Goalies

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

2024-25All 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 2024-25: Anthony Stolarz (TOR), +2.0 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), 2024-25, All situations
#	Goalie	Team			±95%	Rebound SV%	Rush SV%	Cycle SV%	Off-draw SV%
1	Anthony StolarzTOR	TOR	+2.0	1,501	1.1	86.2%	95.7%	93.9%	100.0%
2	Connor HellebuyckWPG	WPG	+1.8	2,599	0.8	87.4%	95.7%	92.0%	97.8%
3	Darcy KuemperLAK	LAK	+1.4	1,952	1.0	83.6%	96.0%	92.3%	95.0%
4	Andrei VasilevskiyTBL	TBL	+1.1	2,496	0.9	92.4%	93.0%	90.8%	98.1%
5	Filip GustavssonMIN	MIN	+0.9	2,578	0.9	82.5%	93.2%	91.9%	96.0%
6	Logan ThompsonWSH	WSH	+0.9	1,814	1.1	88.0%	92.5%	91.0%	95.7%
7	Casey DeSmithDAL	DAL	+0.9	1,133	1.4	85.1%	96.8%	92.6%	95.9%
8	Eric ComrieWPG	WPG	+0.8	833	1.6	84.8%	95.1%	91.7%	96.7%
9	Joey DaccordSEA	SEA	+0.7	2,513	0.9	85.5%	94.7%	90.3%	93.8%
10	Adin HillVGK	VGK	+0.7	2,030	1.0	83.3%	91.9%	91.1%	94.5%
11	John GibsonANA	ANA	+0.7	1,209	1.3	82.5%	88.4%	93.1%	95.8%
12	Mackenzie BlackwoodCOL/SJS	COL/SJS	+0.7	2,307	1.0	85.1%	96.3%	90.4%	96.7%
13	Joseph WollTOR	TOR	+0.6	1,832	1.1	87.5%	94.7%	89.5%	93.0%
14	Jake AllenNJD	NJD	+0.5	1,246	1.3	82.7%	93.8%	89.7%	95.0%
15	Ilya SorokinNYI	NYI	+0.5	2,583	0.9	89.0%	95.0%	88.8%	97.4%
16	Jake OettingerDAL	DAL	+0.5	2,431	0.9	83.3%	91.8%	91.4%	95.2%
17	Scott WedgewoodCOL/NSH	COL/NSH	+0.4	867	1.6	89.1%	94.8%	88.3%	100.0%
18	Dustin WolfCGY	CGY	+0.4	2,261	1.0	85.9%	93.1%	91.0%	98.8%
19	Linus UllmarkOTT	OTT	+0.4	1,766	1.1	83.3%	93.9%	91.7%	98.4%
20	Kevin LankinenVAN	VAN	+0.3	2,093	1.0	82.3%	96.7%	90.1%	96.8%
21	Jacob MarkstromNJD	NJD	+0.3	1,932	1.1	89.0%	92.6%	88.7%	93.1%
22	Igor ShesterkinNYR	NYR	+0.3	2,610	0.9	84.7%	92.0%	90.8%	98.6%
23	Sergei BobrovskyFLA	FLA	+0.3	2,061	1.0	88.9%	92.5%	88.7%	98.7%
24	Lukas DostalANA	ANA	+0.3	2,498	1.0	82.2%	95.0%	92.0%	97.5%
25	Samuel MontembeaultMTL	MTL	+0.2	2,584	0.9	85.6%	91.1%	90.7%	90.7%
26	Spencer KnightCHI/FLA	CHI/FLA	+0.2	1,558	1.2	86.4%	95.3%	90.7%	90.2%
27	Karel VejmelkaUTA	UTA	+0.1	2,229	1.0	77.6%	95.1%	92.9%	95.7%
28	Joel HoferSTL	STL	+0.1	1,140	1.4	83.6%	94.7%	88.4%	97.6%
29	Jordan BinningtonSTL	STL	+0.0	2,188	1.0	87.9%	95.2%	90.7%	94.4%
30	Cam TalbotDET	DET	−0.0	1,946	1.1	85.2%	92.3%	89.9%	95.8%
