Data: NHL Goaltender Schedule Strength, 2018-19 regular season and playoffs

[Doctor No]

This is a standard feature on my site; although this represents the regular season, I had to wait until the Stanley Cup concluded because the power ratings (and therefore the average strengths) change during the playoffs.

These are for the 2018-19 NHL regular season.

AVGOSTR - average opponent strength, measured in goals/game. So someone like Jordan Binnington faced an average opponent 0.03 goals/game below average (basically an average opponent). These are weighted by minutes played, and do incorporate home ice advantage (it is harder to play on the road than at home).

AVGOSPC - average opponent save percentage. This is what an average NHL goaltender's save percentage would be, facing the slate of opponents that the goaltender faced. (An easier way of looking at it is that it's 1 minus the average shooting percentage of the goaltender's opponents). This does not incorporate home ice advantage.

TOPPCT - percentage of games (shots weighted) facing the top quartile of opponents. An average goaltender plays 25% of his games against the top quartile of opponents. For 2018-19, these teams are Tampa Bay, Boston, Calgary, Columbus, Toronto, NY Islanders, St. Louis, and Winnipeg (75% - since there are 31 teams, there are 7.75 "top quartile" teams).

BOTPCT - percentage of games (shots weighted) facing the bottom quartile of opponents. For 2018-19, these teams are Los Angeles, Ottawa, Anaheim, New Jersey, Detroit, NY Rangers, Buffalo, and Edmonton (75%).

HOMEPCT - percentage of games (shots weighted) played at home. An average goaltender plays 50% of games at home. Backups usually play less than 50% at home.

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[Doctor No]

