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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 hits. 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 hits · assumes no games missed

#
Skater
PTS
G
A
HIT
BLK
PIM
101
Logan MaillouxSTL · D0.88×
18.7
7.3
11.3
103.5
1.4
115.7
71.5
60.0
84
102
Artyom LevshunovCHI · D1.32×
28.3
2.4
25.9
102.9
12.3
115.3
89.7
53.6
84
103
Jeff PetryMIN · D1.33×
17.3
1.8
15.5
79.5
2.7
112.3
108.8
32.5
84
104
Jaycob MegnaVGK · D1.22×
9.4
1.6
7.8
63.8
0.6
110.6
118.1
36.1
84
105
Brandon CarloSTL · D1.25×
12.9
2.0
10.9
94.8
0.1
110.3
152.3
47.1
84
106
Tyler MyersDAL · D1.19×
19.1
3.2
15.9
104.1
0.4
110.2
146.9
70.6
84
107
Erik GudbransonCBJ · D1.16×
15.4
3.4
12.0
89.3
0.2
109.9
149.3
53.6
84
108
Colton ParaykoSTL · D1.21×
29.2
9.1
20.1
135.7
1.0
109.7
190.6
23.4
84
109
Ilya SolovyovPIT · D0.66×
21.3
2.8
18.5
66.5
0.4
109.1
94.3
39.8
84
110
Louie BelpedioWSH · D12 sample
32.2
14.1
18.1
103.8
3.3
108.6
103.2
16.3
84
111
Declan CarlilePIT · D1.94×
10.8
3.1
7.7
72.1
0.3
108.4
72.8
82.7
84
112
Dysin MayoCBJ · D1.43×
20.2
4.5
15.8
104.8
1.5
108.4
102.2
27.8
84
113
Samuel BolducOTT · D1.19×
16.0
6.8
9.1
87.5
2.8
107.3
85.7
20.2
84
114
Zach BogosianMIN · D1.14×
13.7
3.6
10.1
75.5
0.1
107.1
80.4
47.2
84
115
Joshua MahuraSEA · D1.37×
13.8
2.1
11.8
70.0
0.2
106.8
96.7
44.7
84
116
Oliver Ekman-LarssonTOR · D1.23×
35.5
7.4
28.1
121.7
8.1
106.7
81.9
68.5
84
117
Uvis BalinskisFLA · D1.06×
20.9
6.5
14.5
99.7
6.6
106.6
64.3
49.2
84
118
Matt BenningTOR · D1.30×
18.2
1.6
16.5
74.7
1.1
106.3
129.4
42.8
84
119
Drew HellesonANA · D1.09×
20.1
3.8
16.3
67.6
0.2
106.1
100.3
39.9
84
120
Rasmus DahlinBUF · D1.26×
76.2
20.1
56.1
226.4
24.9
105.8
103.7
79.3
84
121
Travis HamonicDET · D1.16×
11.5
2.4
9.1
97.6
0.3
105.7
135.0
61.0
84
122
Ethan Del MastroCHI · D1.46×
12.3
3.4
9.0
76.9
0.5
105.2
85.6
55.1
84
123
Jonas SiegenthalerNJD · D1.51×
15.9
1.3
14.6
71.6
0.2
104.9
141.6
51.0
84
124
Simon EdvinssonDET · D1.09×
30.0
9.5
20.5
98.2
0.8
104.8
162.8
79.6
84
125
Seth JonesFLA · D1.38×
45.3
11.3
34.0
161.8
20.5
103.5
123.0
33.8
84
126
Rasmus SandinWSH · D1.16×
31.6
5.2
26.4
95.5
3.6
101.5
135.2
20.2
84
127
Victor ManciniVAN · D0.99×
16.0
2.7
13.3
62.1
1.4
100.1
97.5
26.3
84
128
Brandon MontourSEA · D1.15×
43.7
15.1
28.6
227.7
13.3
99.9
100.8
65.9
84
129
Alexander AlexeyevPIT · D1.30×
8.3
1.9
6.3
60.8
0.7
99.7
120.9
19.8
84
130
Jacob MacDonaldWSH · D1.68×
19.2
10.2
9.0
119.0
6.1
99.7
52.7
44.8
84
131
Adam GinningVGK · D16 sample
9.9
5.0
4.9
56.8
1.7
99.6
82.0
10.4
84
132
Max CrozierTBL · D0.92×
21.1
2.5
18.6
111.5
0.4
98.2
73.7
83.5
84
133
Dougie HamiltonNJD · D1.12×
48.5
14.5
33.9
235.1
17.3
97.4
92.8
51.0
84
134
Cole HutsonWSH · D14 sample
57.3
17.5
39.8
132.2
27.3
96.9
40.1
47.1
84
135
Scott MayfieldNYI · D1.21×
15.7
2.9
12.8
77.8
0.1
96.0
115.4
72.1
84
136
Emil AndraeCBJ · D0.95×
18.8
2.6
16.2
61.1
2.5
95.7
84.1
32.3
84
137
Artem ZubOTT · D1.17×
27.3
4.9
22.4
91.8
0.4
95.0
137.0
54.0
84
138
Ryan GravesPIT · D1.11×
12.0
3.8
8.2
84.9
0.2
93.6
136.6
36.9
84
139
Ryker EvansSEA · D1.17×
25.0
8.0
17.0
66.1
2.9
93.3
101.8
44.0
84
140
Marcus PetterssonNYR · D1.30×
23.6
3.4
20.2
79.5
0.1
93.3
148.8
54.4
84
141
Justin FaulkDET · D1.56×
41.4
11.9
29.5
158.4
10.6
92.7
134.4
43.5
84
142
Ian ColeCHI · D1.30×
20.0
2.9
17.1
67.0
0.1
92.1
167.2
61.9
84
143
Charles Alexis LegaultCAR · D13 sample
15.3
7.0
8.3
90.2
1.0
91.7
71.6
83.9
84
144
Derrick PouliotCHI · D24 sample
24.1
5.8
18.3
92.3
3.4
90.8
83.2
33.5
84
145
Ryan PulockNYI · D1.34×
28.5
4.7
23.9
109.8
2.7
90.0
169.7
17.5
84
146
Timothy LiljegrenWSH · D1.20×
22.7
4.7
18.1
94.8
4.7
89.7
146.1
43.9
84
147
Tristan LuneauANA · D14 sample
28.9
17.3
11.7
118.4
2.1
89.0
88.3
32.2
84
148
Sam DickinsonSJS · D0.93×
16.9
1.5
15.4
85.3
0.2
86.8
81.3
26.1
84
149
Michael KesselringSJS · D1.08×
19.9
4.4
15.5
126.1
1.5
85.4
78.3
97.2
84
150
Adam PelechNYI · D1.22×
21.2
3.3
17.9
126.4
0.4
84.0
130.0
47.6
84

The × tag compares a skater’s game-to-game swings with chance: under 1.00 steadier, over 1.00 streakier.

How consistency is measured — repeat-measure reliability 0.20, measured 2026-10-02

The tag is a skater’s game-to-game variance in hits divided by the variance a random (Poisson) process at his own rate would show, measured within each season so a change of role between seasons does not count as inconsistency.

Its repeat-measure reliability, 0.20, is how closely the figure from a skater’s odd-numbered games matches the one from his even-numbered games, across skaters. A stat is shown only once that agreement is clearly above what chance produces.

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