Goalies

Goalie Save % by Shot Class (Rebound, Rush, Cycle, Off the Draw)

2023-245-on-5Through 82 team games

Not every shot is equally hard. League-wide, goalies stop about .849 of rebound attempts, .896 of cycle shots, .931 of rush and transition shots and .950 of shots straight off an offensive-zone draw. A goalie who faces more rebounds is going to post a lower raw save percentage through no fault of his own.

This board sorts every unblocked shot against into those classes and ranks goalies by how far their save percentage beats the rate their own shot mix predicts. The class columns show where the gap comes from, with the ± column as a 95% interval.

Leader in 2023-24: Anthony Stolarz (FLA), +2.4 FSV% vs exp..

Season2026-27 2025-26 2024-25 2023-24

StrengthAll situations 5-on-5

Goalie Save % by Shot Class (Rebound, Rush, Cycle, Off the Draw), 2023-24, 5-on-5
#	Goalie	Team			±95%	Rebound SV%	Rush SV%	Cycle SV%	Off-draw SV%
1	Anthony StolarzFLA	FLA	+2.4	773	1.3	92.4%	93.0%	96.8%	88.0%
2	Connor HellebuyckWPG	WPG	+1.4	2,033	0.9	91.3%	94.5%	93.5%	97.7%
3	Joey DaccordSEA	SEA	+1.4	1,648	1.0	87.4%	94.8%	95.9%	90.5%
4	Semyon VarlamovNYI	NYI	+1.2	965	1.4	90.2%	94.1%	92.5%	95.7%
5	Jeremy SwaymanBOS	BOS	+1.2	1,463	1.1	83.8%	93.2%	94.9%	95.1%
6	Linus UllmarkBOS	BOS	+1.1	1,387	1.1	81.7%	91.3%	95.1%	89.6%
7	Cayden PrimeauMTL	MTL	+0.9	808	1.5	92.9%	96.8%	91.9%	95.8%
8	Joel HoferSTL	STL	+0.8	993	1.4	88.2%	94.0%	92.1%	96.9%
9	Ilya SorokinNYI	NYI	+0.7	2,076	1.0	89.5%	93.6%	93.1%	98.7%
10	Casey DeSmithVAN	VAN	+0.7	865	1.5	91.5%	94.6%	93.1%	100.0%
11	Sergei BobrovskyFLA	FLA	+0.6	1,720	1.1	83.3%	92.9%	92.5%	98.1%
12	Thatcher DemkoVAN	VAN	+0.5	1,577	1.1	90.8%	92.9%	93.3%	96.9%
13	Daniil TarasovCBJ	CBJ	+0.5	869	1.5	90.9%	92.0%	92.2%	96.9%
14	Connor IngramARI	ARI	+0.5	1,649	1.1	89.8%	88.3%	92.3%	97.0%
15	Marc-Andre FleuryMIN	MIN	+0.5	1,185	1.3	90.8%	92.9%	93.8%	97.2%
16	Charlie LindgrenWSH	WSH	+0.4	1,668	1.1	91.9%	92.3%	92.1%	96.1%
17	Samuel MontembeaultMTL	MTL	+0.4	1,428	1.2	89.2%	93.5%	93.3%	92.9%
18	Ukko-Pekka LuukkonenBUF	BUF	+0.4	1,746	1.1	87.9%	96.1%	91.6%	96.3%
19	Igor ShesterkinNYR	NYR	+0.3	1,866	1.1	81.0%	94.7%	93.3%	98.4%
20	Logan ThompsonVGK	VGK	+0.3	1,490	1.2	85.0%	92.0%	91.8%	97.7%
21	Jordan BinningtonSTL	STL	+0.3	2,067	1.0	89.0%	94.0%	91.3%	97.1%
22	Tristan JarryPIT	PIT	+0.3	1,648	1.1	82.6%	91.2%	90.7%	97.7%
23	Petr MrazekCHI	CHI	+0.2	2,023	1.0	85.3%	92.6%	91.4%	98.7%
24	Scott WedgewoodDAL	DAL	+0.2	980	1.5	81.8%	99.0%	93.9%	97.4%
25	Jacob MarkstromCGY	CGY	+0.1	1,541	1.2	86.8%	92.1%	92.0%	95.2%
26	Cam TalbotLAK	LAK	+0.1	1,657	1.1	86.7%	91.2%	91.8%	100.0%