NAME	TEAM	AVGOSTR	AVGOSPC	TOPPCT	BOTPCT	HOMEPCT
Bernier Jonathan	DET	0.13	0.910	30%	14%	31%
Binnington Jordan	STL	(0.03)	0.910	22%	25%	55%
Bishop Ben	DAL	(0.07)	0.911	26%	26%	59%
Blackwood Mackenzie	NJD	0.08	0.907	32%	14%	46%
Bobrovsky Sergei	CBJ	(0.01)	0.909	23%	24%	51%
Bow Landon	DAL	0.79	0.900	47%	0%	0%
Boyle Kevin	ANA	(0.15)	0.912	21%	16%	42%
Allen Jake	STL	0.01	0.909	19%	23%	41%
Andersen Frederik	TOR	0.04	0.908	23%	25%	49%
Anderson Craig	OTT	0.06	0.908	31%	24%	55%
Bachman Richard	VAN	(0.15)	0.922	0%	0%	0%
Brossoit Laurent	WPG	(0.19)	0.913	9%	37%	42%
Budaj Peter	LOS	(0.13)	0.902	34%	42%	58%
Campbell Jack	LOS	0.05	0.908	30%	23%	43%
Comrie Eric	WPG	(0.15)	0.922	0%	0%	0%
Condon Mike	OTT	0.70	0.912	79%	0%	0%
Copley Pheonix	WSH	(0.14)	0.910	20%	39%	41%
Crawford Corey	CHI	(0.02)	0.910	24%	22%	50%
Daccord Joey	OTT	(0.37)	0.920	0%	100%	0%
Darling Scott	CAR	0.08	0.907	37%	13%	64%
Delia Collin	CHI	0.10	0.909	24%	10%	52%
Dell Aaron	SJS	0.01	0.909	31%	24%	31%
Demko Thatcher	VAN	(0.13)	0.911	23%	32%	54%
DeSmith Casey	PIT	(0.10)	0.910	22%	28%	55%
DiPietro Michael	VAN	0.06	0.897	0%	0%	100%
Domingue Louis	TBL	(0.13)	0.910	12%	38%	38%
Dubnyk Devan	MIN	(0.00)	0.910	22%	23%	51%
Elliott Brian	PHI	0.02	0.908	26%	25%	53%
Fleury Marc-Andre	VGK	(0.10)	0.910	22%	29%	54%
Francouz Pavel	COL	0.03	0.912	0%	0%	52%
Fulcher Kaden	DET	(0.67)	0.920	0%	100%	100%
Georgiev Alexander	NYR	0.17	0.907	40%	15%	43%
Gibson Christopher	NYI	(0.07)	0.909	0%	0%	50%
Gibson John	ANA	(0.03)	0.911	19%	21%	57%
Greiss Thomas	NYI	0.01	0.909	20%	26%	54%
Grubauer Philipp	COL	(0.05)	0.912	18%	22%	48%
Halak Jaroslav	BOS	(0.03)	0.909	17%	24%	48%
Hart Carter	PHI	(0.01)	0.909	31%	24%	70%
Hellebuyck Connor	WPG	0.04	0.909	25%	18%	53%
Hill Aden	ARI	(0.00)	0.907	19%	33%	45%
Hogberg Marcus	OTT	0.07	0.909	29%	0%	71%
Holtby Braden	WSH	0.04	0.909	30%	23%	54%
Howard Jimmy	DET	(0.02)	0.907	28%	29%	61%
Hutchinson Michael	FLO	(0.31)	0.905	0%	52%	31%
Hutchinson Michael	TOR	(0.15)	0.916	20%	20%	80%
Hutton Carter	BUF	0.10	0.906	30%	18%	51%
Jarry Tristan	PIT	0.64	0.909	51%	0%	0%
Johnson Chad	ANA	0.17	0.906	36%	23%	3%
Johnson Chad	STL	(0.12)	0.911	12%	12%	76%
Jones Martin	SJS	(0.07)	0.911	20%	28%	57%
Khudobin Anton	DAL	0.04	0.908	22%	18%	41%
Kinkaid Keith	NJD	0.05	0.907	24%	19%	54%
Korpisalo Joonas	CBJ	(0.03)	0.908	21%	37%	48%
Koskinen Mikko	EDM	0.01	0.910	23%	25%	49%
Kuemper Darcy	ARI	(0.05)	0.910	23%	25%	51%
Lagace Maxime	VGK	0.41	0.920	0%	0%	0%
Lehner Robin	NYI	(0.04)	0.908	25%	29%	47%
Lindgren Charlie	MON	0.22	0.900	100%	0%	100%
Lundqvist Henrik	NYR	(0.06)	0.910	20%	27%	54%
Luongo Roberto	FLO	0.10	0.907	33%	26%	44%
Lyon Alex	PHI	(0.25)	0.917	0%	28%	0%
Markstrom Jacob	VAN	(0.04)	0.909	26%	29%	53%
McElhinney Curtis	CAR	(0.04)	0.909	27%	30%	54%
McKenna Mike	OTT	0.09	0.910	27%	16%	33%
McKenna Mike	PHI	0.47	0.896	0%	0%	0%
Miller Ryan	ANA	0.06	0.906	32%	21%	48%
Miska Hunter	ARI	(0.40)	0.910	0%	100%	0%
Montembeault Samuel	FLO	(0.04)	0.914	21%	30%	51%
Mrazek Petr	CAR	0.00	0.907	27%	29%	45%
Murray Matt	PIT	0.02	0.908	30%	28%	49%
Nedeljkovic Alex	CAR	(0.21)	0.915	0%	0%	0%
Neuvirth Michal	PHI	0.32	0.906	39%	18%	32%
Niemi Antti	MON	(0.08)	0.911	13%	36%	38%
Nilsson Anders	OTT	(0.05)	0.910	21%	28%	45%
Nilsson Anders	VAN	0.25	0.905	23%	8%	33%
Pasquale Edward	TBL	0.24	0.912	33%	36%	0%
Petersen Calvin	LOS	0.03	0.910	25%	17%	31%
Pickard Calvin	ARI	0.27	0.907	36%	5%	26%
Pickard Calvin	PHI	0.03	0.904	29%	35%	37%
Price Carey	MON	0.02	0.907	32%	27%	52%
Quick Jonathan	LOS	(0.01)	0.910	19%	20%	58%
Raanta Antti	ARI	(0.07)	0.909	22%	35%	59%
Rask Tuukka	BOS	(0.03)	0.908	30%	33%	52%
Reimer James	FLO	(0.04)	0.908	28%	29%	59%
Rinne Pekka	NSH	(0.04)	0.909	26%	26%	54%
Rittich David	CGY	(0.06)	0.910	17%	29%	46%
Saros Juuse	NSH	0.06	0.910	24%	15%	42%
Schneider Cory	NJD	(0.04)	0.909	29%	36%	47%
Smith Mike	CGY	(0.07)	0.911	20%	22%	55%
Sparks Garret	TOR	(0.13)	0.909	26%	44%	45%
Stalock Alex	MIN	0.09	0.909	38%	21%	48%
Stolarz Anthony	EDM	0.05	0.905	54%	22%	71%
Stolarz Anthony	PHI	(0.05)	0.906	34%	40%	32%
Subban Malcolm	VGK	0.07	0.907	28%	22%	42%
Talbot Cameron	EDM	(0.02)	0.909	20%	17%	50%
Talbot Cameron	PHI	0.10	0.916	0%	28%	16%
Ullmark Linus	BUF	(0.07)	0.910	27%	31%	49%
Varlamov Semyon	COL	0.01	0.908	30%	23%	51%
Vasilevski Andrei	TBL	(0.02)	0.909	29%	24%	58%
Ward Cam	CHI	0.01	0.910	29%	28%	50%

[TBODY] [/TBODY]

 

[Doctor No]

I left these alphabetical to make it easier to find your goaltenders.

