Combining every season — this one is larger, give it a moment.
Team reportLigue 1 2025/26
Monaco
A balanced-territory side, aggressive high-press out of possession, with a moderate attack. Finished #7 of 20 on 69 points (20-9-13, 71:61). Tactical profile below is built from 42 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 finished #7 of 20 on 69 points (20W-9D-13L, 1.64 pts/game), scoring 71 and conceding 61 for a goal difference of +10.
On the ball they are a balanced-territory team (54% field tilt, #6 in the league); off it they defend with a aggressive high-press (#3 of 20 for press intensity).
Across the 42 matches of event data, they create 1.40 xG/game (#6) and concede 1.30 (#7), a net of +0.10 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
4
Rennes
42
22
9
11
77
64
+13
75
5
Lyon
42
22
8
12
66
47
+19
74
6
Marseille
42
22
5
15
81
58
+23
71
7
Monaco
42
20
9
13
71
61
+10
69
8
Strasbourg
42
17
9
16
70
65
+5
60
9
Paris FC
42
15
13
14
60
59
+1
58
10
Lorient
42
14
15
13
55
57
-2
57
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 +5.97, second-half -1.74. Their strongest period is 0-15' (net +4.05 xG); their most dangerous period to face is 76-90+' (net -1.58 xG against them).
xG Race — the season, match by match
Expected goals piling up over 42 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 (+4.2 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.
60
Build-Up
possession control from the back
75
Progression
moving the ball upfield with threat
70
Creation
manufacturing chances
78
Press
winning the ball high & hard
65
Solidity
resisting the opponent's threat
Genetic identity
Monaco are defined most by their Press DNA (78/100 · strong at winning the ball high & hard), and least by their Build-Up DNA (60/100). Percentiles vs the 20 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 54% field tilt, ranking #6 of 20 in the league — comfortably above the league mid-point for territorial control, and their 1.64 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
21.1%
Progression
53.0%
Chance creation
23.6%
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
Kehrer ↔ Teze
360p · 14.5xT
Zakaria ↔ Kehrer
318p · 9.5xT
Teze ↔ Camara
257p · 12.5xT
Teze ↔ Akliouche
246p · 14.6xT
Kehrer ↔ Camara
238p · 8.0xT
Akliouche ↔ Camara
194p · 9.0xT
Zakaria ↔ Teze
171p · 8.0xT
Zakaria ↔ Camara
157p · 3.9xT
Kehrer ↔ Köhn
153p · 8.1xT
Kehrer ↔ Akliouche
150p · 9.3xT
Vision AI Model Insight
Busiest connection: Thilo Kehrer ↔ Jordan Teze (360 passes). Most dangerous per pass: Folarin Balogun ↔ Maghnes Akliouche (0.07 xT/pass over 103 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 378 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
22151
ACTIONS
Carries
the ball moved with a player
5960
ACTIONS
Vision AI Model Insight
Their single busiest zone is the centre of the middle third (12.9% of all actions). 62% of actions run through the inside lanes (centre + both half-spaces) versus 38% 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
39%22%39%
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 1924 this season (45.8 a match); 27% came into the left 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
525
12.5
27%
30%
Left half-space
272
6.5
14%
15%
Centre
312
7.4
16%
11%
Right half-space
291
6.9
15%
14%
Right wing
524
12.5
27%
31%
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.
5374build-up passes attempted
81%completed
30%reach the middle third
1%go long into the final third
Pass types
1682CB · 38%
274Long Wide · 6%
45Long Central · 1%
1799Wide → FB · 41%
569Central · 13%
Sequence success — build-up
1053build-up sequences this season
11%succeed — reach the next third or a shot
41%turned over with nothing to show
CB1682 · 38%
Long Wide274 · 6%
Long Central45 · 1%
Wide → FB1799 · 41%
Central569 · 13%
◆ Vision AI Insight
Monaco build predominantly down the left, with Hrádecky as the fulcrum (550 build-up passes). Only 31% 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 (287×).
Pass Explorer
Every pass of Monaco’s last 10 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. 1990 progressive passes across 42 matches (47.4/game).
Origin lane
Right wing
34%
Right half-space
14%
Central
12%
Left half-space
14%
Left wing
26%
Progression is right-dominant: 48% right side · 12% central · 40% left.
