Combining every season — this one is larger, give it a moment.
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.
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.
#
team
P
W
D
L
GF
GA
GD
Pts
1
PSG
8
5
2
1
16
9
+7
17
2
Rennes
8
5
1
2
18
14
+4
16
3
Lille
8
4
3
1
11
6
+5
15
4
Monaco
8
4
3
1
11
7
+4
15
5
Lyon
8
4
2
2
13
7
+6
14
6
Paris FC
8
4
2
2
13
9
+4
14
7
Lens
8
4
1
3
16
12
+4
13
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)
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.
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).
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.
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.
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.
Phase
Share of the time
Build-up
22.8%
Progression
53.2%
Chance creation
21.7%
Attacking transition
2.3%
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.
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
Carries
the ball moved with a player
1084
ACTIONS
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.
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.
Channel
Entries
Per match
Share
League share
Left wing
109
13.6
26%
30%
Left half-space
58
7.2
14%
13%
Centre
60
7.5
14%
11%
Right half-space
66
8.2
16%
14%
Right wing
126
15.8
30%
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%
Long Wide49 · 5%
Long Central12 · 1%
Wide → FB340 · 38%
Central110 · 12%
◆ 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.
From channel
To channel
From third
To third
Outcome
Minutes
Channel to channel · click a cell
Third to third · click a cell
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%
Long Wide82 · 4%
Long Central10 · 0%
Wide → FB1009 · 47%
Central333 · 16%
◆ 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.
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
Kind of attack, then how the shot was set up
Shots
Goals
xG
xG / shot
Attacking transition20% of shots
20
2
1.62
0.08
Cross
1
0
0.21
0.21
Cut-back
1
0
0.11
0.11
Long ball
1
0
0.08
0.08
Pass into the box / short pass
9
2
0.49
0.05
Own creation (carry, dribble, rebound)
8
0
0.73
0.09
Settled attack67% of shots
68
7
6.02
0.09
Cross
18
2
1.65
0.09
Cut-back
7
1
0.77
0.11
Long ball
3
0
0.17
0.06
Pass into the box / short pass
24
3
2.18
0.09
Own creation (carry, dribble, rebound)
16
1
1.26
0.08
Set piece14% of shots
14
1
1.10
0.08
Corner
10
1
0.98
0.10
Free kick
4
0
0.12
0.03
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
Player
Set-ups
xG created
Goals
Vanderson
11
1.65
0
Aleksandr Golovin
14
1.14
3
Flávio Nazinho
7
0.53
0
Stanis Idumbo
5
0.49
0
Matthis Abline
2
0.33
1
Who takes them
Player
Shots
xG
Goals
Paris Brunner
19
2.85
4
Matthis Abline
12
0.89
1
Stanis Idumbo
12
0.77
1
Aleksandr Golovin
11
0.51
0
Vanderson
6
0.48
0
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%
Long Wide4 · 5%
Long Central10 · 14%
Wide → FB16 · 22%
Central37 · 51%
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.
Territory
Crosses
Share
Led to a shot
advanced · right wing
29
27%
5
byline · right wing
23
21%
4
byline · left wing
21
19%
6
advanced · left wing
18
17%
3
deep · left wing
8
7%
0
deep · right wing
7
6%
1
deep · right half-space
1
1%
0
advanced · right half-space
1
1%
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
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.
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
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#10/18
lower = more intense; rank is what matters
Defensive actions / game
160
#7/18
High-press share
25%
#5/18
def actions won high up
Defensive-action height (0–100)
39.7
#6/18
avg pitch height of actions
High recoveries / game
27.6
#2/18
ball won in mid+att third
High-recovery share
57%
#4/18
share of recoveries won high
Opponent completion allowed
84%
#12/18
lower = more disruptive
Opponent progressive allowed
6.9%
#2/18
of 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
Every opponent key pass against them (66 total)
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.
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.
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)
Delivered into the box 100%Short 0%
Left side (21)
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)
Shot 11Goals 2
Left flag (5)
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 type
goals for/g
goals against/g
points/g
vs high-pressing opponents (2m)
1.50
0.50
3.00
vs mid-block opponents (2m)
2.00
1.00
2.00
vs low-block opponents (4m)
1.00
1.00
1.25
Behaviour — how Monaco actually play against each
opponent type
retention
directness (long balls)
prog. passes/g
xT/g
box entries/g
vs high-pressing opponents
80%
13%
47
10.4
10.0
vs mid-block opponents
87%
12%
52
11.5
12.0
vs low-block opponents
87%
8%
50
13.2
17.2
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.]