Here are the weights used to composite the AVGOSTR metric:

OPPONENT	HOME	AWAY
Tampa Bay	0.896	1.199
Boston	0.583	0.886
Calgary	0.446	0.749
Columbus	0.264	0.567
Toronto	0.223	0.527
NY Islanders	0.215	0.518
St. Louis	0.182	0.485
Winnipeg	0.172	0.476
Washington	0.170	0.474
Pittsburgh	0.134	0.438
Carolina	0.107	0.410
Nashville	0.081	0.384
Colorado	0.080	0.383
Vegas	0.059	0.363
San Jose	0.056	0.359
Dallas	0.017	0.320
Montreal	(0.017)	0.286
Florida	(0.296)	0.007
Arizona	(0.302)	0.002
Chicago	(0.391)	(0.088)
Minnesota	(0.450)	(0.147)
Vancouver	(0.508)	(0.205)
Philadelphia	(0.579)	(0.275)
Edmonton	(0.664)	(0.361)
Buffalo	(0.668)	(0.365)
NY Rangers	(0.672)	(0.368)
Detroit	(0.703)	(0.400)
New Jersey	(0.765)	(0.461)
Anaheim	(0.784)	(0.481)
Ottawa	(0.843)	(0.539)
Los Angeles	(0.885)	(0.581)

[TBODY] [/TBODY]

To read the table, figure out if the goaltender is at HOME or AWAY. For instance, Tampa Bay would be favored over an average team by 0.896 if you are at HOME (Tampa Bay on the road) and favored by 1.199 if you are AWAY (Tampa Bay is at home).

 

[Doctor No]

Clear as mud? Let me know if you have questions.

 

[Doctor No]

These are the same measures for the 2019 Stanley Cup playoffs. Obvious differences: the "average team" is usually above average, and you play a lot more top quartile teams (no bottom quartile teams made the playoffs).

NAME	TEAM	AVGOSTR	AVGOSPC	TOPPCT	HOMEPCT
Binnington Jordan	STL	0.36	0.916	43%	50%
Bishop Ben	DAL	0.30	0.923	53%	45%
Bobrovsky Sergei	CBJ	0.86	0.908	100%	47%
Allen Jake	STL	0.58	0.917	100%	100%
Andersen Frederik	TOR	0.76	0.917	100%	42%
Dell Aaron	SJS	0.06	0.926	0%	100%
Fleury Marc-Andre	VGK	0.22	0.906	0%	45%
Greiss Thomas	NYI	0.41	0.929	0%	0%
Grubauer Philipp	COL	0.40	0.901	43%	43%
Hellebuyck Connor	WPG	0.34	0.918	100%	49%
Holtby Braden	WSH	0.23	0.929	0%	61%
Jones Martin	SJS	0.25	0.921	31%	54%
Khudobin Anton	DAL	0.18	0.918	100%	100%
Lehner Robin	NYI	0.26	0.920	0%	55%
McElhinney Curtis	CAR	0.41	0.919	100%	88%
Mrazek Petr	CAR	0.47	0.909	32%	27%
Murray Matt	PIT	0.37	0.920	100%	49%
Rask Tuukka	BOS	0.34	0.916	84%	55%
Rinne Pekka	NSH	0.16	0.930	0%	54%
Saros Juuse	NSH	0.32	0.930	0%	0%
Smith Mike	CGY	0.20	0.918	0%	59%
Vasilevski Andrei	TBL	0.41	0.915	100%	50%

[TBODY] [/TBODY]

 

[morehockeystats]

Clear as mud? Let me know if you have questions.

Shouldn't the top 25 and bottom 25 be measured at the time of the game, and thus be different for each point in time?

 

[Doctor No]

I don't trust data credibility at that level of granularity, but have no problem if others want to do it.

On some level, even if you want to do a "point in time" strength of opponent, you need to have a large enough sample size that doesn't include the game in question (because otherwise you'd get a feedback loop). My chosen sample size is full season. Others may reasonably choose something else.