27	Adin HillVGK	VGK	+0.1	1,173	1.3	86.4%	90.5%	93.5%	97.8%
28	Ilya SamsonovTOR	TOR	+0.1	1,264	1.3	88.8%	88.4%	92.0%	100.0%
29	Juuse SarosNSH	NSH	+0.1	2,018	1.0	90.1%	92.3%	92.5%	93.1%
30	Kaapo KahkonenNJD/SJS	NJD/SJS	−0.0	1,350	1.3	88.0%	88.7%	89.6%	95.9%
31	Stuart SkinnerEDM	EDM	−0.0	1,708	1.1	85.1%	93.3%	90.8%	98.0%
32	Alex LyonDET	DET	−0.2	1,517	1.2	84.2%	94.1%	90.5%	93.8%
33	Jake AllenMTL/NJD	MTL/NJD	−0.2	1,223	1.4	87.5%	94.5%	92.3%	100.0%
34	Alex NedeljkovicPIT	PIT	−0.2	1,202	1.4	80.6%	92.8%	92.4%	100.0%
35	Lukas DostalANA	ANA	−0.2	1,322	1.3	83.5%	92.3%	92.0%	96.2%
36	Jonathan QuickNYR	NYR	−0.3	912	1.6	89.5%	93.8%	87.4%	100.0%
37	Pyotr KochetkovCAR	CAR	−0.3	1,129	1.4	85.4%	94.1%	91.2%	93.8%
38	Alexandar GeorgievCOL	COL	−0.4	1,952	1.1	81.9%	94.2%	91.0%	98.4%
39	Philipp GrubauerSEA	SEA	−0.4	1,076	1.5	82.2%	95.3%	91.6%	95.8%
40	Jake OettingerDAL	DAL	−0.4	1,669	1.2	79.8%	93.9%	92.1%	94.5%
41	John GibsonANA	ANA	−0.4	1,468	1.3	83.2%	89.2%	91.5%	93.8%
42	Carter HartPHI	PHI	−0.6	820	1.7	84.2%	92.7%	91.2%	92.9%
43	Joonas KorpisaloOTT	OTT	−0.6	1,733	1.2	88.2%	89.2%	89.2%	100.0%
44	Joseph WollTOR	TOR	−0.6	839	1.7	79.5%	90.8%	93.2%	94.1%
45	Mackenzie BlackwoodSJS	SJS	−0.6	1,523	1.2	87.2%	92.7%	90.3%	96.5%
46	Darcy KuemperWSH	WSH	−0.7	1,078	1.5	83.5%	90.4%	92.4%	100.0%
47	Elvis MerzlikinsCBJ	CBJ	−0.7	1,410	1.3	87.6%	90.6%	90.2%	100.0%
48	Samuel ErssonPHI	PHI	−0.7	1,376	1.3	87.6%	92.9%	89.6%	97.6%
49	Karel VejmelkaARI	ARI	−0.7	1,198	1.4	80.5%	95.3%	89.4%	100.0%
50	Andrei VasilevskiyTBL	TBL	−0.7	1,653	1.2	82.3%	94.5%	91.4%	96.1%
51	Filip GustavssonMIN	MIN	−0.9	1,353	1.3	83.3%	93.3%	89.7%	96.2%
52	James ReimerDET	DET	−0.9	806	1.7	80.8%	91.1%	89.6%	100.0%
53	Vitek VanecekNJD	NJD	−1.0	958	1.6	72.0%	96.5%	92.9%	92.0%
54	Anton ForsbergOTT	OTT	−1.4	841	1.8	85.1%	96.7%	89.1%	96.6%
55	Jonas JohanssonTBL	TBL	−1.5	815	1.8	90.4%	90.1%	89.6%	89.7%
56	Arvid SoderblomCHI	CHI	−1.8	1,107	1.6	84.8%	87.6%	88.7%	95.0%

Minimum sample: 738 shots (it scales with games played and sequence coverage). 42 below the minimum are hidden.

How to read it

Percentage points above expected. +1.0 means about one extra save per 100 unblocked shots compared with an average goalie facing the same mix.

How reliable is it?

Per-class splits are small samples; the overall adjustment is steadier. Use the ± interval: two goalies whose intervals overlap are not reliably different.

Shot classes and goalie splits →

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Save % by shot class leaderboard

How to read it

How reliable is it?

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