On-ice results

Sequence xG (sxGF%) Leaders

2023-24All situationsThrough 82 team games

Plain expected goals add up every shot in a scramble as if each one were a fresh chance, even though only one goal can come out of it. Sequence xG discounts each follow-up attempt by the chance the play was already over: a shot worth 0.30 after a 0.25 rebound counts as 0.30 × 0.75.

That takes about 5.5% out of the league total, but it moves individual players much more: skaters whose lines live on second and third whacks drop, and clean one-shot rush lines rise.

Leader in 2023-24: Zach Hyman (EDM), 69.1% sxGF%.

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

Strength5-on-5 Even strength All situations

617 qualified

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Sequence xG (sxGF%) Leaders, 2023-24, All situations
#	Player	Team
1	Zach HymanFEDM	EDM	69.1%	1,560	5.23	2.33	69.6%	98%▼1.4
2	Connor McDavidFEDM	EDM	68.3%	1,623	5.24	2.43	68.6%	100%▲1.5
3	Evan BouchardDEDM	EDM	67.0%	1,863	4.79	2.36	67.4%	100%
4	Matthew TkachukFFLA	FLA	66.4%	1,492	4.90	2.48	67.2%	100%▲2.7
5	Matt BoldyFMIN	MIN	66.1%	1,418	4.26	2.19	66.9%	100%
6	Nathan MacKinnonFCOL	COL	65.8%	1,871	4.68	2.43	65.8%	100%▲0.8
7	Evan RodriguesFFLA	FLA	65.5%	1,213	3.48	1.83	66.2%	8.3%▲6.5
8	Jason RobertsonFDAL	DAL	65.5%	1,503	3.95	2.08	66.1%	100%▲0.5
9	Kirill KaprizovFMIN	MIN	65.2%	1,618	4.30	2.29	66.0%	98%▼0.8
10	Stefan NoesenFCAR	CAR	65.2%	961	3.44	1.84	65.3%	0.1%
11	Carter VerhaegheFFLA	FLA	65.1%	1,365	4.53	2.43	65.7%	73%▲1
12	James van RiemsdykFBOS	BOS	65.1%	959	3.78	2.03	65.5%	0.3%
13	Shane PintoFOTT	OTT	64.4%	742	3.76	2.08	65.0%	37%▲22
14	Mats ZuccarelloFMIN	MIN	64.3%	1,304	4.07	2.26	64.9%	50%▲28
15	Aleksander BarkovFFLA	FLA	64.1%	1,433	4.49	2.51	64.6%	97%▲7.2
16	Artemi PanarinFNYR	NYR	64.0%	1,650	4.67	2.62	64.3%	98%▼1.2
17	Brady TkachukFOTT	OTT	63.9%	1,551	4.09	2.32	64.2%	100%▲2.2
18	Sam ReinhartFFLA	FLA	63.5%	1,665	4.24	2.44	63.8%	99%
19	Brock BoeserFVAN	VAN	63.5%	1,507	4.06	2.34	64.2%	63%▼5.5
20	Mikko RantanenFCOL	COL	63.4%	1,832	4.36	2.51	63.3%	99%▲0.7
21	Leon DraisaitlFEDM	EDM	63.4%	1,676	4.72	2.72	63.9%	100%
22	Andrei KuzmenkoFVAN/CGY	VAN/CGY	63.3%	1,072	3.94	2.28	63.9%	11%▲9.8
23	John TavaresFTOR	TOR	63.3%	1,429	4.30	2.50	63.8%	96%▲9.3
24	Jake GuentzelFPIT/CAR	PIT/CAR	63.2%	1,341	5.03	2.92	63.8%	100%
25	Auston MatthewsFTOR	TOR	63.2%	1,698	4.48	2.60	63.8%	99%
26	Jonathan DrouinFCOL	COL	63.1%	1,437	4.12	2.40	63.1%	0.5%
