Returning regular · 2025-26

Michael Eyssimont

Boston Bruins · C · L shot · age 30 at the dated snapshot.

Team: BOS
Lane: C
Age: 30

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.

Games56

Goals8

Assists10

Points18

Even strength14all even-strength points

Power play4

Short handed0

Empty net1

5v5 points / 601.384

Average TOI10:55

PP TOI0:39

PK TOI0:02

Organizational context

11 C regulars in BOS

The all-team median is 7. BOS’s farm system ranks #18 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 shots155

Expected goals9.66.2% per shot

Goals8

Finishing−1.6goals 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 quality53%2.94 expected goals for and 2.64 against per 60 minutes

Drive, three seasons (ranking measure)+1.4 pts5v5 chance share against his club without him, over this season and the two before (weights 1 / 0.7 / 0.4; 564 / 771 / 916 minutes) · 80% band -1.0 to 3.5 pts. This is the number the rankings order by: it predicts next season better than one season does.

Drive, this season only+4 ptsat 5-on-5, over 9.4 hours: his share minus the club's share when he is off the ice · with its 80% band: 3.8 pts, 80% band -0.4 to 8.3 pts

Setup (chances his passes produced)1.57 xG5 primary assists; 0.315 expected goals per pass on average, 0.154 per 60. A pass to a tap-in counts more than a pass to a point shot.

Drive, offence+0.14expected goals created per 60 with him on, minus the club without him

Drive, defence+0.26expected goals suppressed per 60 with him on, against the club without him; positive means fewer chances allowed

Isolated impact (RAPM), offence / defence+0.01 / +0.04expected goals per 60 at 5v5 with every teammate, opponent, score state, venue and zone start held as columns of one ridge regression; ± 0.10 / 0.10 SE over 564 minutes

Offensive-zone starts44%of his faceoffs in either end zone; heavy offensive use lifts the numbers above

Chance share, score-and-venue adjusted (5v5)49%raw 49%; 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)-0.6raw -1.4 on 155 shots; the data carries 24% of the weight, the league the rest (about 580 shots to weight the player one half)

Company (linemates / opponents)average / averageteammates +1.4, opponents -0.5 points of relative chance share, weighted by seconds shared

Start +/- (goals begun on his shift)191 of them were scored after he had changed off

On the ice for25 for, 24 againstthe plus/minus view, for comparison

Giveaways that led to a goal0his own turnovers inside the sequence, over 10.2 hours on the ice

Line or pairLine 4his rank by 5-on-5 ice time within the club, most common over 34 games

LinematesSean Kuraly, Fraser Mintenshared 43% and 35% of his 5-on-5 time

Matchup51% vs first lines51% against the other club's first line (usual)

Shutdown (drive, defence, deployment removed)+0.16offence the same way +0.33; zone starts, opponent quality and first-line share removed by a league-wide fit

Two-way (offence + defence, deployment removed)+0.49chances created minus chances allowed per 60, against his club without him; a fast-paced player can allow more and still win here

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

Matchup rating (Elo)1497above the league middle · results by who he faced over 9.4 hours of 5-on-5; not enough time against top-rated opponents to say

Finish rating (Elo)1488below the league middle · 8 goals on 154 shots worth 9.4 expected; rated against the goalies and the chances actually faced, so the same goal counts more against a strong goalie from a poor spot

In the play-by-play38.2 per 60how often the league's feed names him on the puck (the moments a caller says a name): 94th percentile of forwards · in the play a lot and drives it; the feed never records carries or passes that do not end in a goal

Power-play rating (Elo)1426above the league middle · 7.58 expected goals per 60 on the power play over 37 minutes, rated against the penalty-killers faced (league rate 8.27)

Shot-lane rating (Elo)1590above the league middle · 153 of 191 attempts got through (80%), rated against the defenders on the ice

Lane defence rating (Elo, unit)1495above the league middle · 185 of 615 attempts faced were blocked while he was on (30%); shared with the four others on the ice

Turnover balance-3.24 per 604 takeaways, 37 giveaways (15 in his own end) over 10.2 hours; recorded by the league's scorers, so counts differ by rink

Blocked shots and hits3.0 · 5.1 per 6030 blocks, 52 hits given, 49 taken, 14 penalties taken

Real points171 empty-net point left out · 8 goals, 5 primary and 5 secondary assists · 4 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)7.0from 8 goals · 0 clutch (worth twice an average goal or more) · +7.0 above the chances they came from

Advanced primary assists (aA1)3.6from 5 primary assists

Advanced secondary assists (aA2)4.3from 5 secondary assists; kept apart, not guessed at

Advanced points14.9against 18 scoresheet points

Advanced plus/minus (a+/-)-2.1on the ice for 26 goals for and 24 against, each weighted by what it changed · 1 of the goals for began on his shift after he had changed off · -0.20 per 60

Shot map · 2025-26

Where he shoots from, and how it goes

Every unblocked shot placed by distance, angle and shot type. His rate from each spot is set against the league's rate for the same shot from the same spot, so the edge is against everyone else taking that shot, not against an average.