Who generates it
Jordan Teze
217 · 11%
Vanderson
167 · 8%
Lamine Camara
157 · 8%
Caio Henrique
150 · 8%
Maghnes Akliouche
133 · 7%
Top two — Jordan Teze & Vanderson — account for 19% of all progression.
PROGRESSION
47.4 prog passes/game
mostly right
→
PENETRATION
27% reach final third
21% reach the box
→
THREAT
the other 73% is recycled
progress ≠ danger
What came of it.27% of progressive passes reached the final third and 21% the box; the rest was recycled in front of the block. 19% 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
Centralbalanced
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.
11682progression passes attempted
88%completed
15%break into the final third
9%go backward into the defensive third
Pass types
3353CB · 33%
323Long Wide · 3%
59Long Central · 1%
4969Wide → FB · 48%
1556Central · 15%
Sequence success — progression
1901progression sequences this season
26%succeed — reach the next third or a shot
40%turned over with nothing to show
CB3353 · 33%
Long Wide323 · 3%
Long Central59 · 1%
Wide → FB4969 · 48%
Central1556 · 15%
◆ Vision AI Insight
Monaco progress predominantly down the right, with Camara the busiest midfield outlet (1055 progression passes). Only 15% of passes that start in midfield actually break the final-third line — the rest (77%) 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 (566×) — 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
49%
Safe / backward
36%
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?
1135 progressive carries this season across the whole squad (22% of 5143 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 59m. Most active carrier: Kehrer (97). Filter to one player below.
First (362)Second (347)Third (241)Fourth (185)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.
528shots
58.9xG
0.11xG per shot
28big chances (0.3+ xG)
67goals
#7key 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 transition29% of shots
155
24
16.05
0.10
Cross
16
0
1.72
0.11
Cut-back
11
2
1.39
0.13
Long ball
10
0
0.88
0.09
Pass into the box / short pass
80
16
8.31
0.10
Own creation (carry, dribble, rebound)
38
6
3.75
0.10
Settled attack53% of shots
279
33
31.16
0.11
Cross
51
9
6.61
0.13
Cut-back
21
3
2.66
0.13
Long ball
9
1
1.19
0.13
Pass into the box / short pass
141
13
15.45
0.11
Own creation (carry, dribble, rebound)
57
7
5.25
0.09
Set piece18% of shots
94
10
11.67
0.12
Corner
75
4
5.77
0.08
Free kick
12
0
0.34
0.03
Penalty
7
6
5.56
0.79
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
Maghnes Akliouche
39
4.57
6
Jordan Teze
30
4.53
5
Vanderson
21
2.75
2
Aleksandr Golovin
34
2.71
7
Lamine Camara
21
2.66
3
Who takes them
Player
Shots
xG
Goals
Folarin Balogun
82
12.55
13
Ansu Fati
48
9.05
11
Maghnes Akliouche
48
5.14
6
Aleksandr Golovin
45
3.76
5
Paris Brunner
23
3.18
4
Vision AI Model Insight
The biggest source of Monaco's chances, by xG, is settled attack (31.2 of 58.9 xG, 279 of 528 shots), mostly pass into the box / short pass. 155 of the 528 shots (29%) came within 15 seconds of winning the ball. 94 of the 528 shots came from set pieces; 67 goals from 58.9 xG (+8.1).
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
18CB · 5%
15Long Wide · 4%
48Long Central · 12%
116Wide → FB · 30%
194Central · 50%
Sequence success — final-third
1337final-third sequences this season
20%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
CB18 · 5%
Long Wide15 · 4%
Long Central48 · 12%
Wide → FB116 · 30%
Central194 · 50%
Who creates — volume × threat
42Akliouche
40Golovin
37Teze
37Camara
21Vanderson
21Henrique
20Balogun
391key passes #7 of 20
14.6box entries/game #4 of 20
37.0final-3rd entries/game #4 of 20
1.40xG/game #6 of 20
67goals #5 of 20
+8.1finishing vs xG #4 of 20
◆ Vision AI Insight
Monaco lean right — 40% of their key passes come from that flank and its half-space (157 of 391), most often from Teze (23). They funnel chances into the c of the final third, most often via LHS att → C att (54 key passes, most often from Henrique). Akliouche is the primary source overall. They generate 1.40 xG a game and finish it clinical — they beat their xG, led by Folarin Balogun (13G), Ansu Fati (11G), Maghnes Akliouche (6G).
