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Projections

Every headline stat per skater over the full 2026-27 regular season (Sep 29, 2026 – Apr 10, 2027), ranked league-wide by points. Each total assumes the skater plays every one of his team’s games.

Sort by
PTS PointsG GoalsA AssistsSOG Shots on goalPPP Power-play pointsHIT HitsBLK Blocked shotsPIM Penalty minutes
View
Average weekFull-season pace
Position
AllF ForwardsC CentresLW Left wingsRW Right wingsD Defencemen
How the shading works — a stronger shade means more, not better, and a skater on a thin sample is left plain

Each figure sits on a tile shaded by its share of that stat’s leading projection in this view, in five steps from zero to the leader. Hits and penalty minutes shade the way points do. The leader is the league’s, among skaters whose rate rests on at least 41 real games, so a shade means the same on every page and under every filter. A skater with fewer is left unshaded, his sample marked beside his name.

912 skaters · sorted by points · assumes no games missed

#
Skater
PTS
G
A
HIT
BLK
PIM
801
Jacob BrysonDET · D
14.0
2.4
11.6
39.0
0.6
31.1
68.8
17.1
84
802
Marc McLaughlinNJD · C35 sample
13.9
8.7
5.3
55.2
1.0
110.7
34.3
8.4
84
803
Justin HollWSH · D
13.9
3.2
10.7
53.9
0.2
80.9
94.8
34.3
84
804
Curtis LazarEDM · C
13.9
6.5
7.4
64.6
0.2
191.7
46.1
26.3
84
805
Jeff MalottANA · L
13.9
6.0
7.9
75.7
0.2
230.7
42.7
82.4
84
806
Joshua MahuraSEA · D
13.8
2.1
11.8
70.0
0.2
106.8
96.7
44.7
84
807
Nick SeelerPHI · D
13.8
3.9
9.9
93.8
0.1
139.6
182.3
59.9
84
808
Ross JohnstonSTL · L
13.8
3.1
10.7
41.8
0.1
247.3
31.1
136.6
84
809
Jonah GadjovichFLA · L
13.8
6.2
7.5
74.4
0.3
241.1
37.3
134.0
84
810
Zach BogosianMIN · D
13.7
3.6
10.1
75.5
0.1
107.1
80.4
47.2
84
811
Dakota MermisTOR · D
13.7
6.5
7.2
67.7
0.6
73.2
86.2
65.7
84
812
Adam EdstromNSH · C
13.6
7.9
5.7
72.3
0.2
154.8
53.9
21.2
84
813
Jake BeanCGY · D
13.6
4.4
9.2
92.4
0.7
31.4
98.5
35.6
84
814
Charles Alexis LegaultCAR · D14 sample
13.6
6.3
7.3
89.5
0.9
100.2
73.0
186.6
84
815
Tyson KozakBUF · C
13.6
5.0
8.6
58.2
0.9
192.0
70.5
5.2
84
816
Zach MetsaBUF · D
13.6
5.7
7.8
48.3
2.0
12.8
64.5
32.5
84
817
Joshua BrownEDM · D
13.5
4.2
9.4
78.6
0.5
176.6
136.3
100.6
84
818
Ryan LombergCBJ · L
13.4
5.7
7.7
81.8
0.3
170.8
23.4
79.9
84
819
Tobias BjornfotFLA · D
13.4
5.7
7.8
60.6
0.5
71.0
88.3
13.6
84
820
Jared DavidsonMTL · C10 sample
13.4
2.4
11.0
85.2
1.4
143.2
18.5
76.8
84
821
Tim WasheANA · C
13.4
3.5
9.9
67.8
0.3
221.2
56.5
28.5
84
822
Ryan JohnsonBUF · D
13.4
1.7
11.7
67.3
1.0
34.9
82.2
40.0
84
823
Colin WhiteNJD · C
13.3
5.5
7.8
70.8
2.3
75.7
39.6
16.3
84
824
Spencer StastneyEDM · D
13.3
2.8
10.5
69.0
0.2
19.7
83.0
23.6
84
825
Lian BichselDAL · D
13.3
6.1
7.2
57.0
0.2
263.9
61.7
73.0
84
826
Landon SlaggertCHI · L
13.3
5.1
8.2
78.6
0.2
109.0
37.4
32.8
84
827
Brian HalonenBOS · L19 sample
13.2
6.1
7.2
53.0
0.9
145.1
18.8
45.3
84
828
Noah JuulsenCOL · D
13.2
1.4
11.7
46.2
0.2
197.4
98.5
33.5
84
829
Samuel HeleniusLAK · C
13.1
6.9
6.2
58.7
0.2
232.6
27.0
