Returning regular · 2025-26

Stefan Noesen

New Jersey Devils · RW · R shot · age 33 at the dated snapshot.

Team: NJD
Lane: RW
Age: 33

Production and deployment

How the points were built

Official NHL 2025–26 regular-season reports. A missing split stays “—”; it is never converted to zero.

Games38

Goals3

Assists4

Points7

Even strength4all even-strength points

Power play3

Short handed0

Empty net0

5v5 points / 600.64

Average TOI11:45

PP TOI1:37

PK TOI0:03

Organizational context

7 RW regulars in NJD

The all-team median is 3.5. NJD’s farm system ranks #19 in the maintained two-board consensus.

Thin lane

Questions a GM still has to answer

How much of the scoring comes at five-on-five, on special teams, or into an empty net?
Is the player creating the play, finishing it, or receiving a secondary point?
Does deployment reveal trust, scarcity, underuse, or a role the roster cannot offer?
What contract, acquisition cost, development alternative and Cup-window effect complete the argument?

Shot quality · 2025-26

Expected goals

CapOrCup's shot model (distance, angle, shot type, strength, rebounds, rushes), trained on 2023-24 and 2024-25 and tested on unseen games (AUC 0.756). Finishing is goals minus expected goals; it is noisy in one season.

Unblocked shots74

Expected goals7.19.6% per shot

Goals3

Finishing−4.1goals above expected

CapOrCup measures · 2025-26

What happens while he plays

Measured from the league's play-by-play and shift charts, not from the scoresheet. Expected goals are counted for every player on the ice, and a goal is credited to whoever was on the ice when the sequence began, including players who had changed off before it was scored.

Share of chance quality58%3.35 expected goals for and 2.43 against per 60 minutes

Drive, this season only—at 5-on-5, over 6.2 hours: his share minus the club's share when he is off the ice · with its 80% band: -5.1 pts, 80% band -10.6 to 0.0 pts

Setup (chances his passes produced)0.15 xG2 primary assists; 0.077 expected goals per pass on average, 0.021 per 60. A pass to a tap-in counts more than a pass to a point shot.

Drive, offence—expected goals created per 60 with him on, minus the club without him

Drive, defence—expected goals suppressed per 60 with him on, against the club without him; positive means fewer chances allowed

Isolated impact (RAPM), offence / defence-0.10 / +0.03expected goals per 60 at 5v5 with every teammate, opponent, score state, venue and zone start held as columns of one ridge regression; ± 0.11 / 0.11 SE over 375 minutes

Offensive-zone starts60%of his faceoffs in either end zone; heavy offensive use lifts the numbers above

Chance share, score-and-venue adjusted (5v5)43%raw 44%; each shot weighted by the league's share in that score state and venue, so trailing at home is not mistaken for tilting the ice

Finish, shrunk (goals above expected per 100 shots)-0.9raw -6.1 on 74 shots; the data carries 9% of the weight, the league the rest (about 580 shots to weight the player one half)

Company (linemates / opponents)average / averageteammates -1.4, opponents -0.6 points of relative chance share, weighted by seconds shared

Start +/- (goals begun on his shift)101 of them were scored after he had changed off

On the ice for13 for, 18 againstthe plus/minus view, for comparison

Giveaways that led to a goal0his own turnovers inside the sequence, over 7.4 hours on the ice

Line or pairLine 4his rank by 5-on-5 ice time within the club, most common over 21 games

LinematesPaul Cotter, Jesper Brattshared 36% and 17% of his 5-on-5 time

Matchup47% vs first lines47% against the other club's first line (sheltered)

Penalty kill1%1% of his club's penalty kill (rarely used) · 2 shorthanded minutes

Matchup rating (Elo)1466below the league middle · results by who he faced over 6.2 hours of 5-on-5; not enough time against top-rated opponents to say

Finish rating (Elo)1472bottom tenth of the league · 3 goals on 74 shots worth 7.5 expected; rated against the goalies and the chances actually faced, so the same goal counts more against a strong goalie from a poor spot

Power-play rating (Elo)1443above the league middle · 10.29 expected goals per 60 on the power play over 61 minutes, rated against the penalty-killers faced (league rate 8.27)

Shot-lane rating (Elo)1551above the league middle · 73 of 90 attempts got through (81%), rated against the defenders on the ice

Lane defence rating (Elo, unit)1457above the league middle · 122 of 401 attempts faced were blocked while he was on (30%); shared with the four others on the ice

Turnover balance-2.82 per 602 takeaways, 23 giveaways (4 in his own end) over 7.4 hours; recorded by the league's scorers, so counts differ by rink

Blocked shots and hits3.0 · 4.3 per 6022 blocks, 32 hits given, 26 taken, 12 penalties taken

Real points70 empty-net points left out · 3 goals, 2 primary and 2 secondary assists · 3 on the power play

How these measures are defined, and where they stop.

Advanced scoring · 2025-26

Goals, assists and plus/minus, weighted by what they changed

Every goal counts the win probability it added, scaled so the average goal is 1.0: a late tying goal counts several, a goal at 6-1 or into an empty net late counts little. Plus/minus also credits the players on the ice when the goal's sequence began.

