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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
701
Luke KuninFLA · C
17.8
9.1
8.7
95.5
1.3
182.9
70.3
69.7
84
702
Max JonesEDM · L
17.7
8.5
9.3
98.2
0.6
208.9
19.6
56.8
84
703
Yan KuznetsovCGY · D
17.7
6.0
11.8
95.5
0.3
117.2
145.2
39.7
84
704
Zach WhitecloudCGY · D
17.7
3.4
14.3
75.9
0.1
140.6
138.1
38.5
84
705
Arttu HyryDAL · R30 sample
17.7
9.2
8.5
95.5
0.6
141.7
30.7
35.7
84
706
Akil ThomasVAN · C32 sample
17.7
8.2
9.4
79.7
1.0
131.6
43.4
25.7
84
707
Ty DellandreaSJS · C
17.6
4.0
13.6
85.5
0.2
198.9
57.3
53.9
84
708
Erik CernakTBL · D
17.6
3.6
14.0
78.2
0.1
185.6
124.1
75.9
84
709
Josh SamanskiEDM · C30 sample
17.6
8.5
9.1
36.1
0.5
85.2
39.8
27.9
84
710
Cole SmithCHI · R
17.6
8.6
8.9
86.8
0.1
242.4
29.5
65.7
84
711
Andrew PeekeUTA · D
17.5
3.9
13.6
87.1
0.1
127.4
151.5
32.8
84
712
Isaak PhillipsWPG · D
17.5
4.8
12.7
87.3
1.0
121.2
71.4
46.2
84
713
Brenden DillonNJD · D
17.4
3.5
14.0
61.1
0.1
209.8
108.6
83.5
84
714
Sheldon DriesDET · C
17.4
9.9
7.5
97.1
4.8
116.2
54.1
24.8
84
715
Nikita OkhotiukCGY · D
17.3
4.0
13.4
87.4
1.0
223.6
115.8
67.7
84
716
Joakim KemellNSH · R18 sample
17.3
5.9
11.4
84.8
0.9
130.3
49.3
4.3
84
717
Jeff PetryMIN · D
17.3
1.8
15.5
79.5
2.7
112.3
108.8
32.5
84
718
Zachary Aston-ReesePHI · C
17.2
6.0
11.2
87.5
0.2
207.7
56.2
31.9
84
719
Justin KirklandMIN · C
17.1
5.8
11.3
48.5
0.5
139.4
50.3
21.0
84
720
Jaroslav ChmelarNYR · R30 sample
17.1
10.7
6.4
74.5
0.5
151.8
43.6
29.6
84
721
Georgii MerkulovCOL · C11 sample
17.0
4.1
12.9
89.1
2.4
47.5
73.7
23.7
84
722
Nathan BastianDAL · R
17.0
9.9
7.0
73.4
0.8
202.3
34.0
49.1
84
723
Sam DickinsonSJS · D
16.9
1.5
15.4
85.3
0.2
86.8
81.3
26.1
84
724
Ethan CardwellSJS · R13 sample
16.9
12.9
4.0
81.7
1.4
108.2
29.0
27.8
84
725
Egor ZamulaCBJ · D
16.9
3.0
13.8
55.3
2.4
31.6
107.9
16.2
84
726
Cody CeciLAK · D
16.9
2.5
14.3
78.5
0.1
69.3
126.2
17.4
84
727
Niko MikkolaFLA · D
16.9
4.8
12.1
97.8
0.3
156.7
103.1
71.9
84
728
Samuel HonzekCGY · L24 sample
16.9
8.0
8.8
110.3
0.7
166.2
36.5
13.6
84
729
Dennis CholowskiNYR · D
16.8
3.8
13.0
66.2
2.5
17.2
77.0
16.8
84
730
Isac LundeströmCBJ · C
16.8
5.7
11.1
68.1
0.6
49.7
54.2
7.1
84
731
Henry ThrunWPG · D
16.7
3.5
13.2
77.8
2.0
57.4
97.1
33.7
84
732
Ryan LindgrenSEA · D
16.7
3.1
13.6
65.9
0.1
79.6
132.7
56.4
84
733
Donovan SebrangoFLA · D
16.6
0.6
16.0
67.5
0.4
148.3
62.8
128.8
84
734
Joel KivirantaDAL · L
16.6
8.5
8.1
87.1
0.1
124.4
34.0
18.9
84
735
Mathew DumbaPIT · D
16.6
4.1
12.5
88.5
1.1
140.4
99.3
82.3
84
736
Victor SoderstromBOS · D
16.5
1.9
14.6
87.7
3.1
39.1
105.4
23.6
84
737
Isaiah GeorgeNYI · D37 sample
16.5
3.1
13.3
67.9
0.7
39.1
80.8
14.8
84
738
Ben ChiarotDET · D
16.5
5.1
11.4
111.6
0.5
163.9
161.2
70.3
84
739
Zemgus GirgensonsTBL · C
16.4
7.6
8.8
93.4
0.3
200.4
51.3
36.3
84
740
Caleb JonesPIT · D
16.4
3.1
13.3
81.1
2.0
124.8
119.4
31.1
84
741
Oscar Fisker MolgaardSEA · C13 sample
16.3
1.9
14.4
51.4
1.1
64.2
54.0
16.6
84
742
Dylan GambrellMIN · C
16.3
6.4
9.8
78.5
1.2
102.5
60.7
38.1
84
743
Marc GatcombVGK · C
16.3
8.3
8.0
97.7
0.2
325.8
41.9
23.1
84
744
Adam ErneDAL · L
16.2
10.1
6.2
82.1
0.3
244.2
41.0
34.0
84
745
Nolan AllanSJS · D
16.2
2.8
13.4
45.0
0.7
121.8
91.1
29.6
84
746
Tomas NosekFLA · L
16.1
4.4
11.7
66.9
0.7
141.2
42.3
21.5
84
747
Ivan IvanBOS · C
16.0
7.7
8.3
65.5
4.6
31.1
19.4
18.9
84
748
Dmitry KulikovFLA · D
16.0
3.4
12.7
74.9
0.1
149.8
89.6
51.6
84
749
Victor ManciniVAN · D
16.0
2.7
13.3
62.1
1.4
100.1
97.5
26.3
84
750
Scott SabourinTBL · R39 sample
16.0
3.5
12.5
40.2
0.5
201.0
36.2
251.6
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