31	Alex LyonDET	DET	−0.0	1,092	1.5	86.7%	94.8%	89.6%	89.2%
32	Anton ForsbergOTT	OTT	−0.0	1,144	1.4	81.9%	92.1%	90.0%	96.7%
33	James ReimerANA/BUF	ANA/BUF	−0.0	1,042	1.5	76.5%	93.3%	89.6%	97.4%
34	Arvid SoderblomCHI	CHI	−0.1	1,636	1.2	84.7%	94.2%	89.1%	97.6%
35	Frederik AndersenCAR	CAR	−0.1	810	1.7	83.1%	88.3%	90.9%	93.8%
36	Charlie LindgrenWSH	WSH	−0.1	1,531	1.3	80.9%	93.3%	89.8%	95.6%
37	Joonas KorpisaloBOS	BOS	−0.2	1,076	1.5	81.3%	91.3%	90.3%	96.6%
38	Ilya SamsonovVGK	VGK	−0.2	1,197	1.4	85.1%	93.8%	88.2%	95.1%
39	Dan VladarCGY	CGY	−0.2	1,223	1.4	72.9%	90.0%	92.1%	97.6%
40	Jonas JohanssonTBL	TBL	−0.3	834	1.7	86.2%	94.7%	90.9%	86.4%
41	Thatcher DemkoVAN	VAN	−0.4	888	1.7	78.5%	93.2%	90.7%	89.5%
42	Pyotr KochetkovCAR	CAR	−0.4	1,717	1.2	85.7%	89.9%	89.8%	98.1%
43	Stuart SkinnerEDM	EDM	−0.4	1,955	1.1	83.1%	93.8%	88.5%	96.4%
44	Marc-Andre FleuryMIN	MIN	−0.5	965	1.6	85.1%	100.0%	88.5%	93.1%
45	Alex NedeljkovicPIT	PIT	−0.5	1,564	1.3	84.8%	94.4%	88.7%	97.7%
46	Tristan JarryPIT	PIT	−0.5	1,468	1.3	83.2%	93.9%	88.9%	100.0%
47	Jonathan QuickNYR	NYR	−0.5	977	1.6	90.4%	91.2%	88.0%	92.6%
48	Calvin PickardEDM	EDM	−0.5	1,201	1.5	82.4%	93.9%	89.5%	100.0%
49	Jeremy SwaymanBOS	BOS	−0.6	2,447	1.0	86.1%	91.4%	89.3%	95.1%
50	Elvis MerzlikinsCBJ	CBJ	−0.6	2,331	1.0	82.2%	92.4%	90.0%	96.2%
51	Juuse SarosNSH	NSH	−0.6	2,319	1.1	86.0%	92.3%	89.6%	93.6%
52	Petr MrazekCHI/DET	CHI/DET	−0.8	1,550	1.3	85.2%	90.5%	90.2%	97.9%
53	Ukko-Pekka LuukkonenBUF	BUF	−0.8	2,272	1.1	78.1%	92.0%	87.5%	98.6%
54	David RittichLAK	LAK	−0.9	1,164	1.5	80.8%	95.9%	88.8%	90.9%
55	Vitek VanecekFLA/SJS	FLA/SJS	−1.0	1,084	1.6	85.5%	92.1%	88.9%	86.4%
56	Samuel ErssonPHI	PHI	−1.1	1,759	1.2	89.8%	92.1%	87.7%	87.2%
57	Philipp GrubauerSEA	SEA	−1.2	1,079	1.6	78.8%	94.1%	88.6%	97.3%
58	Daniil TarasovCBJ	CBJ	−1.2	822	1.8	80.0%	89.5%	91.1%	95.8%
59	Justus AnnunenCOL/NSH	COL/NSH	−1.2	1,217	1.5	85.7%	94.0%	88.2%	93.0%
60	Connor IngramUTA	UTA	−1.3	839	1.8	80.0%	92.8%	88.2%	93.1%
61	Ivan FedotovPHI	PHI	−1.4	937	1.7	85.7%	98.3%	85.5%	84.0%
62	Alexandar GeorgievCOL/SJS	COL/SJS	−1.8	1,934	1.2	80.5%	93.4%	88.3%	91.3%

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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How to read it

How reliable is it?

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