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One of the reasons we love sports as much as we do is 
because of the great debates it can spawn. I came across a classic hockey 
debate the other day in relation to <a href="http://stats.nhlnumbers.com/player_stats/278-miller-ryan">Ryan Miller</a>. “Of course Miller didn’t post 
elite numbers last year. He played for Buffalo!”   
As I was thinking about this, it struck me that while we 
have made huge strides in advanced statistics overall, we haven’t made much 
progress in terms of quantifying the impact of defense and separating that 
contribution to statistics for the goalie themselves. We can tell the 
percentage of shots a goalie save (SV%), but we have little to no insight into 
the difficulty level of the shots themselves.  
From a quantitative standpoint, we understand the game 
better now than ever before. We understand shot attempt differential, which we 
know we can be used to approximate possession. We call this corsi, and we understand this to be a 
key component of overall team success. We can also begin to isolate the impact a player has 
on their team’s shot attempt differential, which is a solid representation of 
strong two way play. 
However, there is one factor that detractors of advanced 
stats have always struggled with. Detractors have long protested that not all 
shots have an equal chance of going in, and they’re right. Is it possible to adjust save-percentage to reflect easier or more difficult workload? Find out after the jump. 

In the 2013-14 season, goalies league wide faced 73,614 
shots. Using Greg Sinclair’s immensely useful <a href="http://somekindofninja.com/nhl/">website </a>we 
can break shot attempts down by the distance the shot was taken from the net. 
As you would expect, shots taken from within 10 feet, such as rebounds, 
redirections, cross crease feeds, and other scoring chances, have the highest shooting percentage at 19%. 
This shooting percentage is over 4.5 times higher than shots taken outside 30 
feet, but shots from here are significantly more rare (only 7% of total shots).
Table 1
<img loading="lazy" decoding="async" src="//thenationnetwork.s3.amazonaws.com/uploads/images/b4/2f/article_b42f7f53-1c1a-4a55-87fb-19328dcca183.png" alt="adj sv% table 1" height="134" width="600"> 
This confirms something we’ve always known: not only is the 
goal of defenses to limit shot attempts (which we can measure with corsi), but 
perhaps just as importantly, limit the chances the other team gets in high 
percentage areas. Based on the information above, a shot from inside 10 feet 
from the goal is almost five times as likely of finding the twine than a shot 
from 30 feet out. Naturally, some defensemen do a better job of clearing the 
puck from high percentage areas than others, and we can use this data to 
calculate how much of a goalies save percentage is the result of having a better 
or worse defense in front of them. 
Here is the distribution of shots faced for each goalie who 
played over 1,000 minutes in 2013-14: 

Table 2
<img loading="lazy" decoding="async" src="//thenationnetwork.s3.amazonaws.com/uploads/images/bb/c2/article_bbc2a02a-ed26-4495-b195-87a9c990505f.png" alt="adj sv% table 2" height="769" width="600"> 
My next step was to calculate each goalie’s save percentage 
for each of the four distances: 

Table 3
<img loading="lazy" decoding="async" src="//thenationnetwork.s3.amazonaws.com/uploads/images/20/52/article_20521309-205f-4b11-b3bb-a69c41cafa7a.png" alt="adj sv% table 3" height="893" width="600">
Then in order to isolate the impact of team defense, I 
applied the redistributed the total shots faced by each goalie by the average 
shot distribution at each of the four distances, then used the goalies actual 
save percentage at each distance to calculate an adjusted save percentage 
(excluding empty nets and shootouts): 

Table 4
<img loading="lazy" decoding="async" src="//thenationnetwork.s3.amazonaws.com/uploads/images/40/01/article_4001e4a5-2782-423f-bcd8-c0148538a9fe.png" alt="adj sv% table 4" height="465" width="600"> 
Now before I get into discussing the result, there are obviously limitations of this approach which are worth noting. Using the four distance buckets obviously runs the risk of overly aggregating shots that are dissimilar. For example, a goal mouth rebound from two feet out likely has a much higher chance of going in than a shot from nine feet out where the goalie has position. In the future, as we track more variables such as whether the shot is coming of a rebound or a one-timer, or even shot velocity, I could envision a much more complex version of this approach. There are also issues with inconsistencies in today&#8217;s data gathering when it comes to shot distance data from one arena to the next. In short, this isn&#8217;t perfect by any means. 
Detractions aside, I think the data above provides an interesting glimpse into the varying 
levels of difficulty each goalie faced. Lack, Miller, and Luongo, all faced a 
lower proportion of shots in the 0-10 foot and 10-20 foot categories than the 
league average, meaning that the defenses in front of them did a better than 
average job of limiting the amount of high percentage shots they faced from in 
close.
A number of other interesting data points jump off the page. 
For instance, Steve Mason enjoyed a renaissance season in Philadelphia last 
year, but when you adjust for shot distribution you find that his adj SV% at 
.909 was actually right in line with his career SV% of .907. Sorry Philly fans, 
you still may not have solved your goalie problems.
On the other end of the spectrum, Jonas Hiller faced an 
incredibly difficult shot distribution. When adjusting for shot distribution he 
went from a SV% of .911 to an adjusted SV% of .918, which was slightly above 
his career SV% of .916. He signed with Calgary for $4.5M per year, which makes 
me wonder how much more he could have gotten if teams realized just how much 
the Duck’s defense hung him out to dry last season.

