Goalies

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

2025-26All situationsThrough 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 2025-26: Scott Wedgewood (COL), +1.7 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), 2025-26, All situations
#	Goalie	Team			±95%	Rebound SV%	Rush SV%	Cycle SV%	Off-draw SV%
1	Scott WedgewoodCOL	COL	+1.7	1,689	1.0	91.4%	92.9%	92.9%	89.1%
2	Dennis HildebyTOR	TOR	+1.3	853	1.5	89.2%	91.3%	91.4%	100.0%
3	Logan ThompsonWSH	WSH	+1.2	2,461	0.9	85.6%	93.4%	92.1%	98.5%
4	Igor ShesterkinNYR	NYR	+1.0	2,148	1.0	84.6%	93.7%	92.2%	92.2%
5	Jesper WallstedtMIN	MIN	+1.0	1,527	1.2	83.3%	96.9%	91.7%	92.5%
6	Devin CooleyCGY	CGY	+0.9	1,286	1.3	88.1%	90.7%	90.5%	100.0%
7	Philipp GrubauerSEA	SEA	+0.9	1,328	1.3	86.0%	94.2%	92.2%	100.0%
8	Andrei VasilevskiyTBL	TBL	+0.9	2,227	1.0	86.9%	91.1%	92.2%	97.4%
9	Joel HoferSTL	STL	+0.9	1,950	1.0	79.8%	94.4%	92.2%	98.7%
10	Anton ForsbergLAK	LAK	+0.9	1,463	1.2	88.3%	91.7%	90.3%	90.6%
11	Jeremy SwaymanBOS	BOS	+0.8	2,428	0.9	84.9%	96.4%	90.8%	92.3%
12	Dan VladarPHI	PHI	+0.8	2,013	1.0	88.4%	90.9%	91.3%	92.2%
13	Jet GreavesCBJ	CBJ	+0.8	2,380	1.0	91.5%	92.4%	90.4%	93.0%
14	Ukko-Pekka LuukkonenBUF	BUF	+0.8	1,419	1.2	86.7%	93.5%	90.7%	98.4%
15	Ilya SorokinNYI	NYI	+0.7	2,324	1.0	88.4%	93.8%	90.0%	95.0%
16	Casey DeSmithDAL	DAL	+0.7	1,166	1.4	80.2%	92.9%	91.4%	100.0%
17	Alex LyonBUF	BUF	+0.7	1,506	1.2	84.8%	92.4%	90.7%	100.0%
18	Justus AnnunenNSH	NSH	+0.7	1,112	1.4	82.1%	93.5%	89.4%	100.0%
19	Mackenzie BlackwoodCOL	COL	+0.6	1,441	1.2	87.3%	83.2%	90.8%	97.4%
20	Jake AllenNJD	NJD	+0.6	1,572	1.2	84.4%	91.4%	89.4%	97.4%
21	Filip GustavssonMIN	MIN	+0.5	2,130	1.0	83.0%	93.0%	91.6%	89.0%
22	Jakub DobesMTL	MTL	+0.5	1,817	1.1	86.6%	92.5%	90.9%	97.8%
23	John GibsonDET	DET	+0.5	2,278	1.0	87.0%	91.5%	89.5%	96.0%
24	Spencer KnightCHI	CHI	+0.5	2,423	1.0	87.2%	94.4%	90.3%	98.6%
25	David RittichNYI	NYI	+0.3	1,166	1.4	88.3%	92.4%	89.0%	100.0%
26	Connor IngramEDM	EDM	+0.3	1,173	1.4	80.3%	94.2%	90.2%	90.3%
27	Akira SchmidVGK	VGK	+0.2	1,284	1.4	84.4%	95.5%	90.9%	91.7%
28	Joey DaccordSEA	SEA	+0.2	2,055	1.1	86.0%	91.9%	88.7%	97.1%
29	Jake OettingerDAL	DAL	+0.1	2,079	1.1	85.0%	94.2%	89.1%	94.1%
30	Karel VejmelkaUTA	UTA	+0.1	2,515	1.0	73.7%	92.9%	90.9%	97.4%
31	Joseph WollTOR	TOR	+0.1	1,840	1.1	87.4%	90.6%	88.9%	92.0%
32	Connor HellebuyckWPG	WPG	+0.1	2,363	1.0	85.2%	93.8%	89.0%	93.8%
