His repeatable signature. A step only appears if its reliability score (sample size × match coverage) reaches at least Medium confidence — pattern mining, not anecdotes.
Signature: Link → Finish → Convert — 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: receive / move — 3× (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.
20 tracked carries (3.2/90, 9 progressive, 292m 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.
progressivelateral/short% of his 20 carries originating in each zone · own goal left, attacking →
Every carry, exact path — median 11m, longest 56m
By zone — where the carry started → where it ended
First (20)grouped by carry pattern (start + end position), largest group first
startendown goal left, attacking →
Dribbles (1v1s)
Does he drop to link or stay central? Touches by channel. Season rate: 2.11 dribbles attempted /90.
brighter band = more dribbles · own goal left, attacking →
INSIDE 85%
OUTSIDE 15%
Dribble location — 11 inside · 2 outside
Right Wing
2 · 15%
Right Half-Space
2 · 15%
Central
3 · 23%
Left Half-Space
6 · 46%
Left Wing
0 · 0%
Vision AI His touches are central and high — a penalty-box striker.
04
How does he create chances?
6 key passes (0.97/90), 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:
chance creation (6)assist (0)% of the selected group's key passes originating in each zone
Box deliveries — key passes reaching the final third, by flank
from the left (5)from the right (1)of 6 key passes landing in the final thirdattacking ↑ toward goal
02
Can he finish?
15 shots · 2.08 xG · 2 goals (-0.08 vs xG). Mostly left-footed. Circle size = xG.
shots 15 on target 8 goals 2 xG 2.08 xG on target 1.54 xG / shot 0.139
Where he shoots from — click an area of the box
05
Where does he become vulnerable?
31 possessions lost. Directness has a cost — the question is where. Attacking-third losses are cheap; deep ones are dangerous.
16
attacking third (cheap)
15
middle third
0
own third (dangerous)
Vision AI 52% of losses are in the attacking third — he gambles where a turnover costs little.
Mistake danger (proxy): 0.93 danger-index/90 · 0.185 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 6 key passes, his busiest route is a delivery where he plays straight through the central in the final 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 right half-space in the middle third: 38% of his 13 touches there are lost (5 times).
51 defensive actions across the season (8.3/90). 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 →
0
tackles · 0% won
0
interceptions · 0.0/90
9
recoveries · 1.5/90
4
clearances · 0.6/90
1
blocks
55
avg action height
13
won high (att 3rd)
33
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. 9 proactive ball-wins (1.5/90), tackle success 0%. He defends high, stepping out to intercept — suits a high line.
Defensive work in the attacking half
Ball-wins in the attacking half only (own half vs. attacking half, a simple 2-way split, not the usual thirds) — the clearest single signal of how much he presses and recovers the ball high up the pitch.
31
attacking-half actions · 5.04/90
07
Against Different Pressing Intensities
Split by how hard the opponent pressed (opponent PPDA — lower = more intense press). Does Rasmus Højlund keep producing when the game is tight and the press is high? Tiers of ~2-4 matches — directional evidence, not proof.
press tier
xT /90
prog /90
key P /90
dribble%
High press (PPDA 3.6, 2m)
0.36
2.7
1.37
0%
Mid press (PPDA 4.7, 2m)
0.07
0.0
2.09
75%
Low press (PPDA 7.7, 4m)
0.13
0.0
0.00
20%
Team
Team context
How much of the team runs through him, and what changes without him.
Insufficient sample: Rasmus Højlund played nearly every Napoli match in the loaded data, so there is no meaningful "without him" comparison yet. This section fills in as more matches (and the rest of the season) are scraped.
10
His Share of the Team
Of everything Napoli produced this season, the categories where the biggest share ran through Rasmus Højlund specifically — his share of the team total, not a per-90 rate.
category
share
him / team
expected goals (xG)
19%
12.2 / 66.0
goals
15%
2 / 13
shots
12%
75 / 635
Recruitment
Style and verdict
Style profile, closest comparables, and the summary.
Not raw event counts — football style scores: weighted models scored 0–100 against the 299 strikers 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.
Why Target man 49%: a weighted blend of box threat 54 (weight 50%) + retention 61 (weight 20%) + final-third threat 34 (weight 30%). The score reflects how distinctively his profile SHAPE matches this archetype vs his peers.
aerial ability, marking, off-ball running — not in event data
Carrying
64
Retention
61
Receiving zones
57
Box threat
54
Creativity
53
Crossing
50
Press resistance ~proxy
46
Defensive aggression
46
Build-up
40
Transition ~proxy
35
Dribbling
35
Final-third threat
34
Progression
32
Territory (wide vs central)
21
Through-balls ~proxy
20
Line-breaking
18
Passing directness
11
Ball-winning
6
12
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: 191 cm / 86 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 Serie A, Bundesliga, La Liga, Ligue 1 and Premier League, 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.
Emmanuel Emegha Strasbourg · Ligue 1 25/26 · 196 cm / 82 kg
71%
Driven by: ball-winning (24%), passing directness (14%), line-breaking (11%). Different mechanism — Højlund more via defensive aggression, creativity; Emegha more via retention, build-up.
Brian Brobbey Sunderland · Premier League · 181 cm / 80 kg
69%
Driven by: ball-winning (22%), territory (wide vs central) (11%), passing directness (11%). Different mechanism — Højlund more via carrying, dribbling.
Patrik Schick Leverkusen · Bundesliga · 191 cm / 86 kg
69%
Driven by: ball-winning (22%), through-balls (11%), line-breaking (11%). Different mechanism — Højlund more via carrying, defensive aggression; Schick more via build-up, box threat.
Serhou Guirassy Borussia Dortmund · Bundesliga · 187 cm / 82 kg
68%
Driven by: ball-winning (29%), passing directness (19%), territory (wide vs central) (13%). Different mechanism — Højlund more via carrying; Guirassy more via box threat, receiving zones.
Robert Lewandowski Barcelona · La Liga 25/26 · 186 cm / 81 kg
67%
Driven by: ball-winning (21%), line-breaking (13%), through-balls (12%). Different mechanism — Højlund more via carrying; Lewandowski more via build-up, retention.
William Osula Newcastle · Premier League · 180 cm / 81 kg
66%
Driven by: passing directness (21%), line-breaking (16%), through-balls (14%). Different mechanism — Højlund more via receiving zones, creativity; Osula more via ball-winning, crossing.
13
Summary
Rasmus Højlund is a target man: 2 goals from 2.08 xG (-0.08 vs expected), 15 shots, 3 box entries. Under-performing his chances. [Off-ball runs, aerials and hold-up under contact need tracking/aerial data.]
Based on 8 matches (554 minutes) — an early read, treat with caution until more minutes are logged. He profiles as a target man — best used in a system built around that identity.
His biggest room for growth is final-third threat — even a modest improvement there would meaningfully lift his overall target man 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.