His office (most-used spot)—fewer than 15 shots from any one spot

Crease (under 15 ft and inside 45°)7% of shots2 goals on 11 shots (18.2%, expected 14.6%)

Slot (under 30 ft and inside 40°)10% of shots3 goals on 15 shots (20.0%, expected 10.9%)

Circles (under 40 ft, wider)58% of shots2 goals on 90 shots (2.2%, expected 6.0%)

Point (40 ft and beyond inside 30°)4% of shots0 goals on 6 shots (0.0%, expected 4.2%)

Wall (40 ft and beyond, wide)21% of shots1 goals on 32 shots (3.1%, expected 1.7%)

Wrist shots8.3%6 of 72; expected 6.5%

Snap shots0.0%0 of 40; expected 5.6%

Wrap-around shots0.0%0 of 15; expected 4.9%

Backhand shots9.1%1 of 11; expected 4.3%

From the video · model judgement

What the condensed games showed

Read from the league's free condensed games (1 game so far): entries, exits, battles and turnovers the model could see and attribute to his jersey number. A condensed game is a sample of the game, so these are observations, not season counts.

goals9over 1 game read

shots1over 1 game read

Career arc · era-adjusted points per 82 games

Michael Eyssimont, 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 file52021-22 to 2025-26

Best season24.32025-26, age 29, 56 games

Latest season24.3expected −0.1 to 31.5

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 0 NHL hours through his age-25 season (80% band 0 to 14). He logged 0 hours, between the median and the 90th percentile.

Every season

Season	Age	GP	Level	Expected (80%)
2021-22	25	1	0.0	−36.7 to 121.1
2022-23	26	54	20.6	7.7 to 66.5
2023-24	27	81	23.4	5.4 to 37.5
2024-25	28	77	16.1	6.1 to 35.9
2025-26	29	56	24.3	−0.1 to 31.5

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

Comparables at the same age

Who looked like this at 26 to 29

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 seasons6 of 8

Their later peak (adjusted pts/game)0.34median across the comparables; this player's own line is below

This player so far: age 14 14U AAA 1.46 → age 15 T1EHL U16 0.78 → age 17 USHL 0.52 → age 18 USHL 0.78 → age 19 NCAA 0.82 → age 20 NCAA 0.83 → age 21 NCAA 1.00 → age 22 AHL 0.32 → age 23 AHL 0.50 → age 24 AHL 0.42 → age 25 AHL 0.72 → age 26 NHL 0.26 → age 27 NHL 0.31 → age 28 NHL 0.17 → age 29 NHL 0.32

All 3 columns shown

Player	Distance (lower is closer)	What he did afterwards
Dominic ToninatoC · matched at ages 26, 28, 29	0.01	age 30 NHL 0.33 → age 31 AHL 0.27 → age 32 AHL 0.38
Cameron LundC · matched at ages 26, 27, 28	0.03	age 30 ACH 0.32 → age 31 ACH 0.14
Michael McCarronC · matched at ages 27, 28, 29	0.04	age 30 NHL 0.19 → age 31 NHL 0.20
Sean KuralyC · matched at ages 26, 27, 28, 29	0.04	age 30 NHL 0.28 → age 31 NHL 0.29 → age 32 NHL 0.21 → age 33 NHL 0.27
Garnet HathawayR · matched at ages 26, 27, 28, 29	0.05	age 30 NHL 0.34 → age 31 NHL 0.27 → age 32 NHL 0.21 → age 33 NHL 0.31 → age 34 NHL 0.04
Radek FaksaC · matched at ages 26, 27, 28, 29	0.06	age 30 NHL 0.26 → age 31 NHL 0.21 → age 32 NHL 0.29
Justin DowlingC · matched at ages 26, 27, 28, 29	0.07	age 30 NHL 0.18 → age 31 NHL 0.18 → age 32 AHL 0.37 → age 33 AHL 0.32 → age 34 NHL 0.14
Jordan MartinookL · matched at ages 26, 27, 28, 29	0.07	age 30 NHL 0.41 → age 31 NHL 0.39 → age 32 NHL 0.46 → age 33 NHL 0.38

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

Metropolitan

Central

Pacific