17510

2659

Attacker Won

47511 (51.81% of rebounds)

11227 (64.12%)

934 (35.13%)

Defender Won

44195 (48.19%)

6283 (35.88%)

1725 (64.87%)

When the defending team is on the powerplay and somehow still needs to rebound, it goes to a defender almost twothirds of the time. We might remember the shorthanded breakaways more, but most shots are part of a timekilling strategy to make a line change.
And what about counterpasses?
When the defending team is at even strength, 4.6% of shots get converted to counterpasses. That changes dramatically depending on the numerical situation. When the goalie is on the penalty kill, that drops to a third the neutral rate, and when the goalie is on the power play, the rate increases to seven times the neutral rate. That reveals some of the nature of counterpasses: a lot of them are happening in response to the attacking team trying to ice the puck. Technically an icing still counts as a shot if it would have become a goal without goalie intervention, so a lot of these ‘counterpasses’ are merely lowpressure passes back to a skater during a line change.
Next, consider those 1.6 counterpasses per game. If every goalie was equally good at producing counterpasses (and faced an equal number of shots per game) then the number of counters per game should follow a Poisson distribution. Does it?
According to Table 4, the distribution table of counterpasses, there’s a clearly detectable difference between the uniformgoalies ideal of a Poisson distribution. On its own, that only means that goalies, are not clones of each other. Digging deeper, there are fewer 2counterpass games and more 0, 5, and 6counterpass games than clonegoalies would produce. In other words, there are fewer games in the middle and more at the extremes than simple statistics can explain.
Table 4: Table of distribution of number of counterpasses.
counterpasses

Number of Games

% of total

Expected Poisson

0

1064

21.87

1016.6

1

1609

32.17

1619.8

2

1204

24.07

1290.5

3

649

12.97

685.4

4

282

5.64

273.0

5

118

2.36

87.0

6

37

0.74

23.1

7

7

0.14

5.3

8

1

0.02

1.0

9 (or more)

1

0.02

0.2

ChiSquared: 37.54 on df = 8, p < .00001 (Setting the cutoff at 9+)
ChiSquared: 33.39 on df = 5, p < .00001 (Setting the cutoff at 6+)
Split by goalie
That’s a hint that some goalies are genuinely better at punishing a shot with a quick pass to a teammate than others. Who might these Frank Castles between the pipes be? This next table gives the 10 best and worst counterpassers among those 64 goalies that have faced 1000 shots in the study period.
Table 5: Best and worst counterpassers.
Goalie

Shots Faced

counterpass %

Goal % / SVG

Mackenzie Blackwood

1119

7.33

8.85 / .9115

Jimmy Howard

3662

6.20

8.36 / .9164

Anders Nilsson

1820

6.10

8.57 / .9143

Craig Anderson

3558

6.07

9.58 / .9042

Aaron Dell

1978

5.97

9.76 / .9024

...

...

...

...

Corey Crawford

2827

3.68

8.95 / .9105

Laurent Brossoit

1200

3.42

9.75 / .9025

Cam Ward

2488

3.30

9.24 / .9074

Scott Darling

1675

3.16

6.93 / .9307

Anton Forsberg

1116

3.14

8.06 / .9194

Table 5: Best and worst counterpassers.
Let’s examine rebound share (using Method 3) and break down the results by individual goalie. In the 3 seasons we’re using, there are 64 goalies that have faced 1000 or more shots, which we’ll use an arbitrary cutoff finding the best and worst rebounders to avoid goalies that were only in net a handful of times. What can we say about these results?
Can we say anything about this? Specifically, are the differences between players’ rebound share large enough not to be noise? The weighted (by shots faced) mean of rebound share among these 64 goalies is .5092. The next table shows the number of standard errors (the zscores) above or below that global mean each goalie is, using the standard error estimated from their rebound share and shots faced.
Using Method 2, we see a few goalies that are more than 4 standard errors above the global mean, which we wouldn’t otherwise expect to see in a dataset this small. Method 3 tends to compress rebound share towards 50% because it assigns half a defensive rebound and half an offensive rebound to each stoppage in play based on the assumption that stoppages matter and that the average team wins a faceoff 50% of the time. Using Method 3, these differences are no longer unusual for randomly generated rebound shares.
Table 6: Best and worst rebounders.
Goalie

Shots Faced

Rebound Share Method 2

Rebound Share Method 3

SE(share)

Mackenzie Blackwood

1119

.553

.538

.0149

Jacob Markstrom

4161

.546

.532

.0077

Jimmy Howard

3662

.545

.531

.0082

Anders Nilsson

1820

.534

.525

.0117

Casey DeSmith

1366

.534

.524

.0135

...