Crosses & Cutbacks — squad-wide
The question
How does Monaco deliver into the box from wide areas, and what comes of it?
581 crosses: 205 found a team-mate (35%), 75 led to a shot (13%). 147 cut-backs: 63 found a team-mate (43%), 32 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?
581 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. 24% come from one territory, the advanced right wing; 29 come from the half-spaces and 0 from the centre.
Territory
Crosses
Share
Led to a shot
advanced · right wing
137
24%
19
byline · right wing
124
21%
23
byline · left wing
110
19%
16
advanced · left wing
100
17%
9
deep · right wing
42
7%
3
deep · left wing
39
7%
0
byline · left half-space
9
2%
3
advanced · right half-space
6
1%
1
advanced · left half-space
5
1%
0
byline · right half-space
5
1%
1
deep · right half-space
3
1%
0
deep · left half-space
1
0%
0
Finishing3 sections
Shot Map
528 shots for 58.9 xG (1.40/game, #6 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.
Moderate attack. 12.6 shots/game at 0.112 xG/shot. Box entries 15/game (#4). Threat created 11.6 xT/game (#5).
Goal Sequences
The move behind each of Monaco’s 71 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 — 71 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
57 · 85%
Set pieces
4 · 6%
Penalties
6 · 9%
By finish
Right foot
37 · 55%
Left foot
19 · 28%
Header
11 · 16%
Vision AI Model Insight
67 goals scored. 85% come in open play; set pieces add 6%. 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
1394
Failed forward pass
938
Failed take-on
394
Lost in the defensive third
1005
Their weakest zone is the right half-space in the attacking third: 31% of 850 pass attempts there are lost (265 times). Separately, 36% 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
Intensity72
PPDA, high-press share, high-zone actions
Height65
how high up the pitch they defend
Effectiveness85
high recoveries & high-regain share
Disruption52
opponent completion & progression allowed
Every defensive action & where it happens
Where they win the ball back — by lane
RW
20%
RHS
20%
C
23%
LHS
20%
LW
17%
By third (high / mid / low regain)
Def 46%
Mid 40%
Att 14%
Read
Ranked #3 of 20 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, 58% are won high up the pitch.
Every pressing metric, ranked in the league
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#3/20
lower = more intense; rank is what matters
Defensive actions / game
141
#6/20
High-press share
23%
#8/20
def actions won high up
Defensive-action height (0–100)
39.0
#7/20
avg pitch height of actions
High recoveries / game
26.9
#3/20
ball won in mid+att third
High-recovery share
58%
#3/20
share of recoveries won high
Opponent completion allowed
80%
#2/20
lower = more disruptive
Opponent progressive allowed
9.1%
#17/20
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 1525 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 4.9 opponent passes in the first half-hour and after 4.0 after the hour mark (sooner).
PASSES THEY GET
4.4passes
median 3 · before Monaco win it
TIME THEY KEEP IT
12sec
median 6s · 36.3 wins per game
How many passes the opponent got in before the ball was won
44%
31%
14%
11%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
4.8
14s
15–30′
5.0
15s
30–45+′
4.5
12s
45–60′
4.0
11s
60–75′
4.0
12s
75–90+′
4.0
12s
average passesaverage seconds
Phases of play
Every moment of the match read twice — what Monaco were doing with the
ball, and what the opposition were doing about it at that exact moment. Built from the
real action sequence in time order, so one possession is split across the phases it
genuinely passed through rather than labelled once by where it began.
6,431phase segments analysed
9.0smedian time before the opposition engage
2.0their passes per defensive action when pressed
#2of 18 for share of defending spent pressing high
High press 11.6%Mid block 14.7%Low block 43.2%Unopposed 30.6%
What opponents do against Monaco
The mirror image, and the part no other section can show: whether teams come
out to press Monaco or sit in and invite them on. A side that is always faced with a low
block is playing a different sport from one that is pressed every week.
High press 7.4%Mid block 13.3%Low block 51.3%Unopposed 28.0%
Which phases actually produce
Phase
Segments
Avg length
Avg actions
Ends in shot
Turned over
Build-up
1,817
4.5s
2.9
0.2%
47.8%
Progression
2,771
7.5s
4.5
0.3%
47.7%
Chance creation
1,710
5.4s
3.3
23.0%
54.3%
Attacking transition
133
6.8s
4.2
1.5%
42.1%
Most common routes through the phases
How a possession actually travelled, in order. The chain is the tactical signature — a move that climbs all three thirds is a different thing from one that never leaves the first.
