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Team reportLigue 1 2026

Monaco

A balanced-territory side, aggressive high-press out of possession, with a moderate attack. Currently #4 of 18 on 15 points (4-3-1, 11:7). Tactical profile below is built from 8 matches of ball-by-ball event data, contextualised against the league.

balanced-territoryaggressive high-pressmoderate attack
W
L
D
D
W
L
L
W
W
W
D
W
D
L
D
15
points
#4 of 18 · 8 games
11:7
goals
+4 GD
4-3-1
record
W-D-L
1.88
points/game
#4 in the league
1.09
xG for/game
rank #16
1.26
xG against/game
rank #6
#10
press rank
of 18 · most aggressive
52%
field tilt
#6
Monaco
League average
Expected goals for / game
1.091.41
Expected goals against / game
1.261.41
Field tilt
52%50%
Pass retention
86%83%
Overview

The team

Who they are and how the season went.

Who Are They? — Identity

The question
In one read, what kind of team is Monaco and how good was the season?
Read. Monaco currently sit #4 of 18 on 15 points (4W-3D-1L, 1.88 pts/game), scoring 11 and conceding 7 for a goal difference of +4. On the ball they are a balanced-territory team (52% field tilt, #6 in the league); off it they defend with a aggressive high-press (#10 of 18 for press intensity). Across the 8 matches of event data, they create 1.09 xG/game (#16) and concede 1.26 (#6), a net of -0.17 xG/game — the underlying performance matches the actual goal difference.

Season Context — the league table

The question
Where did Monaco actually finish, and against whom?

The authoritative final table (full season), centred on Monaco.

#teamPWDLGFGAGDPts
1PSG8521169+717
2Rennes85121814+416
3Lille8431116+515
4Monaco8431117+415
5Lyon8422137+614
6Paris FC8422139+414
7Lens84131612+413

Match Momentum — when they create and concede danger

The question
Across a whole game, which periods does Monaco actually dominate for chances — and which periods hurt them?

Net xG (created minus conceded) in each 15-minute window, summed across the whole season's matches — above the line is time they dominate for chances, below is time it hurts them. Real on-clock shot timing.

dominant (net xG for)conceding (net xG against)
even0-15'16-30'31-45'46-60'61-75'76-90+'+1.11-1.08
Vision AI Model Insight
Monaco tend to fade after a strong start — first-half net xG +0.37, second-half -1.69. Their strongest period is 0-15' (net +1.11 xG); their most dangerous period to face is 46-60' (net -1.08 xG against them).

xG Race — the season, match by match

Expected goals piling up over 8 matches: Monaco against what their opponents created. A line pulling away upwards is a side creating more than it gives away; the gap at the end is the season’s balance (-1.3 xG). Hover a point for that match.

047118.710.1Le Havre (A) 1-0 · xG 1.51 to 0.83Marseille (H) 2-0 · xG 1.03 to 1.39PSG (A) 2-1 · xG 0.42 to 2.28Strasbourg (A) 1-1 · xG 0.86 to 0.80Lens (H) 2-1 · xG 0.96 to 1.31Toulouse (H) 2-2 · xG 1.14 to 1.25Lorient (A) 0-1 · xG 0.82 to 1.70Lille (H) 1-1 · xG 2.01 to 0.52M1M2M3M4M5M6M7M8
MonacoOpponents against Monaco

Tactical DNA

The football question
In five genes, what is Monaco's footballing identity?

Each gene is a 0–100 score — the team's league percentile across the metrics that define that phase of play. It distils the whole report into an identity fingerprint: the taller the gene, the more that trait defines them.

72
Build-Up
possession control from the back
86
Progression
moving the ball upfield with threat
43
Creation
manufacturing chances
56
Press
winning the ball high & hard
67
Solidity
resisting the opponent's threat
Genetic identity
Monaco are defined most by their Progression DNA (86/100 · elite at moving the ball upfield with threat), and least by their Creation DNA (43/100). Percentiles vs the 18 teams in the league.

The Playstyle Wheel

Fifteen style metrics across five domains — Possess, Disrupt, Finish, Press, Defend — each a percentile against the rest of the league. Metrics marked ~proxy use the closest available event-data signal where the standard definition needs data this feed doesn't carry (explicit pressure events, offside/keeper-sweep incidents).

Possess63Disrupt50Finish78Press83Defend80
Possess
Deep buildup
45%
Possession
75%
Field tilt
70%
Disrupt
Central progression ~proxy
60%
Dribble tendency
15%
Progressive passes
75%
Finish
Final-third patience
85%
Shot quality
80%
Open-play xG
70%
Press
Forward press ~proxy
85%
High regains
80%
Pressing efficiency ~proxy
85%
Defend
Def-mid press ~proxy
90%
High line ~proxy
65%
PAdj defensive actions
85%
Monaco is defined most by Press (83% in the league) and weakest on Disrupt (50%). Percentiles vs the 20 teams in the league this season.
In possession

In possession

How they keep the ball, build up, progress, create and finish, and where their attacks die.

Keeping the ball6 sections

Field Tilt — Territorial Control

The question
How much of the pitch does Monaco actually control, relative to the rest of the league?

Field tilt = share of final-third touches between the two sides across Monaco's matches, the standard proxy for territorial dominance. Plotted against points per game for every side in the league — the real question isn't just who controls territory, it's whether that control actually converts into results. Dashed lines mark the league average on each axis; Monaco highlighted. Hover any dot for the team.

PSG: 74% tilt, 2.12 pts/gRennes: 50% tilt, 2.00 pts/gLille: 44% tilt, 1.88 pts/gMonaco: 52% tilt, 1.88 pts/gLyon: 49% tilt, 1.75 pts/gParis FC: 46% tilt, 1.75 pts/gLens: 55% tilt, 1.62 pts/gLorient: 40% tilt, 1.50 pts/gMarseille: 58% tilt, 1.50 pts/gNice: 53% tilt, 1.38 pts/gToulouse: 51% tilt, 1.25 pts/gAuxerre: 52% tilt, 1.25 pts/gBrest: 44% tilt, 1.12 pts/gAngers: 43% tilt, 1.00 pts/gStrasbourg: 42% tilt, 0.88 pts/gLe Mans: 41% tilt, 0.88 pts/gTroyes: 54% tilt, 0.62 pts/gLe Havre: 51% tilt, 0.25 pts/gMonaco40%52%63%74%0.20.91.52.1FIELD TILT %POINTS / GAME
Vision AI Model Insight
Monaco post 52% field tilt, ranking #6 of 18 in the league — comfortably above the league mid-point for territorial control, and their 1.88 points/game sits above the league-average return — territory is genuinely converting into results.

