Goalies

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

2023-24All 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 2023-24: Laurent Brossoit (WPG), +1.8 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, All situations
#	Goalie	Team			±95%	Rebound SV%	Rush SV%	Cycle SV%	Off-draw SV%
1	Laurent BrossoitWPG	WPG	+1.8	922	1.4	89.1%	96.9%	91.7%	93.3%
2	Anthony StolarzFLA	FLA	+1.8	1,022	1.3	88.8%	91.7%	94.6%	90.6%
3	David RittichLAK	LAK	+1.4	912	1.5	82.5%	93.5%	93.2%	100.0%
4	Connor HellebuyckWPG	WPG	+1.2	2,570	0.9	86.3%	94.6%	92.6%	98.1%
5	Semyon VarlamovNYI	NYI	+1.1	1,223	1.3	89.4%	94.2%	92.4%	97.1%
6	Joey DaccordSEA	SEA	+1.0	2,061	1.0	83.8%	94.2%	93.6%	90.7%
7	Jeremy SwaymanBOS	BOS	+1.0	1,897	1.0	83.9%	91.8%	93.9%	95.8%
8	Thatcher DemkoVAN	VAN	+0.8	2,090	1.0	89.0%	92.3%	92.5%	96.4%
9	Linus UllmarkBOS	BOS	+0.8	1,766	1.1	77.2%	90.0%	94.2%	92.2%
10	Jonathan QuickNYR	NYR	+0.7	1,149	1.4	91.1%	94.7%	88.3%	100.0%
11	Joel HoferSTL	STL	+0.7	1,203	1.3	84.7%	95.2%	90.2%	95.3%
12	Igor ShesterkinNYR	NYR	+0.6	2,307	1.0	83.4%	94.7%	92.5%	98.8%
13	Jordan BinningtonSTL	STL	+0.6	2,572	0.9	87.6%	92.8%	91.2%	94.9%
14	Sergei BobrovskyFLA	FLA	+0.6	2,232	1.0	83.1%	91.2%	92.3%	97.3%
15	Charlie LindgrenWSH	WSH	+0.6	2,109	1.0	93.2%	91.5%	91.3%	97.2%
16	Ukko-Pekka LuukkonenBUF	BUF	+0.6	2,175	1.0	88.4%	94.8%	91.3%	94.9%
17	Cam TalbotLAK	LAK	+0.6	2,138	1.0	85.4%	90.6%	91.4%	100.0%
18	Adin HillVGK	VGK	+0.5	1,462	1.2	88.1%	90.5%	92.2%	98.2%
19	Cayden PrimeauMTL	MTL	+0.4	1,051	1.5	90.1%	94.5%	90.5%	91.4%
20	Logan ThompsonVGK	VGK	+0.3	1,866	1.1	80.3%	92.7%	90.7%	98.3%
21	Connor IngramARI	ARI	+0.3	2,149	1.0	89.4%	88.7%	89.9%	96.0%
22	Ilya SorokinNYI	NYI	+0.3	2,580	0.9	88.2%	93.7%	91.5%	98.9%
23	Jacob MarkstromCGY	CGY	+0.3	2,007	1.1	84.5%	90.1%	91.9%	94.0%
24	Martin JonesTOR	TOR	+0.2	857	1.7	85.9%	95.9%	90.9%	100.0%
25	Calvin PickardEDM	EDM	+0.2	829	1.7	85.2%	91.7%	91.2%	100.0%
26	Tristan JarryPIT	PIT	+0.2	2,052	1.1	80.3%	88.6%	90.4%	96.4%
27	Joseph WollTOR	TOR	+0.1	1,092	1.5	83.3%	89.9%	92.6%	96.0%
28	Jake OettingerDAL	DAL	+0.1	2,146	1.0	79.1%	95.2%	91.1%	95.8%
29	Pyotr KochetkovCAR	CAR	+0.1	1,420	1.3	83.5%	92.9%	90.9%	93.5%
30	Juuse SarosNSH	NSH	+0.1	2,556	1.0	87.7%	92.2%	91.5%	91.7%
31	Petr MrazekCHI	CHI	+0.1	2,439	1.0	82.8%	90.8%	90.5%	98.9%
32	Daniil TarasovCBJ	CBJ	+0.1	1,097	1.5	88.9%	89.8%	89.7%	91.9%