Route
Times
Ends in shot
progression
713
0.1%
build_up
710
0.3%
chance_creation
392
34.2%
progression → chance_creation
378
25.4%
build_up → progression
362
0.0%
build_up → progression → chance_creation
198
29.3%
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) 5,374 passes
nothing unusual in this zone
Width of use-0.2620.47
Wide-channel share-0.300.29
Side bias-0.35-1.13
Pass length-0.5920.57
Long-ball share-0.450.19
Forward gain per pass-0.3010.15
Backward-pass share-0.370.18
Deep-pass share-0.210.27
Completion+0.210.81
Seconds between passes-0.303.14
Side used
left 25% · central 47% · right 28%
Progression (middle third) 11,682 passes
nothing unusual in this zone
Width of use-0.2025.14
Wide-channel share-0.170.39
Side bias-0.19-0.70
Pass length-0.5416.46
Long-ball share-0.640.08
Forward gain per pass-0.133.41
Backward-pass share-0.080.33
Deep-pass share-0.420.14
Completion+0.450.87
Seconds between passes-0.443.00
Side used
left 34% · central 31% · right 35%
Final third 5,095 passes
nothing unusual in this zone
Width of use-0.4730.45
Wide-channel share-0.460.55
Side bias+0.01-0.39
Pass length-0.1915.18
Long-ball share-0.390.08
Forward gain per pass-0.290.65
Backward-pass share+0.350.41
Deep-pass share-0.040.09
Completion+0.290.72
Seconds between passes+0.203.55
Side used
left 40% · central 19% · right 41%
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%
#9 of 20 · reaches final third in ≤3 passes
Patient build-up share30%
#11 of 20 · spells of 6+ passes
Tempo (possession length)18.3s
#10 of 20 · shorter = faster tempo
Passes per possession4.9
#10 of 20
Counter-attack shots /game1.10
#10 of 20 · shot within 12s of a regain in own half
Press→shot /game3.43
#6 of 20 · shot within 8s of a high regain
Speed to final third after regain11.5s
#5 of 20 · lower = faster break
Sequence success — attacking transition
129attacking transition sequences this season
15%succeed — produce a shot
44%turned over with nothing to show
Sequence success — defensive transition
476defensive transition sequences this season
63%succeed — reach the next third or a shot
9%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 (#9 of 20), against 30% built patiently over 6+ passes (#11 of 20). Winning the ball back leads to a shot inside 12 seconds 1.10 times a game (#10), and a high regain converts to a shot within 8 seconds 3.43 times a game (#6 of 20) — 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? 1800 opponent progressive passes and 553 shots faced (54.6 xG against) across the season.
Opponent threat by lane (attacking into Monaco)
Right wing
26%
Right half-space
14%
Central
16%
Left half-space
14%
Left wing
31%
Where the danger came from. Opponents generated most of their threat through the left wing (31% 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 (416 total)
By situation
Open play
46 · 79%
Set pieces
6 · 10%
Penalties
6 · 10%
Vision AI Model Insight
58 goals conceded. 79% come in open play; set pieces account for only 10%, so dead-ball defending is not the main issue. 12% 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 (21% 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
57 g · 47.2 xG
Set pieces
4 g · 6.1 xG
Penalties
6 g
6% of goals come from set pieces (4 of 67), worth 6.1 xG from 87 shots. Set pieces are a secondary threat; the danger is in open play.
Where do Monaco's corners go?
205 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 (100)
Delivered into the box 95%Short 5%
Left side (105)
Delivered into the box 96%Short 4%
Where opponents attack Monaco from set pieces
90 shots from opponent corners against Monaco, 6 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 (46)
Shot 46Goals 4
Left flag (44)
Shot 44Goals 2
Tactical Strengths & Weaknesses
Strengths (top-third vs league)
press intensity (PPDA) — 5.45 (85th pct)
box entries /game — 14.57 (80th pct)
progression /game — 47.38 (75th pct)
threat created /game — 11.62 (75th pct)
xG created /game — 1.40 (70th pct)
Weaknesses (bottom-third vs league)
none stand out
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.