Where the time goes — how long each phase lasts

The question
When Monaco has the ball, how much of that time is spent building, progressing and creating?

The share of Monaco's time on the ball spent in each phase of play, in the order the phases happen. A phase is where the ball is on the pitch (own third, middle third, final third); a transition or a set piece skips the build-up and is counted on its own.

Build-up: 22.8% of the time on the ball23%Progression: 53.2% of the time on the ball53%Chance creation: 21.7% of the time on the ball22%Attacking transition: 2.3% of the time on the ball53%PROGRESSION
PhaseShare of the time
Build-up22.8%
Progression53.2%
Chance creation21.7%
Attacking transition2.3%
Vision AI Model Insight
Progression is where Monaco spends most of its time on the ball (53% of its time on the ball).

Passing Network

The question
Who actually plays with whom — the real on-pitch connections, not just average positions?

Every pass linked to its likely receiver (the next same-team touch), aggregated into player-pair combinations at each player's average position. Line width = how often the pair combines; colour = threat generated per pass (blue low → red high). Hover a line for the pair, or click a player to isolate their connections.

Vanderson ↔ Sadibou Sané: 208 passes, -1.5 combined xTEric Dier ↔ Flávio Nazinho: 164 passes, -0.6 combined xTLamine Camara ↔ Sadibou Sané: 142 passes, -1.1 combined xTEric Dier ↔ Sadibou Sané: 135 passes, -1.1 combined xTVanderson ↔ Lamine Camara: 120 passes, 1.3 combined xTDenis Zakaria ↔ Flávio Nazinho: 105 passes, 0.6 combined xTEric Dier ↔ Lamine Camara: 87 passes, -0.9 combined xTLukás Hrádecky ↔ Sadibou Sané: 79 passes, -0.5 combined xTAleksandr Golovin ↔ Flávio Nazinho: 79 passes, 0.8 combined xTDenis Zakaria ↔ Lamine Camara: 79 passes, 0.2 combined xTEric Dier ↔ Denis Zakaria: 75 passes, -0.4 combined xTFlávio Nazinho ↔ Stanis Idumbo: 74 passes, 1.1 combined xTLukás Hrádecky ↔ Eric Dier: 67 passes, 0.1 combined xTFlávio Nazinho ↔ Lamine Camara: 54 passes, -0.3 combined xTJordan Teze ↔ Sadibou Sané: 49 passes, 0.4 combined xTAleksandr Golovin ↔ Vanderson: 47 passes, 1.0 combined xTDenis Zakaria ↔ Sadibou Sané: 45 passes, -0.4 combined xTVanderson ↔ Mamadou Coulibaly: 41 passes, 0.9 combined xTAleksandr Golovin ↔ Lamine Camara: 38 passes, 1.0 combined xTFlávio Nazinho ↔ Mathys Detourbet: 37 passes, 1.1 combined xTLukás Hrádecky ↔ Lamine Camara: 35 passes, -0.1 combined xTJordan Teze ↔ Lamine Camara: 33 passes, 0.4 combined xTAleksandr Golovin ↔ Denis Zakaria: 32 passes, 0.4 combined xTAleksandr Golovin ↔ Sadibou Sané: 31 passes, 0.5 combined xTLukás Hrádecky — isolate this player's connectionsHrádeckyAleksandr Golovin — isolate this player's connectionsGolovinDenis Zakaria — isolate this player's connectionsZakariaVanderson — isolate this player's connectionsVandersonFlávio Nazinho — isolate this player's connectionsNazinhoMaghnes Akliouche — isolate this player's connectionsAklioucheLamine Camara — isolate this player's connectionsCamaraSadibou Sané — isolate this player's connectionsSanéParis Brunner — isolate this player's connectionsBrunnerMatthis Abline — isolate this player's connectionsAblineEric Dier — isolate this player's connectionsDierStanis Idumbo — isolate this player's connectionsIdumboMathys Detourbet — isolate this player's connectionsDetourbetEdan Diop — isolate this player's connectionsDiopMamadou Coulibaly — isolate this player's connectionsCoulibalyJordan Teze — isolate this player's connectionsTeze
width = passes between the paircolour = xT per pass: low → high

Top 10 combinations

Vanderson ↔ Sané
208p · -1.5xT
Dier ↔ Nazinho
164p · -0.6xT
Camara ↔ Sané
142p · -1.1xT
Dier ↔ Sané
135p · -1.1xT
Vanderson ↔ Camara
120p · 1.3xT
Zakaria ↔ Nazinho
105p · 0.6xT
Dier ↔ Camara
87p · -0.9xT
Hrádecky ↔ Sané
79p · -0.5xT
Golovin ↔ Nazinho
79p · 0.8xT
Zakaria ↔ Camara
79p · 0.2xT
Vision AI Model Insight
Busiest connection: Vanderson ↔ Sadibou Sané (208 passes). Most dangerous per pass: Flávio Nazinho ↔ Mathys Detourbet (0.03 xT/pass over 37 passes) — the combination that hurts opponents most when it fires, not just the one used most.

Touch Density — where Monaco had the ball

Every touch across 72 matches. Brighter means more touches.

Which Zones Do They Attack Through?

The question
Across the whole pitch, where does Monaco actually do its on-ball work?

Passes on one map, carries (the ball travelling with a player) on the other. Each is binned into the 15 pitch zones (three thirds × five vertical lanes) and shaded by its share of that kind of action; the arrows are the real actions.

Passes
the ball moved by a pass
4444
ACTIONS
4%5%9%4%2%9%12%13%12%9%6%4%3%4%6%PASSES
Carries
the ball moved with a player
1084
ACTIONS
2%5%10%5%1%9%10%15%10%10%6%2%2%4%7%CARRIES
Vision AI Model Insight
Their single busiest zone is the centre of the middle third (13.1% of all actions). 64% of actions run through the inside lanes (centre + both half-spaces) versus 36% down the two wings — an inside-dominant, half-space build. By third: 24% defensive, 53% middle, 23% attacking.

