NHL Goaltending - Best Friend or Worst Enemy

“Goaltending is 75 percent of your hockey team, unless you don't have it. Then it's 100 percent.” – Harry Neale

Goaltending can make or break a hockey team. Every season there will be a handful of men between the pipes dragging their teams into the playoffs and typically just as many who will will cost their teams an invite to the big dance.

For those of you who are unaware, goalies can be wildly inconsistent. A goalies SV% tends to vary quite largely from year-to-year. Even better evaluators of true talent such as ESSV% or RoadSV% will rise and fall from year to year.

Ottawa Senators are possibly the most recent poster boy for the reality of goaltending inconsistency. The Senators have seen their goalies SV% plummet from an absurd .935 to a below-average .908. How could this happen? Did Anderson and Lehner suddenly forget how to play goalie? Probably not. What probably did happen however was a brutal form of regression. Essentially, Anderson and Lehner's stats came hurling back to earth. So how good are they really then? Are they .935% goalies? Probably not. Are they .908? Probably not that either. The answer most likely lies somewhere in the middle. 

In this analysis, I hope to show what the league would have looked like this year had some goalies not played as amazing as they did (ex. Varlamov) or not as poorly as they did (ex. Dubnyk). 

The method for my research came from the idea posted by Phil Birnbaum:

Roughly speaking, that means you can expect 25% of a goalie's difference from the mean to be repeated next year. Put another way, you have to regress the goalie 75% towards the mean.

Yes, that's not as much as you'd expect. By that calculation, if the average save percentage is .904, and goalie X comes in one season at .924, you'd expect next year he'd be at .909 -- one quarter of the distance between .904 and .924.

The basics of what I did was look at every NHL goalie who had faced about 900 shots (or about 35 games played), and regressed their save percentage 75% towards the mean. The reason I chose to do this and not just set everyone at league average is because I believe this is a better reflection of what a team should realistically expect from them their individual goalie.

I then calculated how many more goals you would expect a goalie to either surrender or save for the given season. Finally, I gave a team 1 point for every 3 more goals saved and vice-versa.

Here are my results...

Team	Official Points	Goalie Regression Points	+/- PTS	Original League Standings	Regressed League Standings	+/- Standings
WINNIPEG	84	89	5	22	17	5
EDMONTON	67	71	4	28	28	0
OTTAWA	88	90	2	19	15	4
NY ISLANDERS	79	81	2	26	24	2
FLORIDA	66	68	2	29	29	0
DETROIT	93	94	1	14	12	2
NEW JERSEY	88	89	1	19	17	2
NASHVILLE	88	89	1	19	17	2
ANAHEIM	116	117	1	2	1	1
SAN JOSE	111	112	1	4	3	1
CALGARY	77	78	1	27	27	0
NY RANGERS	96	96	0	12	10	2
WASHINGTON	90	90	0	17	15	2
LOS ANGELES	100	100	0	9	8	1
PHOENIX	89	89	0	18	17	1
BOSTON	117	117	0	1	1	0
MINNESOTA	98	97	-1	11	9	2
PITTSBURGH	109	108	-1	6	5	1
CHICAGO	107	106	-1	7	6	1
VANCOUVER	83	82	-1	24	23	1
ST LOUIS	111	110	-1	4	4	0
PHILADELPHIA	94	93	-1	13	14	-1
DALLAS	91	89	-2	16	17	-1
BUFFALO	52	49	-3	30	30	0
TORONTO	84	81	-3	22	24	-2
CAROLINA	83	80	-3	24	26	-2
COLUMBUS	93	89	-4	14	17	-3
TAMPA BAY	101	96	-5	8	10	-2
MONTREAL	100	94	-6	9	12	-3
COLORADO	112	105	-7	3	7	-4

As you can see, Varlamov and Price both had huge season for Colorado and Montreal respectively. These new league standings have Montreal sliding down to a wildcard spot and Colorado sliding down to 3rd in the Central. While Winnipeg once again suffered a severe case of Pavelectricity keep them out of a potential 4 way tie for the final wildcard spot in the West.

The results show what I think many would agree with, some teams benefited highly from their goalies while others really suffered. While this helps us more or less neutralize the effects of a particularly strong or weak season by a particular goalie it doesn't totally level the playing field, keeping individuality live and well.

***Notes***

• I only chose goalies fitting my 900 shots or about 35 games played just to eliminate as many small sample sizes as possible
• I didn't run this regression for Henrik Lundqvist and Tuukka Rask, simply based on the fact that these two have yet to post non-elite numbers so I felt it unfair to hurt either of them on the basis that we shouldn't expect much regression at all
• Minnesota didn't have any goalies who fit my minimum requirement therefore I simply ran the regression for their team average