27	Roope HintzFDAL	DAL	63.0%	1,379	4.14	2.44	63.5%	85%▲7.6
28	Miro HeiskanenDDAL	DAL	62.8%	1,742	3.80	2.25	63.4%	100%▲4.5
29	Nikita KucherovFTBL	TBL	62.8%	1,755	4.76	2.82	63.1%	100%
30	Joe PavelskiFDAL	DAL	62.7%	1,378	3.87	2.30	63.2%	—
31	Kevin FialaFLAK	LAK	62.7%	1,479	3.70	2.20	63.3%	67%▼8.4
32	David PastrnakFBOS	BOS	62.6%	1,634	4.19	2.51	62.9%	100%▲1.2
33	Conor GarlandFVAN	VAN	62.3%	1,192	3.36	2.04	62.6%	10%▲7.4
34	Kailer YamamotoFSEA	SEA	62.3%	708	2.90	1.76	62.2%	0.2%
35	Quinn HughesDVAN	VAN	62.1%	2,024	3.80	2.32	62.8%	100%
36	Arthur KaliyevFLAK	LAK	61.7%	602	3.20	1.99	62.7%	—
37	Ryan Nugent-HopkinsFEDM	EDM	61.7%	1,570	4.64	2.87	62.1%	77%▲15
38	Quinton ByfieldFLAK	LAK	61.7%	1,319	3.33	2.07	62.0%	86%▲18
39	Tommy NovakFNSH	NSH	61.4%	1,008	3.75	2.36	61.2%	11%▲9.1
40	Josh MorrisseyDWPG	WPG	61.3%	1,959	3.68	2.32	61.1%	99%▲3.3
41	Jordan EberleFSEA	SEA	61.3%	1,361	3.57	2.26	62.0%	30%▲2.6
42	Luke EvangelistaFNSH	NSH	61.2%	1,116	3.48	2.21	61.5%	70%▲23
43	Brandon MontourDFLA	FLA	61.2%	1,548	3.73	2.37	61.5%	83%▲10
44	Valeri NichushkinFCOL	COL	60.9%	1,153	4.01	2.57	61.0%	60%
45	Andrei SvechnikovFCAR	CAR	60.9%	1,002	4.18	2.69	61.1%	97%▲8.5
46	Nikolaj EhlersFWPG	WPG	60.9%	1,302	3.67	2.36	60.4%	93%▲7.9
47	Gabriel VilardiFWPG	WPG	60.9%	799	3.79	2.44	60.2%	80%▲7.7
48	Sam BennettFFLA	FLA	60.8%	1,146	4.00	2.58	61.2%	83%▲4
49	Sebastian AhoFCAR	CAR	60.6%	1,531	4.15	2.70	60.5%	99%▲0.9
50	Morgan FrostFPHI	PHI	60.6%	1,124	3.29	2.14	61.1%	23%▲17
51	Filip ForsbergFNSH	NSH	60.5%	1,550	4.27	2.79	61.2%	99%▲1.4
52	Adam FoxDNYR	NYR	60.5%	1,688	3.82	2.50	60.8%	99%▲6.5
53	J.T. MillerFVAN	VAN	60.3%	1,579	4.10	2.69	61.0%	92%▲7.9
54	Ben HuttonDVGK	VGK	60.2%	667	2.89	1.91	60.5%	—
55	Vincent TrocheckFNYR	NYR	60.1%	1,759	4.30	2.86	60.5%	88%▼5.5
56	Rickard RakellFPIT	PIT	60.1%	1,156	3.71	2.47	60.5%	82%▲9.9
57	Brayden PointFTBL	TBL	60.0%	1,623	4.45	2.97	60.1%	95%▲7.7
58	Jack DruryFCAR	CAR	59.9%	909	2.87	1.92	60.5%	5.6%▲5.4
59	Valtteri PuustinenFPIT	PIT	59.9%	611	2.99	2.00	60.8%	—
60	Sidney CrosbyFPIT	PIT	59.9%	1,647	4.79	3.21	60.4%	100%▲2.4
61	Drake BathersonFOTT	OTT	59.8%	1,490	3.59	2.41	60.4%	97%▲7.4
62	Mark ScheifeleFWPG	WPG	59.8%	1,494	3.95	2.65	59.3%	100%
63	William NylanderFTOR	TOR	59.8%	1,634	4.34	2.93	60.2%	99%▼0.9
64	Matt DucheneFDAL	DAL	59.7%	1,341	3.80	2.56	60.3%	62%▼17
65	Tyler BertuzziFTOR	TOR	59.7%	1,284	3.62	2.44	60.3%	62%▼3.2
66	Elias PetterssonFVAN	VAN	59.7%	1,611	4.06	2.75	60.5%	87%▼5.8
67	Jordan SpenceDLAK	LAK	59.6%	1,024	3.06	2.08	59.7%	21%▲17