Attack Sides

Which channel they attacked through this season: the share of every action made in the opponent’s half, down the left, through the middle and down the right. Monaco leaned on their right (38%). The bars under the pitch put the league’s share beside theirs.

37%Left side25%Middle38%Right sideATTACKING →
Monaco
37%25%38%
League
38%22%40%

Final-Third Entries

Every completed pass or carry that took the ball from outside the final third into it, by the channel it arrived in. Monaco made 419 this season (52.4 a match); 30% came into the right wing. Longer, bolder arrows mean more entries. Entries by carrying the ball in count as well as completed passes; dribbles are not counted.

Left wing: 109 entries (26%)10926%LEFT WINGLeft half-space: 58 entries (14%)5814%LEFT HALF-SPACECentre: 60 entries (14%)6014%CENTRERight half-space: 66 entries (16%)6616%RIGHT HALF-SPACERight wing: 126 entries (30%)12630%RIGHT WINGATTACKING ↑
ChannelEntriesPer matchShareLeague share
Left wing10913.626%30%
Left half-space587.214%13%
Centre607.514%11%
Right half-space668.216%14%
Right wing12615.830%32%
Build-up2 sections

Build-Up — the pass map

The football question
How does Monaco progress possession from defence into attack — and who runs it?

Build-up passes grouped into 5 real categories — not the old zone grid, not raw individual lines: CB (passer is a centre-back), Wide→FB, Central, Long Wide, Long Central. Each gets its own panel below, so nothing competes for attention. Arrow width = volume, labelled with the exact count; hover for the top passer.

1070build-up passes attempted
84%completed
29%reach the middle third
1%go long into the final third

Pass types

392CB · 43%
49Long Wide · 5%
12Long Central · 1%
340Wide → FB · 38%
110Central · 12%

Sequence success — build-up

102build-up sequences this season
14%succeed — reach the next third or a shot
30%turned over with nothing to show
CB392 · 43%
right: 223 passes, most from Sané223left: 169 passes, most from Dier169
Long Wide49 · 5%
right: 31 passes, most from Hrádecky31left: 18 passes, most from Hrádecky18
Long Central12 · 1%
left: 6 passes, most from Hrádecky6right: 6 passes, most from Hrádecky6
Wide → FB340 · 38%
right: 188 passes, most from Hrádecky188left: 152 passes, most from Nazinho152
Central110 · 12%
left: 71 passes, most from Hrádecky71right: 39 passes, most from Hrádecky39
◆ Vision AI Insight
Monaco build predominantly down the right, with Hrádecky as the fulcrum (196 build-up passes). Only 30% of their build-up passes actually advance a third — the rest is lateral circulation, the hallmark of a possession-controlling side that circulates patiently in midfield before progressing. Their single most-repeated pattern is C def → LHS def (68×).

Pass Explorer

Every pass of Monaco’s last 8 matches as an arrow, theirs first and the opponents’ with other team, each side attacking left to right (solid = completed, dashed grey = failed). Eight families per team — click any map to open it wide, then filter by channel or third, from one to another (or click a cell of the flow grids), by outcome, half, minute and passer. Channels: right wing, right half-space, centre, left half-space, left wing.

Progression4 sections

How They Progress — the mechanism

Not just how many progressive passes, but where they start, who plays them, and whether they actually penetrate. 399 progressive passes across 8 matches (49.9/game).

Origin lane

Right wing
32%
Right half-space
16%
Central
12%
Left half-space
14%
Left wing
27%
Progression is right-dominant: 47% right side · 12% central · 41% left.

Who generates it

Vanderson
82 · 21%
Flávio Nazinho
58 · 15%
Sadibou Sané
40 · 10%
Aleksandr Golovin
33 · 8%
Lamine Camara
27 · 7%
Top two — Vanderson & Flávio Nazinho — account for 35% of all progression.
PROGRESSION
49.9 prog passes/game
mostly right
→
PENETRATION
31% reach final third
20% reach the box
→
THREAT
the other 69% is recycled
progress ≠ danger
What came of it. 31% of progressive passes reached the final third and 20% the box; the rest was recycled in front of the block. 35% of the progression ran through two players, mostly down the right side.

Which lanes actually create the threat

Share of passes played from each lane against its share of the threat (xT) they add. Efficient = more threat than volume, sterile = lots of the ball, little damage.

Per lane — volume vs threat
Right wingefficient
Right half-spacebalanced
Centralsterile
Left half-spacebalanced
Left wingbalanced

Progression — the pass map

The football question
Once Monaco have the ball in midfield, how do they actually break into the final third — and who carries that job?

Progression passes grouped into 5 real categories: CB, Wide→FB, Central, Long Wide, Long Central. Each gets its own panel below, so nothing competes for attention. Arrow width = volume, labelled with the exact count; hover for the top passer.

2360progression passes attempted
91%completed
15%break into the final third
9%go backward into the defensive third

Pass types

707CB · 33%
82Long Wide · 4%
10Long Central · 0%
1009Wide → FB · 47%
333Central · 16%

Sequence success — progression

159progression sequences this season
31%succeed — reach the next third or a shot
31%turned over with nothing to show
CB707 · 33%
right: 396 passes, most from Sané396left: 311 passes, most from Dier311
Long Wide82 · 4%
right: 49 passes, most from Vanderson49left: 33 passes, most from Nazinho33
Long Central10 · 0%
right: 8 passes, most from Vanderson8left: 2 passes, most from Camara2
Wide → FB1009 · 47%
left: 559 passes, most from Nazinho559right: 450 passes, most from Vanderson450
Central333 · 16%
left: 188 passes, most from Camara188right: 145 passes, most from Camara145
◆ Vision AI Insight
Monaco progress predominantly down the right, with Camara the busiest midfield outlet (348 progression passes). Only 15% of passes that start in midfield actually break the final-third line — the rest (76%) is recycled back into midfield or (9%) played backward, the hallmark of a side that carries progression itself through midfield rather than just passing through it. Their most-repeated pattern is RHS mid → C mid (142×) — press that lane and midfield progression stalls.

Decision Quality — not all completed passes are equal

A 90% pass completion can hide a sideways habit. Grading every pass by the threat it adds separates value creation from safe circulation.

