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Projections

Every headline stat per skater over the full 2026-27 regular season (Sep 29, 2026 – Apr 10, 2027), ranked by power-play 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.

322 defencemen · sorted by power-play points · assumes no games missed

#
Skater
PTS
G
A
HIT
BLK
PIM
251
Alexander PetrovicFLA · D
14.3
3.4
10.9
83.6
0.2
145.8
108.2
63.0
84
252
Justin HollWSH · D
13.9
3.2
10.7
53.9
0.2
80.9
94.8
34.3
84
253
Dylan DeMeloWPG · D
22.0
3.5
18.5
82.1
0.2
134.9
115.6
41.7
84
254
Adam WilsbyNSH · D
22.2
2.1
20.1
89.9
0.2
45.1
72.2
37.7
84
255
Sam DickinsonSJS · D
16.9
1.5
15.4
85.3
0.2
86.8
81.3
26.1
84
256
Haydn FleuryWPG · D
12.3
3.0
9.3
70.6
0.2
116.1
120.5
30.6
84
257
Matthew RobertsonNYR · D
21.2
7.1
14.1
93.2
0.2
123.7
92.0
43.6
84
258
Ian MooreANA · D
15.0
5.6
9.5
87.8
0.2
70.9
93.0
28.1
84
259
Nikolas MatinpaloOTT · D
8.3
0.9
7.4
67.5
0.2
72.5
86.2
31.3
84
260
Erik GudbransonCBJ · D
15.4
3.4
12.0
89.3
0.2
109.9
149.3
53.6
84
261
Joshua MahuraSEA · D
13.8
2.1
11.8
70.0
0.2
106.8
96.7
44.7
84
262
Brett KulakCOL · D
18.5
3.5
15.1
103.8
0.2
41.5
116.2
33.7
84
263
Ryan GravesPIT · D
12.0
3.8
8.2
84.9
0.2
93.6
136.6
36.9
84
264
Nikita ZadorovBOS · D
22.9
4.4
18.5
110.6
0.2
215.3
99.8
157.2
84
265
John MarinoUTA · D
33.0
4.4
28.6
64.8
0.2
34.1
88.0
19.2
84
266
Dante FabbroCBJ · D
18.8
6.7
12.1
91.0
0.2
79.5
144.7
41.1
84
267
Nicolas HagueNSH · D
17.8
4.3
13.5
90.8
0.2
117.9
92.8
57.9
84
268
Alexander RomanovNYI · D
20.4
4.3
16.0
119.5
0.2
185.2
184.0
29.7
84
269
Spencer StastneyEDM · D
13.3
2.8
10.5
69.0
0.2
19.7
83.0
23.6
84
270
Lian BichselDAL · D
13.6
6.2
7.4
58.3
0.2
264.2
61.2
74.7
84
271
Trevor van RiemsdykPIT · D
19.0
2.7
16.3
69.8
0.2
18.1
138.0
26.1
84
272
Jonas SiegenthalerNJD · D
15.9
1.3
14.6
71.6
0.2
104.9
141.6
51.0
84
273
Noah JuulsenCOL · D
13.2
1.4
11.7
46.2
0.2
197.4
98.5
33.5
84
274
Mario FerraroWPG · D
21.6
6.1
15.5
96.7
0.2
146.1
157.0
40.8
84
275
Brayden PachalCGY · D
14.4
3.0
11.4
69.5
0.2
186.9
92.0
95.7
84
276
Elias PetterssonVAN · D
11.6
3.6
8.0
58.5
0.2
153.0
97.7
49.0
84
277
Mikey AndersonLAK · D
22.5
4.5
18.0
79.4
0.2
139.1
139.9
26.2
84
278
Tyler TuckerSTL · D
19.0
4.8
14.3
80.1
0.2
142.0
69.0
104.4
84
279
Drew HellesonANA · D
20.1
3.8
16.3
67.6
0.2
106.1
100.3
39.9
84
280
Johnathan KovacevicNJD · D
18.3
2.2
16.1
77.7
0.2
117.5
99.0
74.3
84
281
Jacob Bernard-DockerDET · D
10.8
2.6
8.1
60.3
0.2
81.0
123.9
33.6
84
282
Louis CrevierBUF · D
23.1
7.1
16.0
111.2
0.2
136.5
111.2
56.5
84
283
Dmitry KulikovFLA · D
16.0
3.4
12.7
74.9
0.1
149.8
89.6
51.6
84
284
Brandon CarloSTL · D
12.9
2.0
10.9
94.8
0.1
110.3
152.3
47.1
84
285
Vincent DesharnaisWSH · D
10.2
1.2
8.9
50.4
0.1
153.2
133.4
88.1
84
286
Kaiden GuhleMTL · D
27.3
5.5
21.8
91.8
0.1
177.3
165.2
97.3
84
287
Emil LillebergTBL · D
18.4
3.7
14.8
62.4
0.1
158.2
74.6
95.4
84
288
Olli MaattaMIN · D
24.8
3.7
21.1
69.6
0.1
32.7
124.1
13.4
84
289
Joel HanleyCGY · D
10.5
1.3
9.3
55.3
0.1
67.9
114.5
47.9
84
290
Jayden StrubleMTL · D
15.6
2.9
12.7
50.5
0.1
176.3
57.6
80.6
84
291
Albert JohanssonDET · D
12.1
3.5
8.5
64.7
0.1
68.6
99.9
23.4
84
292
Zach BogosianMIN · D
13.7
3.6
10.1
75.5
0.1
107.1
80.4
47.2
84
293
Luke SchennVAN · D
12.4
1.8
10.6
78.7
0.1
288.9
101.1
71.4
84
294
Nick JensenANA · D
22.8
4.3
18.4
72.1
0.1
67.8
104.9
20.9
84
295
Adam LarssonSEA · D
25.9
7.0
18.9
116.6
0.1
133.5
160.8
42.2
84
296
Connor CliftonBOS · D
15.2
3.1
12.1
73.6
0.1
256.8
130.3
71.6
84
297
Jake MiddletonCGY · D
20.1
4.5
15.6
84.9
0.1
119.3
152.6
69.9
84
298
Parker WotherspoonVGK · D
24.3
2.5
21.8
71.8
0.1
160.3
116.2
55.3
84
299
Jeremy LauzonVGK · D
12.8
2.1
10.7
90.3
0.1
323.0
117.8
97.7
84
300
Troy StecherTOR · D
14.2
3.6
10.6
79.5
0.1
53.7
95.4
31.5
84

No consistency figure for power-play points: it has not passed the check this site requires, which is not a finding that power-play 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 2026-10-02.

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