...


...


Carter Hutton

2919

.488

.492

.0092

Jordan Binnington

1677

.488

.492

.0122

Robin Lehner

3160

.486

.491

.0089

Martin Jones

4469

.485

.489

.0074

Laurent Brossoit

1200

.448

.465

.0144

Markstrom and a few others are legitimately better than par at punishing shots against him. There’s only one goalie that’s notably worse than the average in rebound share. On the surface, it doesn’t look like there isn’t much of a range from the best and worst in terms of rebound share. There certainly isn’t enough of a difference to make an attacker think twice about shooting against one goalie but not another. If you consider lossofpossession to be the ‘cost’ of a shot, then it’s 1.1 times as ‘expensive’ to shoot against Markstrom or Howard as it is against Lehner or Jones.
We have a measure of the ‘cost’ of a shot. In save percentage we already had the ‘benefit’ of a shot. We can now put these together to get a costbenefit tradeoff: how many turnovers is it going to cost to get a goal against a certain goalie?
We compute
shots/goal = 1 / (1  save percentage)
And
turnovers/goal = (rebound share) * (shots / goal)
and show the best and worst 5 in our dataset in the following table:
Table 7: Most and least 'punishing' goalies.
Goalie

Rebound Share Method 2

Rebound Share Method 3

Save%

Shots / Goal

Turnovers / Goal
RB Method 2

Turnovers / Goal
RB Method 3

Scott Darling

.513

.509

.931

14.43

7.41

7.34

Carter Hutton

.488

.492

.926

13.57

6.62

6.67

Jack Campbell

.516

.511

.920

12.48

6.45

6.38

Carey Price

.510

.507

.920

12.53

6.40

6.36

Jimmy Howard

.545

.531

.916

11.96

6.52

6.35

...

...

...

...

...

...

...

Laurent Brossoit

.448

.465

.903

10.25

4.60

4.77

Frederik Andersen

.513

.509

.892

9.29

4.76

4.73

Braden Holtby

.500

.500

.894

9.42

4.72

4.71

Andrei Vasilevskiy

.510

.507

.889

8.97

4.58

4.55

Louis Domingue

.517

.512

.886

8.80

4.54

4.50

Unsurprisingly, save percentage (or equivalently, shots per goal) is the dominant factor in determining turnovers per goal. Save percentage matters so much that Carter Hutton, who was near the bottom of rebound share, is near the top for turnovers per goal. Compared to save percentage, rebound share is like a tiebreaker between goalies with otherwise very similar saving ability. Jimmy Howard has a slightly lower save percentage than Jack Campbell and Carey Price, but manages to match them in turnovers per goal by having a very high rebound share.
The numbers in Table 7 may look a bit unimpressive given there are 200 possessions per side per game (if you include the really short ones), but these are turnovers that are directly attributable to the goalie: defensive rebounds, counterpasses, and (half of the) stoppages. They’re also the lion’s share of the most important turnovers during a game.
A graph between save percentage and turnovers per goal demonstrates how important save percentage is to this composite score.

Figure 1 
As an aside, note how much better shots/goal accentuates the differences between goalies than save percentage does. In this table it’s clear that Scott Darling is almost twice as difficult to get past than Louis Domingue, whereas save percentage reveals a more arcane difference of .045.
While rebound share is much less important than save percentage, it shouldn’t just be ignored. It describes a completely different aspect of goalie performance than save percentage, as shown in Figure 1. Considering both save percentage and rebound share together gives more information that save percentage alone.