Excellent (high xT gain)
13%
Progressive
1%
Neutral / sideways
46%
Safe / backward
39%
15% of passes are progressive-or-better; the remaining 85% keep possession without advancing it. A control-first side — safe in build-up, reliant on a few players for the incision.

Carries Map — progressive ball-carries, squad-wide

The question
Where does Monaco actually progress the ball with their feet, not a pass?

237 progressive carries this season across the whole squad (22% of 1084 tracked carries total), grouped into up to 4 spatial patterns by where each carry starts and ends together — not just its origin zone. Median 15m, longest 40m. Most active carrier: Sané (31). Filter to one player below.

First (74)Second (66)Third (50)Fourth (47)grouped by carry pattern (start + end position), largest group first
startendown goal left, attacking →
Chance creation4 sections

Chance creation — how the shots come

The question
Where do Monaco's chances actually come from?

Every shot sorted twice. First by the kind of attack: an attacking transition (the shot came within 15 seconds of winning the ball), a settled attack (open play after the team had the ball), or a set piece. Then by how it was set up: a cross, a cut-back, a long ball, a pass into the box, or no pass at all (the player created it himself). So a cut-back from an attacking transition is counted apart from a cut-back in a settled attack.

102shots
8.7xG
0.09xG per shot
1big chances (0.3+ xG)
10goals
#11key passes / game in the league
#4box entries / game in the league
Attacking transition: 20 shots (20%)20%20Settled attack: 68 shots (67%)67%68Set piece: 14 shots (14%)14%14102SHOTS
Kind of attack, then how the shot was set upShotsGoalsxGxG / shot
Attacking transition 20% of shots2021.620.08
Cross100.210.21
Cut-back100.110.11
Long ball100.080.08
Pass into the box / short pass920.490.05
Own creation (carry, dribble, rebound)800.730.09
Settled attack 67% of shots6876.020.09
Cross1821.650.09
Cut-back710.770.11
Long ball300.170.06
Pass into the box / short pass2432.180.09
Own creation (carry, dribble, rebound)1611.260.08
Set piece 14% of shots1411.100.08
Corner1010.980.10
Free kick400.120.03

Every attempt to create — and what came of it

Every pass into the box, every cut-back and every pass before a shot. Red led to a shot, gold to a goal; grey ones were tried but no shot came: solid found a team-mate, dashed lost the ball. Filter by outcome, type or player; tap an arrow for the detail.

Who sets the shots up

PlayerSet-upsxG createdGoals
Vanderson111.650
Aleksandr Golovin141.143
Flávio Nazinho70.530
Stanis Idumbo50.490
Matthis Abline20.331

Who takes them

PlayerShotsxGGoals
Paris Brunner192.854
Matthis Abline120.891
Stanis Idumbo120.771
Aleksandr Golovin110.510
Vanderson60.480
Vision AI Model Insight
The biggest source of Monaco's chances, by xG, is settled attack (6.0 of 8.7 xG, 68 of 102 shots), mostly pass into the box / short pass. 20 of the 102 shots (20%) came within 15 seconds of winning the ball. 14 of the 102 shots came from set pieces; 10 goals from 8.7 xG (+1.3).

Chance Creation — the pass map

The football question
How does Monaco consistently create dangerous chances — from which zones, and who creates them?

Key passes (the pass immediately before a shot) grouped into 5 real categories: CB, Wide→FB, Central, Long Wide, Long Central. Each gets its own panel below, so nothing competes for attention. Arrow width = volume, labelled with the exact count; hover for the top creator.

Pass types

6CB · 8%
4Long Wide · 5%
10Long Central · 14%
16Wide → FB · 22%
37Central · 51%

Sequence success — final-third

100final-third sequences this season
23%succeed — produce a shot
38%turned over with nothing to show

Danger Funnel — which service carries threat

Every key pass pooled by where it was played FROM, then drawn with its arrow width set by total xT rather than by count. A zone that produces a lot of cheap balls shrinks; one that produces a few decisive ones dominates. Where this disagrees with the volume map, the volume map is the misleading one.

0 key passes shown● low threat● mid● high threatarrow width = total xT, not count
CB6 · 8%
left: 3 passes, most from Teze3right: 3 passes, most from Dier3
Long Wide4 · 5%
left: 3 passes, most from Coulibaly3
Long Central10 · 14%
left: 5 passes, most from Golovin5right: 5 passes, most from Golovin5
Wide → FB16 · 22%
left: 12 passes, most from Golovin12right: 4 passes, most from Camara4
Central37 · 51%
right: 20 passes, most from Vanderson20left: 17 passes, most from Idumbo17

Who creates — volume × threat

18Golovin
10Nazinho
10Vanderson
5Brunner
4Camara
4Abline
4Idumbo
73key passes #11 of 18
14.1box entries/game #4 of 18
41.9final-3rd entries/game #2 of 18
1.09xG/game #16 of 18
10goals #11 of 18
+1.3finishing vs xG #4 of 18
◆ Vision AI Insight
Monaco lean right — 42% of their key passes come from that flank and its half-space (31 of 73), most often from Vanderson (10). They funnel chances into the c of the final third, most often via RW att → C att (11 key passes, most often from Vanderson). Golovin is the primary source overall. They generate 1.09 xG a game and finish it clinical — they beat their xG, led by Paris Brunner (4G), Edan Diop (2G), Flávio Nazinho (1G).

Crosses & Cutbacks — squad-wide

The question
How does Monaco deliver into the box from wide areas, and what comes of it?

108 crosses: 39 found a team-mate (36%), 19 led to a shot (18%). 32 cut-backs: 14 found a team-mate (44%), 7 led to a shot (22%). A cross starts wide and is aimed into the box; a cut-back starts near the byline and is squared back. Grey ones were tried but no shot came: solid found a team-mate, dashed lost the ball.

Crosses by Territory

The question
Which part of the final third does Monaco actually cross from?