In the summer of analytics, goaltending has been noticeably absent from the discussions of major areas of advancement in our understanding of the sport we love. While the above approach is somewhat rudimentary based on the limitations of the data we currently have to work with, it very well may turn out to be an area of interest going forward.

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</div>One of the reasons we love sports as much as we do is
because of the great debates it can spawn. I came across a classic hockey
debate the other day in relation to <a href="http://stats.nhlnumbers.com/player_stats/278-miller-ryan">Ryan Miller</a>. “Of course Miller didn’t post
elite numbers last year. He played for Buffalo!”   As I was thinking about this, it struck me that while we
have made huge strides in advanced statistics overall, we haven’t made much
progress in terms of quantifying the impact of defense and separating that
contribution to statistics for the goalie themselves. We can tell the
percentage of shots a goalie save (SV%), but we have little to no insight into
the difficulty level of the shots themselves.  From a quantitative standpoint, we understand the game
better now than ever before. We understand shot attempt differential, which we
know we can be used to approximate possession. We call this corsi, and we understand this to be a
key component of overall team success. We can also begin to isolate the impact a player has
on their team’s shot attempt differential, which is a solid representation of
strong two way play. However, there is one factor that detractors of advanced
stats have always struggled with. Detractors have long protested that not all
shots have an equal chance of going in, and they’re right. Is it possible to adjust save-percentage to reflect easier or more difficult workload? Find out after the jump. 
In the 2013-14 season, goalies league wide faced 73,614
shots. Using Greg Sinclair’s immensely useful <a href="http://somekindofninja.com/nhl/">website </a>we
can break shot attempts down by the distance the shot was taken from the net.
As you would expect, shots taken from within 10 feet, such as rebounds,
redirections, cross crease feeds, and other scoring chances, have the highest shooting percentage at 19%.
This shooting percentage is over 4.5 times higher than shots taken outside 30
feet, but shots from here are significantly more rare (only 7% of total shots).Table 1<img src="//thenationnetwork.s3.amazonaws.com/uploads/images/b4/2f/article_b42f7f53-1c1a-4a55-87fb-19328dcca183.png" alt="adj sv% table 1" height="134" width="600"> This confirms something we’ve always known: not only is the
goal of defenses to limit shot attempts (which we can measure with corsi), but
perhaps just as importantly, limit the chances the other team gets in high
percentage areas. Based on the information above, a shot from inside 10 feet
from the goal is almost five times as likely of finding the twine than a shot
from 30 feet out. Naturally, some defensemen do a better job of clearing the
puck from high percentage areas than others, and we can use this data to
calculate how much of a goalies save percentage is the result of having a better
or worse defense in front of them. Here is the distribution of shots faced for each goalie who
played over 1,000 minutes in 2013-14: 
Table 2<img src="//thenationnetwork.s3.amazonaws.com/uploads/images/bb/c2/article_bbc2a02a-ed26-4495-b195-87a9c990505f.png" alt="adj sv% table 2" height="769" width="600"> My next step was to calculate each goalie’s save percentage
for each of the four distances: 
Table 3<img src="//thenationnetwork.s3.amazonaws.com/uploads/images/20/52/article_20521309-205f-4b11-b3bb-a69c41cafa7a.png" alt="adj sv% table 3" height="893" width="600"> Then in order to isolate the impact of team defense, I
applied the redistributed the total shots faced by each goalie by the average
shot distribution at each of the four distances, then used the goalies actual
save percentage at each distance to calculate an adjusted save percentage
(excluding empty nets and shootouts): 
Table 4<img src="//thenationnetwork.s3.amazonaws.com/uploads/images/40/01/article_4001e4a5-2782-423f-bcd8-c0148538a9fe.png" alt="adj sv% table 4" height="465" width="600"> Now before I get into discussing the result, there are obviously limitations of this approach which are worth noting. Using the four distance buckets obviously runs the risk of overly aggregating shots that are dissimilar. For example, a goal mouth rebound from two feet out likely has a much higher chance of going in than a shot from nine feet out where the goalie has position. In the future, as we track more variables such as whether the shot is coming of a rebound or a one-timer, or even shot velocity, I could envision a much more complex version of this approach. There are also issues with inconsistencies in today's data gathering when it comes to shot distance data from one arena to the next. In short, this isn't perfect by any means. Detractions aside, I think the data above provides an interesting glimpse into the varying
levels of difficulty each goalie faced. Lack, Miller, and Luongo, all faced a
lower proportion of shots in the 0-10 foot and 10-20 foot categories than the
league average, meaning that the defenses in front of them did a better than
average job of limiting the amount of high percentage shots they faced from in
close. A number of other interesting data points jump off the page.
For instance, Steve Mason enjoyed a renaissance season in Philadelphia last
year, but when you adjust for shot distribution you find that his adj SV% at
.909 was actually right in line with his career SV% of .907. Sorry Philly fans,
you still may not have solved your goalie problems.On the other end of the spectrum, Jonas Hiller faced an
incredibly difficult shot distribution. When adjusting for shot distribution he
went from a SV% of .911 to an adjusted SV% of .918, which was slightly above
his career SV% of .916. He signed with Calgary for $4.5M per year, which makes
me wonder how much more he could have gotten if teams realized just how much
the Duck’s defense hung him out to dry last season.
In the summer of analytics, goaltending has been noticeably absent from the discussions of major areas of advancement in our understanding of the sport we love. While the above approach is somewhat rudimentary based on the limitations of the data we currently have to work with, it very well may turn out to be an area of interest going forward.

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