73.1
84
830
William LagessonDET · D
13.1
1.6
11.4
59.7
0.9
129.0
99.9
56.4
84
831
Nolan FootePHI · L36 sample
13.0
8.1
4.9
90.9
0.8
157.2
30.2
29.8
84
832
Jake LucchiniNSH · C
12.9
5.5
7.5
64.5
1.8
61.9
29.8
43.6
84
833
Jacob QuillanTOR · C24 sample
12.9
4.4
8.5
56.6
0.7
161.3
48.2
9.8
84
834
Rasmus KupariWPG · C
12.8
5.8
7.1
76.2
0.6
103.0
41.1
20.8
84
835
Jansen HarkinsTBL · C
12.8
5.1
7.7
72.8
0.2
190.1
44.1
39.3
84
836
James HamblinANA · L
12.7
6.4
6.3
70.1
1.5
47.4
53.6
9.3
84
837
Brandon CarloSTL · D
12.7
1.9
10.8
93.5
0.1
109.9
151.3
63.8
84
838
Jonas RondbjergVGK · R
12.7
2.9
9.8
98.7
0.8
27.1
30.8
6.9
84
839
Jeremy LauzonVGK · D
12.6
2.1
10.5
92.7
0.1
320.6
116.1
96.2
84
840
Brennan OthmannCGY · L
12.6
6.0
6.6
76.1
3.1
178.2
34.4
33.3
84
841
Riley StillmanEDM · D
12.4
2.8
9.7
57.7
0.9
145.7
73.9
52.4
84
842
Luke SchennVAN · D
12.4
1.8
10.6
78.7
0.1
288.9
101.1
71.5
84
843
Travis MitchellANA · D10 sample
12.4
8.7
3.7
46.4
1.3
112.8
49.4
45.1
84
844
Lane PedersonLAK · C
12.3
4.9
7.4
55.5
1.4
209.1
45.8
33.4
84
845
Ethan Del MastroCHI · D
12.3
3.4
9.0
76.9
0.5
105.2
85.6
55.1
84
846
Haydn FleuryWPG · D
12.3
3.0
9.3
70.6
0.2
116.1
120.5
30.6
84
847
Jacob MelansonSEA · R38 sample
12.3
4.9
7.4
79.2
0.4
407.5
34.5
76.7
84
848
Ozzy WiesblattNSH · C
12.1
2.5
9.6
57.0
0.4
205.7
39.9
73.6
84
849
Kyle MacLeanNYI · C
12.1
4.2
7.8
64.2
0.1
147.3
31.8
39.4
84
850
Justin DowlingNYR · C
12.0
4.2
7.8
61.1
0.5
92.5
36.5
10.0
84

No consistency figure for points: it has not passed the check this site requires, which is not a finding that points is inconsistent.

Why there is no figure — a figure must repeat across a skater’s own games before it is shown

Before it shows a consistency figure, the site checks that the figure from a skater’s odd-numbered games agrees with the one from his even-numbered games, across skaters, more than chance would. That check withholds a figure both when a stat is measurably no better than chance and when the evidence is too split to call, and from here the two cannot be told apart. Sort by another stat to see the tag where it holds. Measured 2026-10-03.

How the projections are built — a rate over every season since 2021-22, recent games weighted more, times the club’s schedule

A skater’s rate is his per-game output over every season since 2021-22, each season weighted half as much as the one after it, and pulled a little toward the league rate so a handful of games cannot top the board. The weighting was chosen by testing which one best predicted the rest of a season from the games before it. A skater needs ten games to appear here.

Each total is that rate times his club’s scheduled games. A full-season pace counts every scheduled game, played or not.

Availability is not projected: Team games is the club’s schedule, not a forecast of how many games the player dresses for, and injuries, scratches and trades are not modelled.

How well does projection ordering hold? View the ordering study

Scoring per dollar of cap hit has a board of its own: Cap efficiency