108 crosses this season (the same crosses as the map above), split into fifteen territories — five channels (both wings, both half-spaces and the centre) by three depths from the halfway line to the byline. 27% come from one territory, the advanced right wing; 2 come from the half-spaces and 0 from the centre.

deep right wing: 7 crosses (6%)6%7deep right half-space: 1 crosses (1%)1%1deep centre: 0 crosses (0%)deep left half-space: 0 crosses (0%)deep left wing: 8 crosses (7%)7%8advanced right wing: 29 crosses (27%)27%29advanced right half-space: 1 crosses (1%)1%1advanced centre: 0 crosses (0%)advanced left half-space: 0 crosses (0%)advanced left wing: 18 crosses (17%)17%18byline right wing: 23 crosses (21%)21%23byline right half-space: 0 crosses (0%)byline centre: 0 crosses (0%)byline left half-space: 0 crosses (0%)byline left wing: 21 crosses (19%)19%21
TerritoryCrossesShareLed to a shot
advanced · right wing2927%5
byline · right wing2321%4
byline · left wing2119%6
advanced · left wing1817%3
deep · left wing87%0
deep · right wing76%1
deep · right half-space11%0
advanced · right half-space11%0
Finishing3 sections

Shot Map

102 shots for 8.7 xG (1.09/game, #16 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.

Moderate attack. 12.8 shots/game at 0.086 xG/shot. Box entries 14/game (#4). Threat created 12.1 xT/game (#2).

Goal Sequences

The move behind each of Monaco’s 11 goals this season, from the moment they got the ball to the finish. Pick a goal from the list, then step through it or press Play: numbered circles are passes and touches, dotted lines are carries (the ball travelling with a player), dashed grey is a pass that was lost, the star is the shot.

Each goal’s possession, rebuilt from the raw event feed — 11 goals traced.

How Do They Score?

The question
Where do the goals actually come from — open play, dead balls, which foot?

Every goal Monaco scored across the loaded matches, broken down by how it was created and finished.

By situation

Open play
9 · 90%
Set pieces
1 · 10%
Penalties
0 · 0%

By finish

Right foot
4 · 40%
Left foot
2 · 20%
Header
4 · 40%
Vision AI Model Insight
10 goals scored. 90% come in open play; set pieces add 10%. Open play is clearly their main route to goal.
When they lose it1 section

Ball-Loss Map — where attacks die

Every turnover has a location and a cause. This is a rate, not a raw count — the share of pass attempts from each zone that end in a turnover, so a zone they simply play through more often doesn't automatically look riskier. Grey cells are real zones with too few attempts to trust a rate.

Loss rate by zone

38 of 177 pass attempts lost here (21%).21%38/177Vanderson27 of 203 pass attempts lost here (13%).13%27/203Vanderson50 of 395 pass attempts lost here (13%).13%50/395Hrádecky20 of 187 pass attempts lost here (11%).11%20/187Nazinho32 of 108 pass attempts lost here (30%).30%32/108Nazinho51 of 399 pass attempts lost here (13%).13%51/399Vanderson42 of 512 pass attempts lost here (8%).8%42/512Sané35 of 557 pass attempts lost here (6%).6%35/557Camara41 of 514 pass attempts lost here (8%).8%41/514Nazinho50 of 378 pass attempts lost here (13%).13%50/378Nazinho66 of 285 pass attempts lost here (23%).23%66/285Vanderson44 of 169 pass attempts lost here (26%).26%44/169Vanderson27 of 136 pass attempts lost here (20%).20%27/136Brunner49 of 176 pass attempts lost here (28%).28%49/176Nazinho60 of 248 pass attempts lost here (24%).24%60/248Nazinhoattacking →

How they lose it

Incomplete pass — attacking third
246
Failed forward pass
158
Failed take-on
65
Lost in the defensive third
167
Their weakest zone is the left wing in the defensive third: 30% of 108 pass attempts there are lost (32 times). Separately, 39% of all losses happen in the attacking third — a fair share happen deeper, which invites transitions.
Out of possession

Out of possession

How they press, where they win the ball back, and how they concede.

Pressing2 sections

Pressing — the full picture

The football question
How hard does Monaco press, where, and does it actually work — beyond a single PPDA number?

A composite Pressing Index built from four event-derived dimensions, each ranked against the league. Not one metric — intensity, height, effectiveness and opponent disruption together. The pitch shows every defensive action (the only five events this data records); the white line is the average height that feeds the score. Toggle by action type below, hover any dot for the player.

70
Pressing Index
/100 · strong
Intensity65
PPDA, high-press share, high-zone actions
Height67
how high up the pitch they defend
Effectiveness83
high recoveries & high-regain share
Disruption61
opponent completion & progression allowed

Every defensive action & where it happens

Where they win the ball back — by lane

RW
20%
RHS
20%
C
25%
LHS
20%
LW
15%
By third (high / mid / low regain)
Def 46%
Mid 38%
Att 16%
Read
Ranked #10 of 18 for press intensity, this is a high press. 54% of defensive actions happen in the middle & attacking thirds, at an average height of 39. Of recoveries, 57% are won high up the pitch.

Every pressing metric, ranked in the league

metricvaluerankmeaning
PPDA (opp passes per def action)—#10/18lower = more intense; rank is what matters
Defensive actions / game160#7/18
High-press share25%#5/18def actions won high up
Defensive-action height (0–100)39.7#6/18avg pitch height of actions
High recoveries / game27.6#2/18ball won in mid+att third
High-recovery share57%#4/18share of recoveries won high
Opponent completion allowed84%#12/18lower = more disruptive
Opponent progressive allowed6.9%#2/18of opp passes, lower = better
Transition timing
This source carries per-event timestamps, so genuine transition metrics — counter-attack shots, press→shot conversions, time-to-recover — are measured; see the dedicated Transitions section below.

How long they keep it — passes and seconds until the ball is won

The question
Once the opponent has the ball, how many passes and how many seconds pass before Monaco win it back with a defensive action?

Counted over the 289 opposition possessions that Monaco ended with a tackle, interception, recovery or challenge. Fewer passes and seconds means the ball is won quickly (a press); more means the opponent is allowed to play before Monaco step in. Monaco win it after 6.4 opponent passes in the first half-hour and after 5.1 after the hour mark (sooner).

PASSES THEY GET
5.5passes
median 3 · before Monaco win it
TIME THEY KEEP IT
15sec
median 8s · 36.1 wins per game
How many passes the opponent got in before the ball was won
39%
29%
15%
17%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
6.2
17s
15–30′
6.6
21s
30–45+′
4.4
11s
45–60′
5.3
15s
60–75′
6.3
16s
75–90+′
4.5
13s
average passesaverage seconds

Attitude in each third

Every other style number here says how much Monaco does. This says how: wide or central, short or long, patient or direct. Bars are standard deviations from the league mean for passes played from that third; a solid bar marks a dimension where Monaco are genuinely unusual, and a verdict is only written where something clears 0.85 sd — a side that is ordinary in a third is told so rather than given a label for average play.

