Thin sample this season. Matthis Abline has played 7 matches (386 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 / MOVE
occupy the box, drop or run channel — mostly in the attacking third (73% of his 59 passes)
Signature: Receive / Move → Link → 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: convert — 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.
16 tracked carries (3.7/90, 8 progressive, 207m 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 16 carries originating in each zone · own goal left, attacking →
Every carry, exact path — median 9m, longest 40m
By zone — where the carry started → where it ended
First (16)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: 2.10 dribbles attempted /90.
brighter band = more dribbles · own goal left, attacking →
INSIDE 67%
OUTSIDE 33%
Dribble location — 6 inside · 3 outside
Right Wing
1 · 11%
Right Half-Space
0 · 0%
Central
3 · 33%
Left Half-Space
3 · 33%
Left Wing
2 · 22%
Vision AI His touches are central and high — a penalty-box striker.
03
How does he create chances?
4 key passes (0.93/90), 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 (4)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 (3)from the right (1)of 4 key passes landing in the final thirdattacking ↑ toward goal
02
Can he finish?
12 shots · 0.89 xG · 1 goals (+0.11 vs xG). Mostly right-footed. Circle size = xG.
shots 12 on target 5 goals 1 xG 0.89 xG on target 0.62 xG / shot 0.074
Where he shoots from — click an area of the box
05
Where does he become vulnerable?
20 possessions lost. Directness has a cost — the question is where. Attacking-third losses are cheap; deep ones are dangerous.
16
attacking third (cheap)
4
middle third
0
own third (dangerous)
Vision AI 80% of losses are in the attacking third — he gambles where a turnover costs little.
Mistake danger (proxy): 0.59 danger-index/90 · 0.127 avg/turnover. Weights each loss by threat conceded at that location. True xT-conceded needs the opponent possession model; positional proxy.
04
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 plays inside from the right hs toward the central in the final third — 1 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 half-space in the final third: 55% of his 11 touches there are lost (6 times).
07
Crosses & Cutbacks
2 crosses (0.47/90, 1 completed) and 6 cutbacks (1.40/90, 2 completed) — two separate wide-delivery patterns into the box, each its own map with a completed/failed filter. Neither is a tagged event, so both are inferred from pass geometry — the intended end point of a failed attempt is still real data (an interception doesn’t erase where the ball was aimed): a cross is a pass aimed into the box starting from a wide channel; a cutback starts almost on the byline but is squared back rather than driven forward, aimed centrally around the box. The completed-cross count was checked against this platform’s own real match-level cross totals and lands within about 1 cross of the true count.
Crosses — close-up on the final third
Cutbacks — close-up on the final third
completedfailed (dashed)close-up on the box — attacking ↑ toward goal
44 defensive actions across the season (10.3/90). 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 →
3
tackles · 67% won
3
interceptions · 0.7/90
9
recoveries · 2.1/90
2
clearances · 0.5/90
0
blocks
60
avg action height
21
won high (att 3rd)
17
middle third
6
own third
Vision AI A front-foot / high defender. He wins the ball mainly by stepping in — tackles, interceptions & recoveries outweigh clearances. 15 proactive ball-wins (3.5/90), tackle success 67%. 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 · 7.23/90
08
Against Different Pressing Intensities
Split by how hard the opponent pressed (opponent PPDA — lower = more intense press). Does Matthis Abline keep producing when the game is tight and the press is high? Tiers of ~2-3 matches — directional evidence, not proof.
press tier
xT /90
prog /90
key P /90
dribble%
High press (PPDA 4.1, 2m)
0.48
0.0
0.00
100%
Mid press (PPDA 6.4, 2m)
0.04
0.0
0.60
75%
Low press (PPDA 9.4, 3m)
0.42
1.4
1.36
100%
Team
Team context
How much of the team runs through him, and what changes without him.
Monaco's output per match with Matthis Abline (7 matches) versus without him (1 matches). Insufficient evidence — the without-sample is only 1 matches; treat every Δ below as noise until more games exist. A proper on-off model needs many more minutes.
WITH (7)
WITHOUT (1)
Δ
goals for /match
1.43
1.00
+0.43
xG for /match
1.03
0.00
+1.03
goals against /match
1.00
0.00
+1.00
points /match
1.71
3.00
-1.29
11
His Share of the Team
Of everything Monaco produced this season, the categories where the biggest share ran through Matthis Abline specifically — his share of the team total, not a per-90 rate.
category
share
him / team
successful take-ons
23%
59 / 252
shots
19%
97 / 503
expected goals (xG)
16%
8.7 / 55.8
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 Poacher 70%: a weighted blend of box threat 56 (weight 55%) + final-third threat 92 (weight 30%) + press resistance 60 (weight 15%). The score reflects how distinctively his profile SHAPE matches this archetype vs his peers.
aerial ability, marking, off-ball running — not in event data
Passing directness
95
Through-balls ~proxy
95
Carrying
93
Final-third threat
92
Progression
89
Territory (wide vs central)
88
Dribbling
88
Transition ~proxy
85
Line-breaking
81
Crossing
70
Creativity
69
Receiving zones
62
Ball-winning
61
Press resistance ~proxy
60
Box threat
56
Defensive aggression
40
Retention
29
Build-up
18
13
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: 176 cm / 67 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 Ligue 1, Bundesliga, La Liga, Premier League 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.
Nicolò Zaniolo Udinese · Serie A · 190 cm / 79 kg
86%
Driven by: transition (14%), final-third threat (11%), carrying (11%). Different mechanism — Zaniolo more via receiving zones, ball-winning.
Issa Soumaré Rennes · 182 cm / 78 kg
86%
Driven by: transition (14%), territory (wide vs central) (11%), passing directness (11%). Different mechanism — Abline more via dribbling, final-third threat; Soumaré more via build-up.
Sheraldo Becker Mainz · Bundesliga · 180 cm / 77 kg
81%
Driven by: progression (12%), transition (12%), through-balls (11%). Different mechanism — Abline more via dribbling, passing directness.
Karim Adeyemi Barcelona · La Liga · 180 cm / 75 kg
80%
Driven by: carrying (12%), progression (11%), final-third threat (10%). Different mechanism — Abline more via passing directness; Adeyemi more via receiving zones, build-up.
Randal Kolo Muani Juventus · Serie A · 187 cm / 73 kg
78%
Driven by: carrying (11%), through-balls (11%), territory (wide vs central) (11%). Different mechanism — Abline more via passing directness, box threat; Muani more via build-up, retention.
Cucho Hernández Real Betis · La Liga · 176 cm / 79 kg
77%
Driven by: transition (14%), progression (11%), passing directness (10%). Different mechanism — Hernández more via build-up, retention.
14
Summary
Matthis Abline is a poacher / transition runner: 1 goals from 0.89 xG (+0.11 vs expected), 12 shots, 4 box entries. A clinical finisher. [Off-ball runs, aerials and hold-up under contact need tracking/aerial data.]
Based on 7 matches (386 minutes) — an early read, treat with caution until more minutes are logged. He profiles as a poacher / transition runner — best used in a system built around that identity.
His biggest room for growth is box threat — even a modest improvement there would meaningfully lift his overall poacher 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.