All 776 players Nashville Predators desk

Returning regular · 2025-26

Matthew Wood

Nashville Predators · RW · R shot · age 21 at the dated snapshot.

Team: NSH
Lane: RW
Age: 21

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.

Games71

Goals17

Assists13

Points30

Even strength21all even-strength points

Power play8

Short handed1

Empty net0

5v5 points / 601.701

Average TOI12:19

PP TOI1:33

PK TOI0:00

Organizational context

2 RW regulars in NSH

The all-team median is 3.5. NSH’s farm system ranks #7 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 shots149

Expected goals11.67.8% per shot

Goals17

Finishing+5.4goals 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 quality49%3.09 expected goals for and 3.17 against per 60 minutes

Drive, three seasons (ranking measure)-3.8 pts5v5 chance share against his club without him, over this season and the two before (weights 1 / 0.7 / 0.4; 741 / 22 / 0 minutes) · 80% band -8.0 to 0.2 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 12.3 hours: his share minus the club's share when he is off the ice · with its 80% band: -3.6 pts, 80% band -7.7 to 0.4 pts

Setup (chances his passes produced)1.51 xG10 primary assists; 0.151 expected goals per pass on average, 0.103 per 60. A pass to a tap-in counts more than a pass to a point shot.

Drive, offence-0.12expected goals created per 60 with him on, minus the club without him

Drive, defence-0.27expected goals suppressed per 60 with him on, against the club without him; positive means fewer chances allowed

Isolated impact (RAPM), offence / defence-0.05 / -0.12expected 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 741 minutes

Offensive-zone starts68%of his faceoffs in either end zone; heavy offensive use lifts the numbers above

Chance share, score-and-venue adjusted (5v5)45%raw 45%; 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)+0.3raw +3.2 on 149 shots; the data carries 19% of the weight, the league the rest (about 580 shots to weight the player one half)

Company (linemates / opponents)average / averageteammates -1.5, opponents +0.1 points of relative chance share, weighted by seconds shared

Start +/- (goals begun on his shift)463 of them were scored after he had changed off

On the ice for54 for, 42 againstthe plus/minus view, for comparison

Giveaways that led to a goal1his own turnovers inside the sequence, over 14.6 hours on the ice

Line or pairLine 3his rank by 5-on-5 ice time within the club, most common over 36 games

LinematesFilip Forsberg, Fedor Svechkovshared 38% and 34% of his 5-on-5 time

Matchup45% vs first lines45% against the other club's first line (sheltered)

Shutdown (drive, defence, deployment removed)-0.29offence the same way -0.14; zone starts, opponent quality and first-line share removed by a league-wide fit

Two-way (offence + defence, deployment removed)-0.43chances created minus chances allowed per 60, against his club without him; a fast-paced player can allow more and still win here

Penalty kill0%not used on the penalty kill · 0 shorthanded minutes

Matchup rating (Elo)1432below the league middle · results by who he faced over 12.3 hours of 5-on-5; 70% chance share against the strongest opponents he saw most (Kiefer Sherwood)

Finish rating (Elo)1536top tenth of the league · 17 goals on 149 shots worth 12.3 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-play26.8 per 60how often the league's feed names him on the puck (the moments a caller says a name): 42th percentile of forwards; the feed never records carries or passes that do not end in a goal

Faceoff rating (Elo)1433bottom tenth of the league · 73–113 (39%), rated by whom he beat and lost to

Power-play rating (Elo)1341below the league middle · 6.38 expected goals per 60 on the power play over 110 minutes, rated against the penalty-killers faced (league rate 8.27)

Shot-lane rating (Elo)1555above the league middle · 142 of 182 attempts got through (78%), rated against the defenders on the ice

Lane defence rating (Elo, unit)1364above the league middle · 234 of 833 attempts faced were blocked while he was on (28%); shared with the four others on the ice

Turnover balance-2.19 per 609 takeaways, 41 giveaways (12 in his own end) over 14.6 hours; recorded by the league's scorers, so counts differ by rink

Blocked shots and hits2.1 · 3.6 per 6030 blocks, 52 hits given, 56 taken, 7 penalties taken · faceoffs 39% of 186

Real points300 empty-net points left out · 17 goals, 10 primary and 3 secondary assists · 8 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)14.2from 17 goals · 0 clutch (worth twice an average goal or more) · +15.2 above the chances they came from