Build-up (own third) 1,070 passes

nothing unusual in this zone

Width of use-0.2520.49
Wide-channel share-0.480.27
Side bias-0.76-2.67
Pass length-0.4920.92
Long-ball share-0.560.18
Forward gain per pass-0.549.11
Backward-pass share+0.080.20
Deep-pass share-0.390.25
Completion+0.530.84
Seconds between passes-0.133.25
Side used

left 23% · central 45% · right 32%

Progression (middle third) 2,360 passes

safer

Width of use-0.6524.19
Wide-channel share-0.740.35
Side bias-0.17-0.62
Pass length-0.3316.76
Long-ball share-0.610.08
Forward gain per pass-0.512.84
Backward-pass share+0.300.35
Deep-pass share-0.540.13
Completion+0.940.90
Seconds between passes-0.443.00
Side used

left 33% · central 33% · right 35%

Final third 1,014 passes

circulating, more recycling

Width of use-0.6729.95
Wide-channel share-0.510.54
Side bias-0.27-2.39
Pass length+0.1615.71
Long-ball share-0.410.08
Forward gain per pass-0.99-0.28
Backward-pass share+0.960.45
Deep-pass share-0.440.07
Completion+0.660.75
Seconds between passes+0.303.62
Side used

left 37% · central 19% · right 44%

Reference measured across roughly 3,500 match-teams in this competition, per third, so the comparison is with Monaco's own league. The means behave as football should: width of use grows 21.0 → 25.6 → 31.6 going forward, pass length shrinks 22.7 → 17.2 → 15.5 m, and completion is hardest in the final third. Tempo is integer-resolution because the feed records whole seconds.

Transitions1 section

Transitions — attacking & defensive

The question
When Monaco win or lose the ball, what actually happens in the seconds after?

Reconstructed from the full timestamped event stream: real possession spells (not zone-clustered), and shots that follow a regain within a fixed window. This phase of play needs ordered, per-event timestamps — which every match here carries.

Direct play share17%
#8 of 18 · reaches final third in ≤3 passes
Patient build-up share30%
#10 of 18 · spells of 6+ passes
Tempo (possession length)18.3s
#9 of 18 · shorter = faster tempo
Passes per possession4.9
#9 of 18
Counter-attack shots /game1.10
#8 of 18 · shot within 12s of a regain in own half
Press→shot /game3.43
#6 of 18 · shot within 8s of a high regain
Speed to final third after regain11.5s
#5 of 18 · lower = faster break

Sequence success — attacking transition

15attacking transition sequences this season
7%succeed — produce a shot
47%turned over with nothing to show

Sequence success — defensive transition

35defensive transition sequences this season
66%succeed — reach the next third or a shot
17%turned over with nothing to show
Vision AI Model Insight
Monaco are balanced between tempo and patience: 17% of possessions reach the final third in 3 passes or fewer (#8 of 18), against 30% built patiently over 6+ passes (#10 of 18). Winning the ball back leads to a shot inside 12 seconds 1.10 times a game (#8), and a high regain converts to a shot within 8 seconds 3.43 times a game (#6 of 18) — a modest but real source of chances.
Defending the box2 sections

Vulnerability Map — how opponents hurt them

The report flipped: where do opponents create their threat against Monaco? 302 opponent progressive passes and 96 shots faced (10.1 xG against) across the season.

Opponent threat by lane (attacking into Monaco)

Right wing
32%
Right half-space
13%
Central
12%
Left half-space
14%
Left wing
29%
Where the danger came from. Opponents generated most of their threat through the right wing (32% of the danger conceded).

How Do They Concede?

The question
Where do the goals against Monaco actually come from — open play, dead balls, and who supplies them?

Every goal Monaco conceded across the loaded matches — the opponent's shots in Monaco's own matches — plus every individual opponent key pass faced, each drawn as its own real arrow (not zone-clustered) with the % share of each zone behind it.

Where the goals go in

← opponent shot at own goal

Every opponent key pass against them (66 total)

3%6%2%6%18%18%18%24%5%Timothée PembéléAmir RichardsonFodé DoucouréJunior MwangaMbwana SamattaEnzo KoffiRassoul NdiayeJosh MajaJosh MajaEmersonPierre-Emile HøjbjergHimad AbdelliAmine HaritKeyliane AbdallahTimothy WeahPierre-Emile HøjbjergAmine HaritKeyliane AbdallahMatthieu UdolMatthieu UdolFlorian ThauvinJunior KadileMichaël CuisanceMaghnes AklioucheDésiré DouéKhvicha KvaratskheliaKhvicha KvaratskheliaKhvicha KvaratskheliaDésiré DouéMaghnes AklioucheVitinhaDésiré DouéVitinhaLucas HernándezMarquinhosOusmane DembéléVitinhaMika GodtsMika GodtsAchraf HakimiOdsonne ÉdouardRuben AguilarJonathan GraditFlorian ThauvinAndrija BulatovicGabin BernardeauIsaak TouréArthur AvomMamadou KonéIsaak TouréJean-Victor MakengoChrist TapéChrist TapéIlyas AziziCristian CásseresCasper TengstedtAron DønnumJacobo OrtegaDiogo SousaGessime YassineNgal'ayel MukauSoriba DiaouneDiogo SousaDário EssugoGiovanni ReynaPape Diop← own goal

By situation

Open play
2 · 33%
Set pieces
2 · 33%
Penalties
2 · 33%
Vision AI Model Insight
6 goals conceded. 33% come in open play, and 33% from set pieces — a genuine defensive frailty at dead balls to fix on the training ground. 17% are headers — aerial defending is not where the damage comes from. Their supply line runs mainly through the left half-space of the attacking third (24% of key passes faced) — that is the zone to shut off.
Context

Shape and tactics

Set pieces, strengths and weaknesses, and who they resemble.

Premium
Tactical Phase Read
The tactical idea behind each phase of this team's play, written from the same numbers shown above — what they do in build-up, how they progress, where they create, and how they defend it.
Subscribe →

Set-Piece Threat

How much of the goal threat is manufactured from dead balls — a route to goal that bypasses their open-play mechanism entirely.