33	Samuel MontembeaultMTL	MTL	+0.0	1,895	1.1	87.2%	91.5%	92.2%	90.2%
34	Alex NedeljkovicPIT	PIT	+0.0	1,538	1.2	79.6%	93.0%	90.8%	98.1%
35	Carter HartPHI	PHI	+0.0	1,029	1.5	83.1%	92.6%	91.3%	88.6%
36	Alex LyonDET	DET	−0.0	1,910	1.1	82.0%	92.4%	89.9%	93.3%
37	Stuart SkinnerEDM	EDM	−0.0	2,212	1.0	83.8%	93.1%	90.7%	97.3%
38	Kevin LankinenNSH	NSH	−0.0	829	1.7	83.6%	97.2%	91.1%	96.3%
39	James ReimerDET	DET	−0.1	1,043	1.5	84.9%	91.9%	89.1%	100.0%
40	Devon LeviBUF	BUF	−0.1	965	1.6	84.1%	92.5%	90.0%	96.6%
41	Philipp GrubauerSEA	SEA	−0.2	1,367	1.3	80.0%	95.9%	91.0%	97.1%
42	Kaapo KahkonenNJD/SJS	NJD/SJS	−0.2	1,675	1.2	84.0%	90.9%	89.2%	96.4%
43	Mackenzie BlackwoodSJS	SJS	−0.3	1,988	1.1	84.8%	92.9%	90.1%	97.1%
44	Lukas DostalANA	ANA	−0.3	1,862	1.2	82.0%	90.8%	90.2%	95.9%
45	Casey DeSmithVAN	VAN	−0.4	1,111	1.5	86.8%	94.1%	91.1%	100.0%
46	Ville HussoDET	DET	−0.4	832	1.7	90.9%	86.0%	88.9%	94.6%
47	Andrei VasilevskiyTBL	TBL	−0.4	2,080	1.1	82.8%	92.2%	91.5%	93.8%
48	Scott WedgewoodDAL	DAL	−0.4	1,217	1.4	76.5%	96.4%	92.7%	93.8%
49	Ilya SamsonovTOR	TOR	−0.4	1,657	1.2	86.6%	89.4%	90.8%	93.3%
50	Nico DawsNJD	NJD	−0.4	837	1.7	85.9%	89.5%	87.2%	89.3%
51	Filip GustavssonMIN	MIN	−0.5	1,836	1.2	82.7%	93.4%	89.7%	97.1%
52	Jake AllenMTL/NJD	MTL/NJD	−0.5	1,545	1.3	84.6%	93.3%	90.5%	98.0%
53	Elvis MerzlikinsCBJ	CBJ	−0.5	1,794	1.2	87.4%	91.3%	88.9%	100.0%
54	Alexandar GeorgievCOL	COL	−0.6	2,526	1.0	82.9%	93.4%	90.4%	97.7%
55	Samuel ErssonPHI	PHI	−0.6	1,806	1.2	84.6%	93.8%	88.4%	96.2%
56	Marc-Andre FleuryMIN	MIN	−0.6	1,543	1.3	85.7%	90.6%	92.2%	97.8%
57	Karel VejmelkaARI	ARI	−0.6	1,556	1.3	78.8%	93.7%	88.2%	100.0%
58	Darcy KuemperWSH	WSH	−0.7	1,385	1.4	84.8%	92.2%	90.7%	100.0%
59	Spencer MartinCAR/CBJ	CAR/CBJ	−0.8	778	1.9	75.4%	92.9%	91.1%	93.1%
60	John GibsonANA	ANA	−0.9	1,968	1.2	81.9%	88.1%	88.8%	94.6%
61	Joonas KorpisaloOTT	OTT	−1.0	2,170	1.1	87.4%	88.1%	88.2%	97.1%
62	Vitek VanecekNJD	NJD	−1.1	1,211	1.5	71.4%	96.2%	90.5%	93.8%
63	Anton ForsbergOTT	OTT	−1.2	1,067	1.6	83.5%	97.2%	88.7%	91.9%
64	Jonas JohanssonTBL	TBL	−1.3	1,021	1.7	88.7%	90.1%	88.4%	89.2%
65	Dan VladarCGY	CGY	−1.5	837	1.9	84.1%	96.2%	82.8%	89.7%
66	Arvid SoderblomCHI	CHI	−1.7	1,345	1.5	83.0%	87.5%	87.3%	96.3%

Minimum sample: 738 shots (it scales with games played and sequence coverage). 32 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

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