Lille
83%
Lens
82%
PSG
81%
Rennes
62%
Marseille
51%
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 (14m)
1.57
0.71
2.29
vs mid-block opponents (14m)
2.07
1.64
1.71
vs low-block opponents (14m)
1.43
2.00
0.93
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
79%
14%
42
9.9
11.8
vs mid-block opponents
82%
11%
48
11.4
14.6
vs low-block opponents
86%
8%
52
13.6
17.3
Vision AI Model Insight
Facing a high press, retention drops to 79% vs 86% against a low block (6pt lower under pressure), and directness (long-ball share) sits at 14% 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 1.40 xG a game and they concede 1.30 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.
Progressive passesat Paris FC · 1-4 · 2026-04-1072avg 47
Pass accuracyat PSG · 3-1 · 2026-03-0673%avg 82%
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 12 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).
+16.9on-ball value, 12 matches
Where the value came from
Passes6708 actions+7.76
Carries1589 actions-0.25
Dribbles200 actions-0.15
Shots153 actions+5.15
Ball wins849 actions+4.40
Most on-ball value (OBV)
Actions
OBV
per 100
Vanderson
685
+2.23
+0.33
Paris Brunner
180
+1.56
+0.86
Aleksandr Golovin
558
+1.53
+0.27
Lamine Camara
1199
+1.35
+0.11
Matthis Abline
110
+1.26
+1.15
Stanis Idumbo
236
+1.06
+0.45
Sadibou Sané
763
+0.91
+0.12
Maghnes Akliouche
251
+0.83
+0.33
Biggest single moves
OBV
1 Matthis Abline · pass
+0.533
2 Vanderson · pass
+0.521
3 Simon Adingra · pass
+0.421
4 Edan Diop · pass
+0.332
5 Lamine Camara · pass
+0.279
6 Folarin Balogun · shot
+0.263
Set Pieces by Player
Who delivers Monaco’s dead balls and who finishes them this season. 205 corners taken, most by Lamine Camara (77); 87 shots from corners and free kicks, 4 goals, led by Eric Dier (2 goals, 0.61 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
Lamine Camara
77
67 (87%)
7
—
0.27
5
—
Aleksandr Golovin
26
23 (88%)
7
—
0.22
4
—
Thilo Kehrer
—
—
14
—
1.10
—
—
Caio Henrique
35
27 (77%)
2
—
0.07
2
—
Folarin Balogun
—
—
9
—
0.53
—
3/3
Maghnes Akliouche
21
16 (76%)
4
—
0.17
—
—
Jordan Teze
29
24 (83%)
1
1
0.11
—
—
Ansu Fati
2
0 (0%)
3
—
0.20
—
3/4
Denis Zakaria
—
—
7
—
0.61
—
—
Eric Dier
—
—
5
2
0.61
—
—
Mohammed Salisu
—
—
5
1
0.43
—
—
Wout Faes
—
—
5
—
0.34
—
—
Flávio Nazinho
11
11 (100%)
1
—
0.02
1
—
Vanderson
—
—
4
—
0.38
—
—
Top Performers
Ranked by Vision AI Score — the same tactical behavioural evaluation each player's own report page shows (min 300 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
2454
1.47
74
27.4
Jordan Teze
M
2796
1.19
59
45.5
Lamine Camara
M
2703
1.23
53
39.7
Caio Henrique
D
1657
1.14
42
28.2
Vanderson
D
1359
1.39
33
29.0
Mamadou Coulibaly
M
1746
0.72
37
16.0
Folarin Balogun
F
2458
0.73
29
4.7
Kassoum Ouattara
M
1014
1.42
25
12.9
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)
Caio Henrique
FB
1657
45 (20%)
11 (45%)
Vanderson
FB
1359
33 (45%)
20 (25%)
Defensive Volume Leaders
Who does the defensive work — tackles, interceptions, recoveries.
player
pos
min
def actions
tackles
intercept
recoveries
Lamine Camara
M
2703
529
85
48
228
Thilo Kehrer
D
2570
447
65
30
92
Jordan Teze
M
2796
418
66
31
138
Denis Zakaria
M
2797
381
50
45
123
Mamadou Coulibaly
M
1746
295
43
23
106
Aleksandr Golovin
M
2454
281
40
29
105
Mohammed Salisu
D
1223
281
26
22
57
Vanderson
D
1359
273
53
36
72
Full Squad Data
Every player with 100+ minutes (32 players).