Open play
9 g · 7.6 xG
Set pieces
1 g · 1.1 xG
Penalties
0 g
10% of goals come from set pieces (1 of 10), worth 1.1 xG from 14 shots. Set pieces are a secondary threat; the danger is in open play.

Where do Monaco's corners go?

32 corners taken this season, split by which flag they were taken from — arrows from the flag, dots at the delivery point, shaded by % of that side's corners landing in each zone (same treatment as the concede map below). No swing-direction (inswinger/outswinger) data is recorded here, only a start and end point per pass, which cannot show how the ball curved — so this shows what is genuinely knowable: where it lands, and short vs actually delivered. Tap or hover a dot for who took it and the match; filter to one match below and the shading recomputes for just that game.

Right side (11)

18%9%9%18%45%
Delivered into the box 100%Short 0%

Left side (21)

10%5%14%5%5%24%33%
Delivered into the box 95%Short 5%

Where opponents attack Monaco from set pieces

16 shots from opponent corners against Monaco, 2 conceded, split by the flag the corner came from — arrows from the flag, dots where the shot was taken (gold = goal), shaded by the % of that flag's shots in each zone. Tap or hover a shot for who took it, the minute and the match; filter to one match and the shading recomputes for just that game.

Right flag (11)

55%18%9%18%
Shot 11Goals 2

Left flag (5)

40%20%20%20%
Shot 5Goals 0

Tactical Strengths & Weaknesses

Strengths (top-third vs league)
  • threat created /game — 12.09 (89th pct)
  • progression /game — 49.88 (83th pct)
  • box entries /game — 14.12 (78th pct)
  • pass retention % — 85.78 (78th pct)
  • xG conceded /game — 1.26 (67th pct)
Weaknesses (bottom-third vs league)
  • xG created /game — 1.09 (11th pct)

Style Archetype & Similar Teams

Team archetype: Direct / counter high-press. Their closest stylistic matches in the league (cosine similarity on style metrics) — sides that play a similar way.

Nice
82%
Toulouse
70%
PSG
52%
Marseille
15%
Lille
13%

Performance vs Opponent Styles

The question
Not just how Monaco results against a high press or a low block — how does their own play actually change?

Every match split into three tiers by how hard that specific opponent pressed (opponent PPDA — lower means a more aggressive press), matching Monaco's own passing behaviour to the result.

Results

opponent typegoals for/ggoals against/gpoints/g
vs high-pressing opponents (2m)1.500.503.00
vs mid-block opponents (2m)2.001.002.00
vs low-block opponents (4m)1.001.001.25

Behaviour — how Monaco actually play against each

opponent typeretentiondirectness (long balls)prog. passes/gxT/gbox entries/g
vs high-pressing opponents80%13%4710.410.0
vs mid-block opponents87%12%5211.512.0
vs low-block opponents87%8%5013.217.2
Vision AI Model Insight
Facing a high press, retention drops to 80% vs 87% against a low block (7pt lower under pressure), and directness (long-ball share) sits at 13% when pressed vs 8% against a low block — a real style switch: they go noticeably more direct when pressed rather than trying to play through it.

Scouting Summary — the team in brief

In brief. Monaco are balanced-territory with a aggressive high-press. Their attack creates 1.09 xG a game and they concede 1.26 a game. [Event-data based — no tracking, so off-ball structure and marking scheme need video.]
Squad

Players

Who drives the numbers.

Against Their Own Norm

For each of ten match numbers, the one match this season that departed furthest from Monaco’s own average, the eight biggest swings first. Bar right of centre means more than usual, left means less. It shows how much the way they play moves from game to game, and which opponent moved it.

Monaco
across 8 matches this season
Threat from passingvs Lille · 1-1 · 2026-10-259.29avg 5.91
Pass accuracyat Le Havre · 1-0 · 2026-08-2378%avg 85%
Defensive actionsat Le Havre · 1-0 · 2026-08-23189avg 160
Expected goalsvs Lille · 1-1 · 2026-10-252.01avg 1.09
PPDAat PSG · 2-1 · 2026-09-049.3avg 5.7
Final-third entriesvs Toulouse · 2-2 · 2026-10-1085avg 52
Field tiltat PSG · 2-1 · 2026-09-0423%avg 52%
Progressive passesvs Toulouse · 2-2 · 2026-10-1068avg 50

On-Ball Value — who moved the game

Every pass, carry, take-on, shot and ball win is worth the change it made to the chance of scoring minus the chance of conceding in the next 10 seconds. This is Monaco’s last 8 matches added up: where the value came from, who produced it, and the six biggest single moves. OBV only measures what a player does with the ball: it shows who added the most on-ball value, not who was the most important player (defending off the ball, positioning and pressing are not in it).

+12.6on-ball value, 8 matches
Where the value came from
Passes4444 actions+5.42
Carries1084 actions+0.28
Dribbles130 actions+0.10
Shots102 actions+3.90
Ball wins566 actions+2.91
Most on-ball value (OBV)ActionsOBVper 100
Vanderson685+2.23+0.33
Paris Brunner160+1.27+0.80
Matthis Abline110+1.26+1.15
Aleksandr Golovin431+1.25+0.29
Stanis Idumbo236+1.06+0.45
Sadibou Sané763+0.91+0.12
Lamine Camara841+0.73+0.09
Eric Dier586+0.71+0.12
123456
Biggest single movesOBV
1  Matthis Abline · pass+0.533
2  Vanderson · pass+0.521
3  Edan Diop · pass+0.332
4  Aleksandr Golovin · pass+0.237
5  Lamine Camara · shot+0.227
6  Paris Brunner · shot+0.221

Set Pieces by Player

Who delivers Monaco’s dead balls and who finishes them this season. 32 corners taken, most by Aleksandr Golovin (15); 14 shots from corners and free kicks, 1 goals, led by Eric Dier (1 goals, 0.32 xG). “Into the box” counts corners that landed inside the penalty area; free-kick takers are the players who shot from one, because this feed does not mark who delivers the others.