Figure 2 
How do these two measures compare to
each other? In the following scatterplot, each dot is a goalie in our set,
where the X axis is save percentage and the Y axis is counterpass %.
It’s not clear what we’re hoping to see
here. A strong positive correlation would show that counterpass is closely
related to save percentage, and thus also be an indicator of goalie quality,
however redundant.
Instead, we see almost no
correlation at all (Pearson r = 0.034). Independence means that CA% and SV% are
two independent facets of goalie behaviour. At worst it suggests that CA% is a
useless curiosity stat, but at best it implies that CA% is a new dimension to
evaluate netminders, like pitch framing ability was for MLB catchers.

Figure 3 
Maybe individual differences are too
noisy, let’s instead group goalies by number of games of played during the
study period. There are 64 goalies with 1000 or more shots faced, so we'll
split these in half. Since we're no longer looking at individual goalies, we'll
expand our scope to everyone who played 10 or more games between the pipes.

Rank 132
GP

Rank 3364 GP

Rank 6590 GP

Number of games
(range)

79151

3376

1030

Number of
games (mean)

115.4

54.9

17.2

Shots Faced / game

32.3

32.2

32.2

counterpasses /
game

1.60

1.60

1.69

Goals Allowed /
game

2.94

2.96

2.89

Save Percentage (mean)

.909

.908

.910

Rebound share (Method
3)

.506

.507

.504

The uniformity
is surprizing. What isn't apparent from the table is how the rank 6590 goalies
in games played are twice as variable in goals allowed per game.
Split by Distance
As long as we’re looking at saves
through a fiveoutcome lens, we should also recreate a classic analysis. The
figure below is the smoothed percentage of shots that become goals from each of
the locations in the offensive zone. Basically, the shorter of a straight line there
is to the net, the better.
The horizontal and vertical dashed red
lines represent the mean shooting percentage and mean distance to the net of
each attempt, respectively.

Figure 4 
If a typical goalie lets in 810% of
shots, then why is the success chance of a shot attempt around 6%? Because a
large portion of shot attempts are either blocked by another skater, or they
miss the net and wouldn’t be goals even if there was no goalie. Figure 5 shows the proportion of shot attempts that never reach the goalie or the
net.

Figure 5 
How frequent are the other outcomes at different shooting distances?
Counterpasses are rare at any distance, and the relationship between them and
shooting distance is noisy. In Figure 6 the dashed lines are the proportion
of shots that get counterpassed and the mean distance to the net.

Figure 6 
Likewise, in Figure 7, which shows stoppage
probability over distance, the horizontal dashed line is the marginal
probability of shot attempt resulting in a stoppage of play at any distance. As
a portion of shot attempts, goalieinduced stoppages in play are moreorless
constant, but as a portion of actual shots, that proportion climbs from 20% at
pointblanc to about 50% at 60 feet (20 metres).

Figure 7 
There are fewer rebounds as you go because ever fewer of the shots ever
reach the goalie in the first place. If we only look atshots (Figure 9) and not shot attempts (Figure 8), the picture is different. Notice that defensive rebounds are rarer
than offensive ones because we’re not considering other ways the defending team
gets the puck like counterpasses and faceoffs.

Figure 8 

Figure 9 
Combining these two, how does rebound
share (Method 1) vary by distance? There’s a lot of noise and no obvious trend, as shown in Figure 10.

Figure 10 
Seasontoseason
consistency
Rebound share may be noisy, but is it
at least consistent for a given goalie from season to season? According to the
next figure, no. This implies that the stat isn’t just tied to the goalie but
to the defense as a whole. The Pearson correlation between the 1718 and 1819
seasons and the 1819 and 1920 seasons are 0.079 and 0.256 respectively, as shown in Figure 11. The
equivalent Spearman correlations, which are more robust to extreme values, are
0.003 and 0.189 respectively. Either way, there’s no clear carryover from
season to season.

Figure 11 
This isn't just a weakness of rebound
share, it’s a feature of goalie evaluation. Save percentage suffers from the
same inconsistencies, as shown in Figure 12. For save percentage over the same pairs of seasons, the
Pearson correlations were 0.001 and 0.166 respectively. The Spearman
correlations were 0.028 and 0.244 respectively.

Figure 12 
No comments:
Post a comment