Goalies

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

2025-265-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 2025-26: Dan Vladar (PHI), +1.5 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, 5-on-5
#	Goalie	Team			±95%	Rebound SV%	Rush SV%	Cycle SV%	Off-draw SV%
1	Dan VladarPHI	PHI	+1.5	1,568	1.0	90.8%	92.2%	92.9%	92.9%
2	Scott WedgewoodCOL	COL	+1.5	1,317	1.1	92.9%	93.2%	93.8%	91.1%
3	Anton ForsbergLAK	LAK	+1.4	1,168	1.2	90.8%	91.7%	91.9%	94.7%
4	Jeremy SwaymanBOS	BOS	+1.4	1,813	1.0	89.0%	97.3%	92.4%	93.4%
5	Philipp GrubauerSEA	SEA	+1.4	1,080	1.3	89.5%	93.3%	93.0%	100.0%
6	Igor ShesterkinNYR	NYR	+1.3	1,659	1.0	88.0%	96.7%	92.6%	95.9%
7	Logan ThompsonWSH	WSH	+1.2	1,904	1.0	86.2%	94.4%	93.7%	98.2%
8	Casey DeSmithDAL	DAL	+1.1	916	1.4	80.6%	94.6%	93.6%	100.0%
9	Justus AnnunenNSH	NSH	+0.9	876	1.5	82.4%	94.0%	90.0%	100.0%
10	Jet GreavesCBJ	CBJ	+0.8	1,865	1.0	91.9%	92.4%	91.2%	94.5%
11	Mackenzie BlackwoodCOL	COL	+0.7	1,138	1.3	85.9%	85.1%	92.2%	95.8%
12	Ilya SorokinNYI	NYI	+0.7	1,851	1.0	90.1%	93.3%	91.2%	94.1%
13	Jakub DobesMTL	MTL	+0.7	1,410	1.2	89.4%	93.3%	92.2%	97.2%
14	Jesper WallstedtMIN	MIN	+0.7	1,208	1.3	89.1%	97.8%	92.2%	92.3%
15	Alex LyonBUF	BUF	+0.7	1,171	1.3	85.2%	91.4%	91.3%	100.0%
16	Joel HoferSTL	STL	+0.7	1,525	1.1	80.2%	95.5%	93.1%	98.5%
17	Filip GustavssonMIN	MIN	+0.6	1,731	1.1	84.6%	92.8%	93.2%	90.3%
18	Akira SchmidVGK	VGK	+0.6	1,059	1.4	84.2%	98.2%	92.0%	93.9%
19	John GibsonDET	DET	+0.5	1,794	1.1	90.4%	91.5%	90.0%	96.6%
20	Ukko-Pekka LuukkonenBUF	BUF	+0.5	1,095	1.4	87.1%	94.0%	90.9%	97.8%
21	Joey DaccordSEA	SEA	+0.5	1,666	1.1	90.2%	93.4%	90.4%	96.2%
22	Darcy KuemperLAK	LAK	+0.4	1,444	1.2	88.0%	96.9%	90.2%	95.7%
23	Jake OettingerDAL	DAL	+0.4	1,605	1.1	86.1%	95.7%	91.5%	94.4%
24	Andrei VasilevskiyTBL	TBL	+0.3	1,709	1.1	85.7%	91.0%	92.0%	100.0%
25	Devin CooleyCGY	CGY	+0.3	994	1.5	88.6%	90.3%	90.7%	100.0%
26	Spencer KnightCHI	CHI	+0.2	1,961	1.0	88.3%	93.9%	90.6%	98.3%
27	Eric ComrieWPG	WPG	+0.2	819	1.6	87.7%	86.0%	93.7%	91.7%
28	David RittichNYI	NYI	+0.2	926	1.5	89.6%	90.7%	89.4%	100.0%
29	Stuart SkinnerEDM/PIT	EDM/PIT	+0.1	1,648	1.1	80.6%	89.7%	92.8%	94.0%
30	Karel VejmelkaUTA	UTA	+0.1	1,952	1.1	77.3%	91.3%	90.6%	96.9%
31	Connor HellebuyckWPG	WPG	+0.1	1,874	1.1	87.5%	93.5%	90.1%	94.5%
32	Jake AllenNJD	NJD	−0.0	1,299	1.3	84.1%	89.5%	90.0%	97.1%
33	Joseph WollTOR	TOR	−0.1	1,464	1.2	86.4%	93.0%	89.4%	92.3%
34	Brandon BussiCAR	CAR	−0.2	1,063	1.5	86.5%	89.0%	90.8%	96.9%
35	Linus UllmarkOTT	OTT	−0.2	1,423	1.3	81.4%	92.8%	90.4%	97.8%
36	Alex NedeljkovicSJS	SJS	−0.2	1,234	1.3	86.3%	95.5%	89.1%	93.5%
37	Connor IngramEDM	EDM	−0.2	942	1.6	83.3%	92.5%	90.6%	88.9%
38	Joonas KorpisaloBOS	BOS	−0.2	980	1.5	92.9%	95.4%	88.8%	87.0%
39	Lukas DostalANA	ANA	−0.3	1,838	1.1	89.2%	92.1%	89.0%	93.7%
40	Dustin WolfCGY	CGY	−0.3	1,808	1.1	85.7%	90.4%	91.2%	95.2%
41	Daniil TarasovFLA	FLA	−0.3	1,097	1.5	82.4%	96.8%	89.4%	90.2%
42	Elvis MerzlikinsCBJ	CBJ	−0.5	981	1.6	90.2%	93.6%	89.1%	93.5%
43	Tristan JarryEDM/PIT	EDM/PIT	−0.5	1,013	1.5	84.1%	92.6%	90.8%	93.8%
44	Jonas JohanssonTBL	TBL	−0.5	768	1.7	93.9%	88.7%	88.7%	97.4%
45	Jonathan QuickNYR	NYR	−0.6	840	1.7	79.2%	88.9%	87.6%	100.0%
46	Juuse SarosNSH	NSH	−0.7	1,992	1.1	78.1%	93.9%	89.9%	97.1%
47	Arturs SilovsPIT	PIT	−0.9	1,238	1.4	79.7%	94.1%	90.4%	100.0%
48	Cam TalbotDET	DET	−1.0	983	1.6	83.3%	88.9%	91.5%	100.0%
49	Jacob MarkstromNJD	NJD	−1.0	1,350	1.4	85.5%	83.2%	89.6%	100.0%
50	Kevin LankinenVAN	VAN	−1.0	1,583	1.3	81.0%	95.7%	91.5%	93.3%
51	Yaroslav AskarovSJS	SJS	−1.0	1,514	1.3	85.7%	93.3%	90.5%	96.2%
52	Sergei BobrovskyFLA	FLA	−1.1	1,522	1.3	76.2%	94.3%	88.3%	94.3%
53	Anthony StolarzTOR	TOR	−1.2	819	1.8	87.2%	88.5%	91.8%	96.0%
54	Samuel ErssonPHI	PHI	−1.4	936	1.7	83.0%	83.3%	88.9%	90.3%
55	Frederik AndersenCAR	CAR	−1.4	1,022	1.6	93.7%	83.5%	86.3%	95.8%
56	Jordan BinningtonSTL	STL	−1.5	1,206	1.5	82.0%	89.0%	88.6%	92.0%
57	Arvid SoderblomCHI	CHI	−1.6	960	1.7	87.0%	90.2%	88.3%	97.3%

Minimum sample: 738 shots (it scales with games played and sequence coverage). 41 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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