PlayerCorners takenInto the boxSet-piece shotsGoalsxGFree-kick shotsPenalties
Aleksandr Golovin1513 (87%)4—0.133—
Flávio Nazinho1111 (100%)1—0.021—
Lamine Camara63 (50%)1—0.12——
Vanderson——3—0.34——
Eric Dier——310.32——
Sadibou Sané——1—0.08——
Matthis Abline——1—0.08——

Top Performers

Ranked by Vision AI Score — the same tactical behavioural evaluation each player's own report page shows (min 216 minutes). Coloured chips are that player's strongest capability blocks.

playerposminVision AI Scorecapability blocks
VandersonFB6887.1Ball-Winning 87%Ball-Carrying 69%Wide Delivery 28%
Lamine CamaraCM7596.6Ball-Carrying 74%Successful Dribble 69%Progression 64%Chance Creation 56%
Eric DierCB5696.3Ball-Carrying 77%Progression 76%Ball-Winning 32%Aerial & Box Defending 28%
Flávio NazinhoFB7445.6Ball-Winning 57%Ball-Carrying 38%Wide Delivery 14%
Lukás HrádeckyGK7595.2Shot-Stopping 52%
Sadibou SanéCB7594.9Ball-Carrying 93%Progression 67%Ball-Winning 64%Aerial & Box Defending 35%
Denis ZakariaCM6144.5Ball-Carrying 63%Successful Dribble 55%Progression 36%Chance Creation 13%
Paris BrunnerST4904.0Box Threat 76%Ball-Carrying 59%Finishing 43%Aerial Threat 0%
Aleksandr GolovinAM7353.8Ball-Carrying 43%Chance Creation 27%Box Threat 24%

Top Creators

Chance creation leaders — key passes /90 and box entries.

playerposminkey P /90box entriesxT
Aleksandr GolovinM7352.20278.5
VandersonD6881.311714.6
Flávio NazinhoD7441.211113.4
Matthis AblineF3860.93101.2
Mamadou CoulibalyM2531.07102.7
Lamine CamaraM7590.4789.6
Stanis IdumboM3591.0052.8
Paris BrunnerF4900.9231.4

Wide Delivery Leaders

Crosses and cutbacks — wingers and full-backs only (by real average position, not the raw D/M position code), ranked by combined volume.

playerposmincrosses (completed)cutbacks (completed)
VandersonFB68816 (44%)12 (25%)
Flávio NazinhoFB74414 (21%)5 (0%)
Stanis IdumboW3591 (0%)6 (50%)
Mamadou CoulibalyW2534 (50%)3 (67%)
Mathys DetourbetW2314 (25%)2 (50%)

Defensive Volume Leaders

Who does the defensive work — tackles, interceptions, recoveries.

playerposmindef actionstacklesinterceptrecoveries
Lamine CamaraM759175301170
VandersonD688138291338
Flávio NazinhoD744132141637
Sadibou SanéD7591248544
Eric DierD5691027425
Denis ZakariaM6148112522
Aleksandr GolovinM7357414524
Mamadou CoulibalyM253566416

Full Squad Data

Every player with 100+ minutes (16 players).

playerposappsmingoalsxGkey PprogdefxT
Lukás HrádeckyG875900.0020255.6
Lamine CamaraM875900.44271759.6
Sadibou SanéD875900.124012411.6
Flávio NazinhoD874410.4105813213.4
Aleksandr GolovinM873500.51833748.5
VandersonD868800.5108213814.6
Denis ZakariaM861400.3219814.2
Eric DierD656910.42241027.8
Paris BrunnerF649042.954551.4
Matthis AblineF738610.946441.2
Stanis IdumboM535910.8426532.8
Mamadou CoulibalyM425300.0310562.7
Mathys DetourbetM623100.3119303.4
Edan DiopM421220.336451.6
Jordan TezeD316100.0214213.4
Ansu FatiM310000.311100.4

Player Role Architecture

The question
Who does what — and what tactical job does each key player actually perform?

The nine most-used players (450+ minutes), each with the role their event fingerprint points to — not just their listed position.

Lukás Hrádecky
Goalkeeper · Goalkeeper
minutes759
goals · xG0 · 0.0
prog passes /902.4
key passes /900.00
def actions /903.0
pass accuracy83%
Lamine Camara
Ball-winner / destroyer · Midfielder
minutes759
goals · xG0 · 0.4
prog passes /903.2
key passes /900.47
def actions /9020.8
pass accuracy91%
Sadibou Sané
Ball-playing defender · Defender
minutes759
goals · xG0 · 0.1
prog passes /904.7
key passes /900.24
def actions /9014.7
pass accuracy94%
Flávio Nazinho
Ball-playing defender · Defender
minutes744
goals · xG1 · 0.4
prog passes /907.0
key passes /901.21
def actions /9016.0
pass accuracy81%
Aleksandr Golovin
Advanced creator · Attacking mid
minutes735
goals · xG0 · 0.5
prog passes /904.0
key passes /902.20
def actions /909.1
pass accuracy76%
Vanderson
Ball-playing defender · Defender
minutes688
goals · xG0 · 0.5
prog passes /9010.7
key passes /901.31
def actions /9018.1
pass accuracy79%
Denis Zakaria
Ball-winner / destroyer · Midfielder
minutes614
goals · xG0 · 0.3
prog passes /902.8
key passes /900.29
def actions /9011.9
pass accuracy92%
Eric Dier
Stopper / marker · Defender
minutes569
goals · xG1 · 0.4
prog passes /903.8
key passes /900.32
def actions /9016.1
pass accuracy92%
Paris Brunner
Poacher / finisher · Forward
minutes490
goals · xG4 · 2.9
prog passes /900.7
key passes /900.92
def actions /9010.1
pass accuracy67%

Recruitment Intelligence — profiles to sign

The question
If you were sporting director, which player profiles would move the needle most?

Each recruitment target is derived directly from the team's weakest areas vs the league — the gap, why it shows in the data, and the profile that closes it.

Need — bottom-third: xG created /game (11th pct)
Sign a higher-volume goalscorer or chance creator. The data flags this because the attack under-produces xG (1.09, 11th percentile vs the league).
Need — below-average: shots /game (39th pct)
Sign a shot-taking forward to raise shot volume. The data flags this because shot volume is low (12.75, 39th percentile vs the league).
Need — below-average: press intensity (PPDA) (44th pct)
Sign a high-energy presser to lift defensive intensity. The data flags this because the press is passive (5.70, 44th percentile vs the league).
Vision AI · building report