68	Roman JosiDNSH	NSH	59.5%	2,020	3.91	2.66	59.9%	98%
69	Jonathan MarchessaultFVGK	VGK	59.3%	1,467	3.66	2.51	59.6%	24%▼21
70	Pavel DorofeyevFVGK	VGK	59.3%	624	2.90	1.99	59.5%	92%▲1.7
71	Jamie BennFDAL	DAL	59.2%	1,240	3.89	2.68	59.4%	22%▲18
72	Teuvo TeravainenFCAR	CAR	59.2%	1,247	3.53	2.43	59.7%	13%▲8.6
73	Cole PerfettiFWPG	WPG	59.1%	965	2.87	1.99	59.1%	36%▲32
74	Seth JarvisFCAR	CAR	59.1%	1,519	3.69	2.56	59.5%	95%▲1.5
75	Declan ChisholmDMIN/WPG	MIN/WPG	59.0%	512	2.79	1.94	59.7%	0.1%
76	Shea TheodoreDVGK	VGK	58.9%	1,038	3.42	2.38	59.4%	91%▲28
77	Kevin ShattenkirkDBOS	BOS	58.9%	963	3.13	2.19	59.4%	—
78	Oliver Ekman-LarssonDFLA	FLA	58.8%	1,472	3.36	2.35	59.5%	43%▲32
79	Thomas ChabotDOTT	OTT	58.8%	1,175	3.40	2.38	59.1%	83%▼11
80	Nino NiederreiterFWPG	WPG	58.8%	1,152	2.94	2.06	58.8%	4.0%▲3.8
81	Mattias EkholmDEDM	EDM	58.8%	1,663	3.84	2.69	59.1%	77%▲9.7
82	Artturi LehkonenFCOL	COL	58.6%	831	3.91	2.76	58.8%	54%▲37
83	Steven StamkosFTBL	TBL	58.6%	1,440	4.07	2.87	58.9%	94%▼3.7
84	Mason MarchmentFDAL	DAL	58.6%	1,220	3.30	2.33	59.1%	47%▲34
85	Erik KarlssonDPIT	PIT	58.4%	1,990	3.95	2.81	58.9%	95%
86	Dylan StromeFWSH	WSH	58.4%	1,472	3.66	2.61	58.5%	82%▲10
87	Jalen ChatfieldDCAR	CAR	58.3%	1,094	2.69	1.93	58.1%	4.1%▲3.4
88	Casey MittelstadtFBUF/COL	BUF/COL	58.3%	1,418	3.53	2.52	58.1%	14%▲12
89	Mitch MarnerFTOR	TOR	58.2%	1,468	4.13	2.97	58.6%	99%▲1.1
90	Tyler ToffoliFNJD/WPG	NJD/WPG	58.2%	1,340	4.18	3.00	57.8%	38%▼17
91	Marco RossiFMIN	MIN	58.1%	1,372	3.04	2.19	58.6%	58%▲12
92	Jack HughesFNJD	NJD	58.1%	1,300	4.11	2.96	57.9%	100%
93	Vince DunnDSEA	SEA	57.9%	1,362	3.06	2.22	58.5%	88%▲11
94	Mark StoneFVGK	VGK	57.9%	1,087	3.75	2.72	57.9%	93%▲9
95	Nick PaulFTBL	TBL	57.8%	1,415	3.68	2.68	58.1%	8.1%▲7.7
96	Pius SuterFVAN	VAN	57.6%	1,038	2.78	2.05	57.9%	4.1%▲3.3
97	Evgenii DadonovFDAL	DAL	57.6%	672	2.89	2.13	57.6%	0.1%
98	Nazem KadriFCGY	CGY	57.5%	1,511	3.76	2.78	57.6%	70%▼9.7
99	Thomas HarleyDDAL	DAL	57.4%	1,660	3.06	2.27	57.6%	88%▲8.9
100	Michael AmadioFVGK	VGK	57.4%	978	2.81	2.09	57.6%	3.3%▲2.6

Minimum sample: 492 min (it scales with games played and sequence coverage). 307 below the minimum are hidden.

How to read it

Read it like xGF%: 50% is even. Compare it with the plain xGF% column to see whose numbers are inflated by flurries.

How reliable is it?

Shares the stability of on-ice xGF%. The flurry discount itself is mechanical (no fitted parameters), so it adds no noise.

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