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 middle third (55% of his 97 passes)
29 tracked carries (4.3/90, 8 progressive, 287m 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 29 carries originating in each zone · own goal left, attacking →
Every carry, exact path — median 7m, longest 32m
By zone — where the carry started → where it ended
First (20)Second (9)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: 2.79 dribbles attempted /90.
brighter band = more dribbles · own goal left, attacking →
INSIDE 74%
OUTSIDE 26%
Dribble location — 14 inside · 5 outside
Right Wing
3 · 16%
Right Half-Space
3 · 16%
Central
5 · 26%
Left Half-Space
6 · 32%
Left Wing
2 · 11%
Vision AI His touches are central and high — a penalty-box striker.
04
How does he create chances?
7 key passes (1.03/90), 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 (7)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 (4)from the right (3)of 7 key passes landing in the final thirdattacking ↑ toward goal
02
Can he finish?
16 shots · 2.30 xG · 7 goals (+4.70 vs xG). Mostly right-footed. Circle size = xG.
shots 16 on target 12 goals 7 xG 2.30 xG on target 3.61 xG / shot 0.144
Where he shoots from — click an area of the box
09
Where does he become vulnerable?
33 possessions lost. Directness has a cost — the question is where. Attacking-third losses are cheap; deep ones are dangerous.
18
attacking third (cheap)
15
middle third
0
own third (dangerous)
Vision AI 55% of losses are in the attacking third — he gambles where a turnover costs little.
Mistake danger (proxy): 0.99 danger-index/90 · 0.204 avg/turnover. Weights each loss by threat conceded at that location. True xT-conceded needs the opponent possession model; positional proxy.
07
Where Does He Operate? — Every Zone
Every on-ball touch (pass, dribble, shot) binned into a 6×3 grid — raw counts of where Gustavo Varela actually gets on the ball. Hover any zone for its breakdown by action type, its share of all touches, and the exact coordinate range it covers.
Filter:
own goal left, attacking → · brighter + higher number = more touches
Passes only — completed vs incomplete, by zone
Same zone grid, restricted to passes so completed/incomplete and match filters actually mean something — a shot or a dribble has no "completed" in the pass sense.
own goal left, attacking →
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 7 key passes, his busiest route is a delivery where he switches it out from the right hs to the left wing in the middle 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 right half-space in the final third: 36% of his 11 touches there are lost (4 times).
06
Does he carry inside or outside — his ball-carries across the pitch
Four arrows, one per movement class of his ball-carries: outside→inside (cutting in with the ball), inside→outside (carrying wide), outside→outside (down the touchline) and inside→inside (through the middle). Each count is measured from the carry's real start and end coordinates.
own goal left, attacking →
Across 29 ball-carries, his dominant tendency is that he carries through central areas (76%). He carries from outside into the middle 2 times and out toward the touchline 2 — balanced between cutting in and holding width.
05
What happens right after he carries the ball
Every carry's end point matched against his own very next pass or shot in the same match, when one starts almost exactly there — the direct continuation of that touch, not a separate stat. “No clean link” means no pass or shot of his own started close enough to count (often the carry ended in a tackle or a loss); it isn't a claim about exactly what happened, only that nothing traceable followed.
Shot
4 · 14%
Progressive / key pass
1 · 3%
Simple pass
9 · 31%
No clean link
15 · 52%
Of 29 carries, 14% flow straight into a shot of his own, 3% into a key or progressive pass — a genuine end product to the carry, not just ball retention. 52% have no clean link to a next touch of his own close to where the carry ended.
63 defensive actions across the season (9.2/90). 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 →
1
tackles · 100% won
1
interceptions · 0.1/90
10
recoveries · 1.5/90
6
clearances · 0.9/90
4
blocks
54
avg action height
16
won high (att 3rd)
34
middle third
13
own third
Vision AI A front-foot / high defender. He wins the ball mainly by stepping in — tackles, interceptions & recoveries outweigh clearances. 12 proactive ball-wins (1.8/90), tackle success 100%. 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.
42
attacking-half actions · 6.17/90
10
Against Different Pressing Intensities
Split by how hard the opponent pressed (opponent PPDA — lower = more intense press). Does Gustavo Varela 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 3.5, 2m)
0.32
1.0
1.43
62%
Mid press (PPDA 4.2, 2m)
0.34
1.2
1.15
12%
Low press (PPDA 4.8, 3m)
0.19
0.7
0.67
33%
Team
Team context
How much of the team runs through him, and what changes without him.
Monza's output per match with Gustavo Varela (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
2.00
1.00
+1.00
xG for /match
1.38
0.00
+1.38
goals against /match
2.43
1.00
+1.43
points /match
0.86
1.00
-0.14
13
His Share of the Team
Of everything Monza produced this season, the categories where the biggest share ran through Gustavo Varela specifically — his share of the team total, not a per-90 rate.
category
share
him / team
goals
54%
7 / 13
expected goals (xG)
10%
2.3 / 22.3
shots
6%
16 / 248
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 75%: a weighted blend of box threat 66 (weight 50%) + final-third threat 77 (weight 30%) + retention 78 (weight 20%). The score reflects how distinctively his profile SHAPE matches this archetype vs his peers.
receiving zones, ball-winning, territory (wide vs central)
Unknown
aerial ability, marking, off-ball running — not in event data
Crossing
79
Retention
78
Final-third threat
77
Dribbling
73
Press resistance ~proxy
71
Box threat
66
Passing directness
65
Carrying
65
Progression
59
Line-breaking
58
Through-balls ~proxy
58
Build-up
57
Transition ~proxy
53
Creativity
51
Defensive aggression
43
Receiving zones
35
Ball-winning
22
Territory (wide vs central)
4
15
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: 184 cm / 74 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.
Georges Mikautadze Villarreal · La Liga · 176 cm / 71 kg
75%
Driven by: territory (wide vs central) (16%), crossing (12%), final-third threat (11%). Different mechanism — Varela more via defensive aggression, through-balls; Mikautadze more via receiving zones, creativity.
Donyell Malen Roma · 178 cm / 68 kg
70%
Driven by: box threat (13%), territory (wide vs central) (13%), retention (13%). Different mechanism — Varela more via build-up, final-third threat; Malen more via receiving zones, box threat.
Aiyegun Tosin Lorient · Ligue 1 · 179 cm / 75 kg
68%
Driven by: ball-winning (19%), retention (11%), defensive aggression (10%). Different mechanism — Varela more via defensive aggression, crossing; Tosin more via territory (wide vs central), passing directness.
Keinan Davis Udinese · 189 cm / 68 kg
64%
Driven by: territory (wide vs central) (26%), press resistance (17%), dribbling (15%). Different mechanism — Varela more via passing directness, line-breaking; Davis more via ball-winning, receiving zones.
Kylian Mbappé Real Madrid · La Liga · 178 cm / 75 kg
61%
Driven by: ball-winning (15%), retention (13%), crossing (13%). Different mechanism — Mbappé more via receiving zones, territory (wide vs central).
Odsonne Édouard Lens · Ligue 1 · 187 cm / 83 kg
60%
Driven by: territory (wide vs central) (30%), ball-winning (17%), retention (16%). Different mechanism — Varela more via line-breaking, final-third threat; Édouard more via receiving zones.
16
Summary
Gustavo Varela is a target man / poacher: 7 goals from 2.30 xG (+4.70 vs expected), 16 shots, 4 box entries. A clinical finisher. [Off-ball runs, aerials and hold-up under contact need tracking/aerial data.]
Based on 7 matches (613 minutes) — an early read, treat with caution until more minutes are logged. He profiles as a target man / poacher — 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 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.