player
pos
apps
min
goals
xG
key P
prog
def
xT
Denis Zakaria
M
32
2797
2
1.9
7
67
381
22.7
Jordan Teze
M
36
2796
1
0.8
37
217
418
45.5
Lamine Camara
M
32
2703
3
1.9
37
157
529
39.7
Thilo Kehrer
D
29
2570
0
1.5
3
125
447
34.4
Folarin Balogun
F
34
2458
13
12.6
20
21
157
4.7
Aleksandr Golovin
M
33
2454
5
3.8
40
98
281
27.4
Lukás Hrádecky
G
25
2283
0
0.0
0
65
64
17.5
Mamadou Coulibaly
M
29
1746
3
1.3
14
60
295
16.0
Philipp Köhn
G
19
1742
0
0.0
0
36
58
10.9
Caio Henrique
D
22
1657
0
0.1
21
150
168
28.2
Vanderson
D
22
1359
0
0.7
21
167
273
29.0
Christian Mawissa
D
20
1316
0
0.3
5
37
234
15.0
Eric Dier
D
15
1315
2
1.0
4
59
205
20.0
Ansu Fati
M
28
1293
11
9.1
16
31
122
9.5
Mika Biereth
F
35
1266
2
2.6
19
16
137
4.5
Mohammed Salisu
D
14
1223
1
0.7
2
37
281
17.0
Wout Faes
D
14
1208
0
0.3
1
37
180
15.3
Krépin Diatta
M
18
1102
0
0.9
13
91
189
16.1
Takumi Minamino
M
16
1033
3
2.5
10
27
94
7.7
Kassoum Ouattara
M
15
1014
0
1.2
16
71
174
12.9
Simon Adingra
M
14
888
3
2.0
13
49
112
8.0
Aladji Bamba
M
18
876
0
0.2
13
41
166
10.3
Sadibou Sané
D
8
759
2
0.5
2
40
124
11.6
Flávio Nazinho
D
8
744
1
0.4
10
58
132
13.4
Paris Brunner
F
13
655
4
3.2
5
5
69
1.8
Stanis Idumbo
M
13
584
2
1.2
7
32
68
3.9
George Ilenikhena
F
15
465
2
1.3
3
5
30
1.4
Matthis Abline
F
7
386
7
8.7
4
6
44
1.2
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
Paul Pogba
M
6
143
0
0.1
0
8
36
2.7
Pape Cabral
M
5
122
0
0.3
0
3
23
1.0
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.
Denis Zakaria
Ball-winner / destroyer · Midfielder
minutes2797
goals · xG2 · 1.9
prog passes /902.2
key passes /900.23
def actions /9012.3
pass accuracy92%
Jordan Teze
Deep-lying playmaker · Midfielder
minutes2796
goals · xG1 · 0.8
prog passes /907.0
key passes /901.19
def actions /9013.5
pass accuracy82%
Lamine Camara
Ball-winner / destroyer · Midfielder
minutes2703
goals · xG3 · 1.9
prog passes /905.2
key passes /901.23
def actions /9017.6
pass accuracy84%
Thilo Kehrer
Stopper / marker · Defender
minutes2570
goals · xG0 · 1.5
prog passes /904.4
key passes /900.11
def actions /9015.7
pass accuracy86%
Folarin Balogun
Poacher / finisher · Forward
minutes2458
goals · xG13 · 12.6
prog passes /900.8
key passes /900.73
def actions /905.7
pass accuracy71%
Aleksandr Golovin
Ball-winner / destroyer · Midfielder
minutes2454
goals · xG5 · 3.8
prog passes /903.6
key passes /901.47
def actions /9010.3
pass accuracy79%
Lukás Hrádecky
Goalkeeper · Goalkeeper
minutes2283
goals · xG0 · 0.0
prog passes /902.6
key passes /900.00
def actions /902.5
pass accuracy74%
Mamadou Coulibaly
Ball-winner / destroyer · Midfielder
minutes1746
goals · xG3 · 1.3
prog passes /903.1
key passes /900.72
def actions /9015.2
pass accuracy84%
Philipp Köhn
Goalkeeper · Goalkeeper
minutes1742
goals · xG0 · 0.0
prog passes /901.9
key passes /900.00
def actions /903.0
pass accuracy79%
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 — below-average: pass retention % (50th pct)
Sign a secure ball-retaining midfielder. The data flags this because possession is turned over cheaply (82.75, 50th percentile vs the league).
← BACK TO SEARCHSeason