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
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)
Actions
OBV
per 100
Vanderson
685
+2.23
+0.33
Paris Brunner
160
+1.27
+0.80
Matthis Abline
110
+1.26
+1.15
Aleksandr Golovin
431
+1.25
+0.29
Stanis Idumbo
236
+1.06
+0.45
Sadibou Sané
763
+0.91
+0.12
Lamine Camara
841
+0.73
+0.09
Eric Dier
586
+0.71
+0.12
Biggest single moves
OBV
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.
Player
Corners taken
Into the box
Set-piece shots
Goals
xG
Free-kick shots
Penalties
Aleksandr Golovin
15
13 (87%)
4
—
0.13
3
—
Flávio Nazinho
11
11 (100%)
1
—
0.02
1
—
Lamine Camara
6
3 (50%)
1
—
0.12
—
—
Vanderson
—
—
3
—
0.34
—
—
Eric Dier
—
—
3
1
0.32
—
—
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.
Chance creation leaders — key passes /90 and box entries.
player
pos
min
key P /90
box entries
xT
Aleksandr Golovin
M
735
2.20
27
8.5
Vanderson
D
688
1.31
17
14.6
Flávio Nazinho
D
744
1.21
11
13.4
Matthis Abline
F
386
0.93
10
1.2
Mamadou Coulibaly
M
253
1.07
10
2.7
Lamine Camara
M
759
0.47
8
9.6
Stanis Idumbo
M
359
1.00
5
2.8
Paris Brunner
F
490
0.92
3
1.4
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.
player
pos
min
crosses (completed)
cutbacks (completed)
Vanderson
FB
688
16 (44%)
12 (25%)
Flávio Nazinho
FB
744
14 (21%)
5 (0%)
Stanis Idumbo
W
359
1 (0%)
6 (50%)
Mamadou Coulibaly
W
253
4 (50%)
3 (67%)
Mathys Detourbet
W
231
4 (25%)
2 (50%)
Defensive Volume Leaders
Who does the defensive work — tackles, interceptions, recoveries.
player
pos
min
def actions
tackles
intercept
recoveries
Lamine Camara
M
759
175
30
11
70
Vanderson
D
688
138
29
13
38
Flávio Nazinho
D
744
132
14
16
37
Sadibou Sané
D
759
124
8
5
44
Eric Dier
D
569
102
7
4
25
Denis Zakaria
M
614
81
12
5
22
Aleksandr Golovin
M
735
74
14
5
24
Mamadou Coulibaly
M
253
56
6
4
16
Full Squad Data
Every player with 100+ minutes (16 players).
player
pos
apps
min
goals
xG
key P
prog
def
xT
Lukás Hrádecky
G
8
759
0
0.0
0
20
25
5.6
Lamine Camara
M
8
759
0
0.4
4
27
175
9.6
Sadibou Sané
D
8
759
0
0.1
2
40
124
11.6
Flávio Nazinho
D
8
744
1
0.4
10
58
132
13.4
Aleksandr Golovin
M
8
735
0
0.5
18
33
74
8.5
Vanderson
D
8
688
0
0.5
10
82
138
14.6
Denis Zakaria
M
8
614
0
0.3
2
19
81
4.2
Eric Dier
D
6
569
1
0.4
2
24
102
7.8
Paris Brunner
F
6
490
4
2.9
5
4
55
1.4
Matthis Abline
F
7
386
1
0.9
4
6
44
1.2
Stanis Idumbo
M
5
359
1
0.8
4
26
53
2.8
Mamadou Coulibaly
M
4
253
0
0.0
3
10
56
2.7
Mathys Detourbet
M
6
231
0
0.3
1
19
30
3.4
Edan Diop
M
4
212
2
0.3
3
6
45
1.6
Jordan Teze
D
3
161
0
0.0
2
14
21
3.4
Ansu Fati
M
3
100
0
0.3
1
1
10
0.4
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).