Advanced goals (aG)2.3from 3 goals · 0 clutch (worth twice an average goal or more) · +2.2 above the chances they came from

Advanced primary assists (aA1)3.9from 2 primary assists

Advanced secondary assists (aA2)1.5from 2 secondary assists; kept apart, not guessed at

Advanced points7.7against 7 scoresheet points

Advanced plus/minus (a+/-)-2.1on the ice for 14 goals for and 18 against, each weighted by what it changed · 1 of the goals for began on his shift after he had changed off · -0.28 per 60

Career arc · era-adjusted points per 82 games

Stefan Noesen, season by season

Each dot is one season. The amber bar is what his earlier seasons and his age predicted for that season (80% band), so a dot far above its bar is a season the numbers did not see coming. The shaded band is where most players at his position sit at each age.

Seasons on file122014-15 to 2025-26

Best season40.82024-25, age 31, 78 games

Latest season13.9expected 16.3 to 50.5

Measured labels0none that passed its test

his seasonexpected range from his earlier seasonsposition, 10th to 90th percentile by ageposition median

Position band: quantile regression (10th/50th/90th) of qualifying seasons on an age spline, weighted for players who drop out.

What the numbers found

No shipped label applies. A label is a measured finding about a run of seasons (for example, a spike that came back to the old level), and it is shown only for label types that passed their test on held-out seasons.

Projection at the draft

At the draft the model expected 38 NHL hours through his age-25 season (80% band 4 to 100). He logged 33 hours, between the 10th percentile and the median.

Every season

Season	Age	GP	Level	Expected (80%)
2014-15	21	1	0.0	−38.3 to 123.6
2015-16	22	1	0.0	−38.3 to 121.5
2016-17	23	44	19.5	9.7 to 69.6
2017-18	24	72	29.9	7.3 to 40.8
2018-19	25	41	15.3	11.1 to 44.9
2019-20	26	40	17.7	3.7 to 38.8
2020-21	27	6	0.0	−15.2 to 51.6
2021-22	28	2	0.0	−42.3 to 69.4
2022-23	29	78	34.3	−9.5 to 30.3
2023-24	30	81	34.6	11.6 to 41.8
2024-25	31	78	40.8	13.8 to 43.4
2025-26	32	38	13.9	16.3 to 50.5

Source: CapOrCup careers desk (method and league curves) · as of 2026-09-29

Comparables at the same age

Who looked like this at 30 to 33

Careers are compared as league-adjusted points per game by age, over the same ages. The comparables below are the closest lines, and what matters is the spread of what they did next, not any single name.

Comparables found8

Went on to two or more NHL seasons8 of 8

Their later peak (adjusted pts/game)0.50median across the comparables; this player's own line is below

This player so far: age 14 Minor-TX 2.65 → age 15 MWEHL 1.45 → age 16 T1EHL 0.82 → age 17 OHL 0.24 → age 18 OHL 1.13 → age 19 OHL 1.30 → age 20 OHL 1.04 → age 22 AHL 0.59 → age 23 AHL 0.49 → age 24 NHL 0.17 → age 25 NHL 0.38 → age 26 NHL 0.20 → age 27 NHL 0.23 → age 28 AHL 0.50 → age 29 AHL 1.21 → age 30 NHL 0.46 → age 31 NHL 0.46 → age 32 NHL 0.53 → age 33 NHL 0.18

All 3 columns shown

Player	Distance (lower is closer)	What he did afterwards
Lars EllerC · matched at ages 30, 31, 32, 33	0.09	age 34 NHL 0.38 → age 35 NHL 0.41 → age 36 NHL 0.22
Nick FolignoL · matched at ages 30, 31, 32, 33	0.10	age 34 NHL 0.20 → age 35 NHL 0.43 → age 36 NHL 0.50 → age 37 NHL 0.45 → age 38 NHL 0.30
Nic DowdC · matched at ages 30, 31, 32, 33	0.15	age 34 NHL 0.33 → age 35 NHL 0.29
Erik HaulaL · matched at ages 30, 31, 32, 33	0.15	age 34 NHL 0.30 → age 35 NHL 0.47
Jordan StaalC · matched at ages 30, 31, 32, 33	0.18	age 34 NHL 0.42 → age 35 NHL 0.38 → age 36 NHL 0.48 → age 37 NHL 0.48
Mikael BacklundC · matched at ages 30, 31, 32, 33	0.19	age 34 NHL 0.68 → age 35 NHL 0.48 → age 36 NHL 0.42 → age 37 NHL 0.52
Anders LeeL · matched at ages 30, 31, 32, 33	0.20	age 34 NHL 0.66 → age 35 NHL 0.51
Corey PerryR · matched at ages 30, 31, 32, 33	0.20	age 34 NHL 0.37 → age 35 NHL 0.43 → age 36 NHL 0.49 → age 37 NHL 0.31 → age 38 NHL 0.56

Reading it

If most comparables became NHL regulars and their later peak sits well above this player's current line, the numbers say there is room to grow. If most did not, the numbers say the odds are against it. Either way it is what the data shows, not a name chosen to flatter.

Atlantic

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Pacific