Advanced primary assists (aA1)10.9from 10 primary assists

Advanced secondary assists (aA2)3.2from 3 secondary assists; kept apart, not guessed at

Advanced points28.4against 30 scoresheet points

Advanced plus/minus (a+/-)+10.6on the ice for 58 goals for and 44 against, each weighted by what it changed · 3 of the goals for began on his shift after he had changed off · +0.72 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°)17% of shots5 goals on 25 shots (20.0%, expected 13.7%)

Slot (under 30 ft and inside 40°)15% of shots4 goals on 23 shots (17.4%, expected 11.2%)

Circles (under 40 ft, wider)52% of shots8 goals on 77 shots (10.4%, expected 7.4%)

Point (40 ft and beyond inside 30°)9% of shots0 goals on 14 shots (0.0%, expected 3.2%)

Wall (40 ft and beyond, wide)7% of shots0 goals on 10 shots (0.0%, expected 1.4%)

Wrist shots9.5%7 of 74; expected 7.1%

Snap shots17.9%5 of 28; expected 10.2%

Tip-in shots15.4%2 of 13; expected 7.8%

Backhand shots0.0%0 of 12; expected 9.6%

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.

shots8over 1 game read

goals5over 1 game read

board battles won2over 1 game read

entries carried in1over 1 game read

Most often up against: D. Hildeby (7), M. Callahan (1), J. Pelletier (1), N. Abruzzese (1), C. Geekie (1).

Career arc · era-adjusted points per 82 games

Matthew Wood, 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 file22024-25 to 2025-26

Best season32.02025-26, age 20, 71 games

Latest season32.0expected 12.8 to 62.1

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 74 NHL hours through his age-25 season (80% band 7 to 119). He logged 16 hours, between the 10th percentile and the median. The model was applied to his draft class without being fitted or tested on it.

Every season

Season	Age	GP	Level	Expected (80%)
2024-25	19	6	12.9	1.3 to 84.7
2025-26	20	71	32.0	12.8 to 62.1

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

Comparables at the same age

Who looked like this at 18 to 21

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 seasons7 of 8

Their later peak (adjusted pts/game)0.61median across the comparables; this player's own line is below

This player so far: age 15 CSSHL U15 2.37 → age 16 BCHL 0.72 → age 17 BCHL 1.85 → age 18 NCAA 0.97 → age 19 NCAA 0.80 → age 20 NCAA 1.00 → age 21 NHL 0.42

All 3 columns shown

Player	Distance (lower is closer)	What he did afterwards
Pavel ZachaC · matched at ages 18, 19, 20, 21	0.02	age 22 NHL 0.49 → age 23 NHL 0.70 → age 24 NHL 0.51 → age 25 NHL 0.69 → age 26 NHL 0.76
Jack RoslovicC · matched at ages 18, 19, 20, 21	0.03	age 22 NHL 0.31 → age 23 NHL 0.41 → age 24 NHL 0.71 → age 25 NHL 0.56 → age 26 NHL 0.57
Kaapo KakkoR · matched at ages 18, 19, 20, 21	0.03	age 22 NHL 0.49 → age 23 NHL 0.31 → age 24 NHL 0.47 → age 25 NHL 0.61
Jakob PelletierL · matched at ages 18, 19, 20, 21	0.04	age 22 NHL 0.29 → age 23 NHL 0.23 → age 24 NHL 0.46 → age 25 AHL 0.56
Ayrton MartinoL · matched at ages 18, 19, 20, 21	0.04	age 22 NCAA 0.50 → age 23 AHL 0.13
Tanner LaczynskiC · matched at ages 18, 19, 20, 21	0.05	age 22 NCAA 0.36 → age 23 AHL 0.33 → age 24 AHL 0.28 → age 25 NHL 0.13 → age 26 AHL 0.41
Vincent TrocheckC · matched at ages 18, 19, 20, 21	0.05	age 22 NHL 0.70 → age 23 NHL 0.66 → age 24 NHL 0.92 → age 25 NHL 0.62 → age 26 NHL 0.66
Alex Barré-BouletC · matched at ages 18, 19, 20, 21	0.05	age 22 AHL 0.42 → age 23 NHL 0.20 → age 24 NHL 0.36 → age 25 AHL 0.55 → age 26 NHL 0.25

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