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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 assists. Each total assumes the skater plays every one of his team’s games.

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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 assists · assumes no games missed

#
Skater
PTS
G
A
HIT
BLK
PIM
251
Ryan JohnsonBUF · D
13.4
1.7
11.7
67.3
1.0
34.9
82.2
40.0
84
252
Tristan LuneauANA · D14 sample
28.9
17.3
11.7
118.4
2.1
89.0
88.3
32.2
84
253
Jacob BrysonDET · D
14.0
2.4
11.6
39.0
0.6
31.1
68.8
17.1
84
254
Ben ChiarotDET · D
16.7
5.2
11.5
111.9
0.5
162.8
161.2
71.2
84
255
William LagessonDET · D
13.1
1.6
11.4
59.7
0.9
129.0
99.9
56.4
84
256
Theo LindsteinSTL · D17 sample
21.7
10.3
11.4
102.5
0.9
46.3
69.2
30.8
84
257
Brayden PachalCGY · D
14.4
3.0
11.4
69.5
0.2
186.9
92.0
95.7
84
258
Logan MaillouxSTL · D
18.7
7.3
11.3
103.5
1.4
115.7
71.5
60.0
84
259
Elias SalomonssonWPG · D32 sample
14.6
3.3
11.3
99.3
3.0
129.6
70.8
32.0
84
260
Zayne ParekhCGY · D39 sample
21.1
9.8
11.3
111.7
12.5
40.4
69.1
20.2
84
261
Kevin KorchinskiCHI · D
15.3
4.2
11.2
80.8
3.9
27.9
84.3
27.9
84
262
Dylan CoghlanVGK · D
15.8
4.8
11.1
104.9
1.4
68.2
89.5
32.1
84
263
Brandon CarloSTL · D
12.9
2.0
10.9
94.8
0.1
110.3
152.3
47.1
84
264
Alexander PetrovicFLA · D
14.3
3.4
10.9
83.6
0.2
145.8
108.2
63.0
84
265
Justin HollWSH · D
13.9
3.2
10.7
53.9
0.2
80.9
94.8
34.3
84
266
Jeremy LauzonVGK · D
12.8
2.1
10.7
90.3
0.1
323.0
117.8
97.7
84
267
Libor HajekSJS · D33 sample
18.3
7.7
10.6
92.1
3.3
83.4
95.7
32.7
84
268
Troy StecherTOR · D
14.2
3.6
10.6
79.5
0.1
53.7
95.4
31.5
84
269
Luke SchennVAN · D
12.4
1.8
10.6
78.7
0.1
288.9
101.1
71.4
84
270
Spencer StastneyEDM · D
13.3
2.8
10.5
69.0
0.2
19.7
83.0
23.6
84
271
Zach BogosianMIN · D
13.7
3.6
10.1
75.5
0.1
107.1
80.4
47.2
84
272
Vincent IorioNYR · D37 sample
10.9
0.9
10.0
54.1
0.5
49.8
112.7
35.0
84
273
Nick SeelerPHI · D
13.8
3.9
9.9
93.8
0.1
139.6
182.3
59.9
84
274
Riley StillmanEDM · D
12.4
2.8
9.7
57.7
0.9
145.7
73.9
52.4
84
275
Carson SoucyCBJ · D
14.8
5.3
9.5
78.7
0.5
128.3
107.7
48.0
84
276
Steven SantiniTBL · D18 sample
11.4
1.9
9.5
46.3
1.1
70.9
60.8
18.0
84
277
Ian MooreANA · D
15.0
5.6
9.5
87.8
0.2
70.9
93.0
28.1
84
278
Dmitri SimashevUTA · D29 sample
10.3
0.9
9.4
87.1
0.5
43.2
77.8
62.9
84
279
Joshua BrownEDM · D
13.5
4.2
9.4
78.6
0.5
176.6
136.3
100.6
84
280
Connor MackeyCHI · D39 sample
15.3
5.9
9.4
82.9
1.6
120.3
70.7
122.4
84
281
Joel HanleyCGY · D
10.5
1.3
9.3
55.3
0.1
67.9
114.5
47.9
84
282
Haydn FleuryWPG · D
12.3
3.0
9.3
70.6
0.2
116.1
120.5
30.6
84
283
Alec RegulaEDM · D
10.3
1.0
9.3
58.4
0.5
49.9
124.2
96.4
84
284
Jake BeanCGY · D
13.6
4.4
9.2
92.4
0.7
31.4
98.5
35.6
84
285
Samuel BolducOTT · D
16.0
6.8
9.1
87.5
2.8
107.3
85.7
20.2
84
286
Travis HamonicDET · D
11.5
2.4
9.1
97.6
0.3
105.7
135.0
61.0
84
287
Jacob MacDonaldWSH · D
19.2
10.2
9.0
119.0
6.1
99.7
52.7
44.8
84
288
Ethan Del MastroCHI · D
12.3
3.4
9.0
76.9
0.5
105.2
85.6
55.1
84
289
Vincent DesharnaisWSH · D
10.2
1.2
8.9
50.4
0.1
153.2
133.4
88.1
84
290
Albert JohanssonDET · D
12.1
3.5
8.5
64.7
0.1
68.6
99.9
23.4
84
291
Keaton MiddletonCOL · D
9.6
1.1
8.5
82.9
0.7
141.5
69.6
62.5
84
292
Kyle CapobiancoDAL · D
14.1
5.7
8.4
53.4
0.7
40.3
68.0
29.5
84
293
Charles Alexis LegaultCAR · D13 sample
15.3
7.0
8.3
90.2
1.0
91.7
71.6
83.9
84
294
Ryan GravesPIT · D
12.0
3.8
8.2
84.9
0.2
93.6
136.6
36.9
84
295
Jacob Bernard-DockerDET · D
10.8
2.6
8.1
60.3
0.2
81.0
123.9
33.6
84
296
Jake ChristiansenCBJ · D
8.9
0.9
8.0
62.9
0.3
66.6
66.8
15.4
84
297
Elias PetterssonVAN · D
11.6
3.6
8.0
58.5
0.2
153.0
97.7
49.0
84
298
Zach MetsaBUF · D
13.6
5.7
7.8
48.3
2.0
12.8
64.5
32.5
84
299
Cale FleurySEA · D
11.1
3.3
7.8
85.5
0.5
178.5
127.7
20.1
84
300
Jaycob MegnaVGK · D
9.4
1.6
7.8
63.8
0.6
110.6
118.1
36.1
84

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