العربيةSeason2025/262026 · this seasonAll seasons
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Player reportPremier League 2026

Mason Mount

Man Utd
Advanced playmaker CM
2.77 key passes/90 · 0.29 xT/90
central midtransition runnerprogressionbox-to-box
130
minutes
3 apps
2
progressive passes
1.4/90
80%
pass completion
81 passes
4
key passes
2.77/90
1
box entries
0.69/90
8
final-3rd entries
5.5/90
6
dribbles
83% won
17
possessions lost
2.59 danger/90
Overview

The player

Who he is and how he compares with his peers.

Thin sample this season. Mason Mount has played 3 matches (130 min) so far, so sections that need a bigger sample to say anything reliable are not shown here — they are missing on purpose, not broken. See all seasons combined →
01

What Patterns Define Him?

His repeatable signature. A step only appears if its reliability score (sample size × match coverage) reaches at least Medium confidence — pattern mining, not anecdotes.

RECEIVE
Left Half-Space, Middle third — mostly in the middle third (53% of his 81 passes)
80% completion · 81× in 3/3 matches
↓
PROGRESS
drive or pass into the final third
1.4 prog/90 · 2× in 2/3 matches
↓
CREATE / FINISH
cut-back or shot from inside
2.77 key passes /90 · 0 goals · 9× in 2/3 matches
Signature: Receive → Progress → Create / Finish — the only steps proven repeatable this season (3 of 4 in the full pattern). Not enough evidence yet for the rest of the chain below.
Not a pattern: arrive — 1× (low confidence, shown as a number only, no tactical conclusion drawn) — omitted from the signature rather than presented as one.
In possession

In possession

What he does with the ball: build-up, progression, creation, carrying and finishing.

04

How dangerous are his carries?

Real ball-carries

15 tracked carries (10.4/90, 1 progressive, 125m total) — the ball moving with his touches, a different signal from dribbles below (beating an opponent 1v1). this counts every touch-carry, not just his dribble attempts below — a fuller picture of how much of his ball-carrying is control-and-move rather than beating a man.

20%13%7%20%7%13%20%
progressivelateral/short% of his 15 carries originating in each zone · own goal left, attacking →
Every carry, exact path — median 8m, longest 14m
By zone — where the carry started → where it ended
1Outside → Inside
5Inside → Inside
6Outside → Outside
3Inside → Outside
First (15)grouped by carry pattern (start + end position), largest group first
startendown goal left, attacking →

Dribbles (1v1s)

Only 6 dribbles attempted this season — too little volume to map a location pattern.

02

How does he create chances?

4 key passes (2.77/90), 0 of them assists — where his chance creation specifically comes from, 18 zones (6×3), showing his share of key passes originating there.

Chance creation — key pass origin zone share
Filter:
67%33%
chance creation (3)assist (0)% of the selected group's key passes originating in each zone
05

Where does he become vulnerable?

17 possessions lost. Directness has a cost — the question is where. Attacking-third losses are cheap; deep ones are dangerous.

8
attacking third (cheap)
7
middle third
2
own third (dangerous)
Vision AI 47% of losses are in the attacking third — he gambles where a turnover costs little.
Mistake danger (proxy): 2.59 danger-index/90 · 0.220 avg/turnover. Weights each loss by threat conceded at that location. True xT-conceded needs the opponent possession model; positional proxy.
03

Tactical patterns — how his football actually looks

Three clustered sequence diagrams from his own event data — his three busiest routes: the way he progresses, the pattern behind his chances, and where a turnover turns dangerous.

own goal left, attacking →
Where do his chances come from?
Of 4 key passes, his busiest route is a delivery where he switches it out from the central to the left hs, advancing it forward in the middle third — 2 times. The receiver isn't identifiable in this data, only where the pass started and ended.
Where does he lose the ball most?
This is a RATE, not a raw count — the share of his touches in each zone that end in a turnover, so a zone he simply plays in more often doesn't automatically look riskier. His weakest zone is the left wing in the final third: 38% of his 8 touches there are lost (3 times).
—1 touch · too few—4 touches · too few—1 touch · too few—1 touch · too few—0 touches · too few22%2/9 lost—5 touches · too few11%1/9 lost9%1/11 lost0%0/9 lost—6 touches · too few—5 touches · too few—7 touches · too few—5 touches · too few38%3/8 lostRWRHSCLHSLWDEFMIDATT
Out of possession

Out of possession

Where and how he defends and presses.

06

How & where does he defend?

24 defensive actions across the season (16.6/90). Where he wins the ball, and how — the core question for a defender. Aerial duels are not recorded for this competition, so aerial dominance is not measured here.

click a card to isolate that action type · own goal left, attacking →
3
tackles · 33% won
2
interceptions · 1.4/90
9
recoveries · 6.2/90
0
clearances · 0.0/90
0
blocks
49
avg action height
3
won high (att 3rd)
16
middle third
5
own third
Vision AI A front-foot / high defender. He wins the ball mainly by stepping in — tackles, interceptions & recoveries outweigh clearances. 14 proactive ball-wins (9.7/90), tackle success 33%. He defends high, stepping out to intercept — suits a high line.
Team

Team context

How much of the team runs through him, and what changes without him.