33	Thatcher DemkoVAN	VAN	+0.1	806	1.7	80.8%	92.1%	91.8%	93.5%
34	Dustin WolfCGY	CGY	−0.0	2,316	1.0	85.2%	90.7%	90.8%	95.9%
35	Stuart SkinnerEDM/PIT	EDM/PIT	−0.1	2,055	1.1	80.6%	88.3%	90.3%	94.9%
36	Jonathan QuickNYR	NYR	−0.1	1,051	1.5	76.5%	89.3%	88.3%	100.0%
37	Daniil TarasovFLA	FLA	−0.2	1,388	1.3	85.0%	95.1%	88.3%	90.4%
38	Eric ComrieWPG	WPG	−0.2	1,019	1.6	81.6%	87.5%	92.7%	92.6%
39	Alex NedeljkovicSJS	SJS	−0.3	1,562	1.3	82.5%	95.3%	88.4%	94.8%
40	Joonas KorpisaloBOS	BOS	−0.3	1,250	1.4	90.8%	92.4%	88.9%	90.3%
41	Darcy KuemperLAK	LAK	−0.4	1,879	1.2	87.5%	93.8%	89.0%	95.5%
42	Lukas DostalANA	ANA	−0.4	2,360	1.0	89.9%	92.3%	87.5%	93.3%
43	Brandon BussiCAR	CAR	−0.4	1,347	1.4	85.0%	89.2%	89.7%	97.7%
44	Anthony StolarzTOR	TOR	−0.4	1,059	1.6	87.1%	87.7%	91.9%	96.3%
45	Linus UllmarkOTT	OTT	−0.4	1,804	1.2	80.7%	91.7%	89.1%	96.1%
46	Juuse SarosNSH	NSH	−0.5	2,482	1.0	78.0%	94.2%	90.0%	94.0%
47	Arturs SilovsPIT	PIT	−0.5	1,571	1.3	76.7%	93.4%	90.8%	96.8%
48	Tristan JarryEDM/PIT	EDM/PIT	−0.7	1,309	1.4	83.1%	93.6%	89.8%	94.9%
49	Elvis MerzlikinsCBJ	CBJ	−0.8	1,246	1.5	88.2%	92.9%	86.5%	92.1%
50	Ville HussoANA	ANA	−0.8	814	1.8	77.8%	91.9%	88.9%	90.5%
51	Cam TalbotDET	DET	−0.9	1,223	1.5	81.2%	88.6%	90.5%	97.2%
52	Vitek VanecekUTA	UTA	−0.9	755	1.9	85.7%	92.5%	88.7%	100.0%
53	Nikita TolopiloVAN	VAN	−0.9	892	1.8	90.7%	91.1%	88.3%	95.8%
54	Jacob MarkstromNJD	NJD	−0.9	1,689	1.3	80.9%	84.8%	89.6%	100.0%
55	Yaroslav AskarovSJS	SJS	−1.0	1,987	1.2	83.0%	92.6%	89.0%	95.2%
56	Sergei BobrovskyFLA	FLA	−1.1	1,948	1.2	74.8%	92.8%	87.6%	95.7%
57	Jonas JohanssonTBL	TBL	−1.1	999	1.7	89.4%	88.9%	87.4%	95.7%
58	Samuel ErssonPHI	PHI	−1.2	1,219	1.5	83.1%	84.3%	87.2%	92.1%
59	Arvid SoderblomCHI	CHI	−1.2	1,159	1.6	86.2%	90.0%	88.6%	95.7%
60	Charlie LindgrenWSH	WSH	−1.2	919	1.7	67.4%	93.7%	88.4%	93.8%
61	Frederik AndersenCAR	CAR	−1.3	1,302	1.5	85.4%	85.3%	86.2%	97.2%
62	Kevin LankinenVAN	VAN	−1.4	1,936	1.2	77.6%	95.4%	89.2%	91.4%
63	Adin HillVGK	VGK	−1.4	931	1.8	76.1%	89.1%	86.2%	92.0%
64	Samuel MontembeaultMTL	MTL	−1.5	967	1.7	84.5%	92.7%	87.7%	82.1%
65	Jordan BinningtonSTL	STL	−1.8	1,488	1.4	79.5%	90.8%	88.4%	91.5%

Minimum sample: 738 shots (it scales with games played and sequence coverage). 33 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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