07

Impact on the Team

Man Utd's output per match with Mason Mount (3 matches) versus without him (5 matches). Insufficient evidence — the without-sample is only 5 matches; treat every Δ below as noise until more games exist. A proper on-off model needs many more minutes.

WITH (3)
WITHOUT (5)
Δ
goals for /match
0.67
1.60
-0.93
xG for /match
1.38
0.48
+0.90
goals against /match
1.67
1.60
+0.07
points /match
0.67
1.00
-0.33
08

His Share of the Team

Of everything Man Utd produced this season, the categories where the biggest share ran through Mason Mount specifically — his share of the team total, not a per-90 rate.

category
share
him / team
zone-14 entries
4%
19 / 524
successful take-ons
3%
12 / 344
tackles
3%
27 / 820
Recruitment

Style and verdict

Style profile, closest comparables, and the summary.

09

Playing-Style Profile

Not raw event counts — football style scores: weighted models scored 0–100 against the 383 central mids in the pool (min 450 min). This is what separates players who post similar numbers but play differently. Proxy dimensions are inferred from on-ball data; true off-ball running / under-pressure context is not measured.

Transition runner71%Box-to-box runner69%Advanced playmaker (8)68%Deep-lying playmaker42%
Why Transition runner 71%: a weighted blend of transition 53 (weight 45%) + defensive aggression 78 (weight 30%) + box threat 75 (weight 25%). The score reflects how distinctively his profile SHAPE matches this archetype vs his peers.
Elite
receiving zones
Strong
line-breaking, defensive aggression, passing directness, box threat, ball-winning, through-balls
Average
dribbling, final-third threat, creativity, territory (wide vs central), progression, press resistance, transition, crossing, retention
Weak
build-up, carrying
Unknown
aerial ability, marking, off-ball running — not in event data
Receiving zones
86
Line-breaking
85
Defensive aggression
78
Passing directness
76
Box threat
75
Ball-winning
74
Through-balls ~proxy
71
Dribbling
69
Final-third threat
68
Creativity
65
Territory (wide vs central)
64
Progression
57
Press resistance ~proxy
54
Transition ~proxy
53
Crossing
45
Retention
43
Build-up
30
Carrying
23
10

Similar Players — in style space

Closest comparables measured on the style scores above, within the same position family — so it separates how players are alike, not just how much they touch the ball. Each line explains the shared strength and the divergent mechanism. A light physical-profile term (height / weight; this player: 181 cm / 76 kg) is included at a quarter of one style axis's weight — enough to break ties between stylistic twins, not to override style. Scope: pooled across the Premier League, Bundesliga, La Liga, Ligue 1 and Serie A, percentiled within each player's own league before comparing — a match from another league is marked with its league name.

Style, not quality: a high match here means two players tend to produce the ball in similar ways — it is not a ranking, a scouting recommendation, or a claim that they are equally good.

Suat Serdar Hellas Verona · Serie A 25/26 · 184 cm / 75 kg
88%
Driven by: defensive aggression (12%), passing directness (12%), line-breaking (12%). Different mechanism — Mount more via receiving zones, retention; Serdar more via passing directness.
Lucas Paquetá West Ham 25/26 · 183 cm / 73 kg
76%
Driven by: defensive aggression (18%), receiving zones (13%), line-breaking (11%). Different mechanism — Mount more via retention, ball-winning; Paquetá more via carrying, creativity.
Laurin Curda Paderborn · Bundesliga · 185 cm / 84 kg
71%
Driven by: ball-winning (21%), receiving zones (13%), line-breaking (12%). Different mechanism — Mount more via passing directness, retention; Curda more via creativity.
Edu Expósito Espanyol · La Liga · 178 cm / 68 kg
71%
Driven by: line-breaking (14%), box threat (10%), passing directness (10%). Different mechanism — Mount more via receiving zones, territory (wide vs central); Expósito more via carrying, through-balls.
Patrick Wimmer Hoffenheim · Bundesliga · 182 cm / 77 kg
69%
Driven by: defensive aggression (22%), ball-winning (19%), build-up (14%). Different mechanism — Mount more via final-third threat, line-breaking; Wimmer more via carrying.
Henrikh Mkhitaryan Inter · Serie A · 177 cm / 75 kg
68%
Driven by: receiving zones (17%), box threat (14%), defensive aggression (14%). Different mechanism — Mkhitaryan more via carrying, transition.
11

Summary

Mason Mount is a transition runner / box-to-box runner: 1.4 progressive passes, 2.77 key passes /90, 1 box entries, 0 goals from 0.25 xG. A box-to-box contributor across every phase.

Based on 3 matches (130 minutes) — an early read, treat with caution until more minutes are logged. He profiles as a transition runner / box-to-box runner — best used in a system built around that identity. His biggest room for growth is transition — even a modest improvement there would meaningfully lift his overall transition runner profile.
✨ This report is interactive

Click any stat card to filter its map, hover a dot for the exact match, and use the dropdowns to slice passes zone-to-zone. Nothing here is static.

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