Combining every season — this one is larger, give it a moment.
Team reportSerie A 2026
AC Milan
A balanced-territory side, aggressive high-press out of possession, with a high-volume attack. Currently #5 of 20 on 18 points (5-3-1, 18:11). Tactical profile below is built from 9 matches of ball-by-ball event data, contextualised against the league.
In one read, what kind of team is AC Milan and how good was the season?
Read. AC Milan currently sit #5 of 20 on 18 points (5W-3D-1L, 2.00 pts/game), scoring 18 and conceding 11 for a goal difference of +7.
On the ball they are a balanced-territory team (52% field tilt, #8 in the league); off it they defend with a aggressive high-press (#6 of 20 for press intensity).
Across the 9 matches of event data, they create 1.68 xG/game (#5) and concede 0.96 (#2), a net of +0.71 xG/game — the underlying performance matches the actual goal difference.
Season Context — the league table
The question
Where did AC Milan actually finish, and against whom?
The authoritative final table (full season), centred on AC Milan.
#
team
P
W
D
L
GF
GA
GD
Pts
2
Lazio
9
8
1
0
22
7
+15
25
3
Roma
9
6
3
0
23
8
+15
21
4
Como
9
6
2
1
17
8
+9
20
5
AC Milan
9
5
3
1
18
11
+7
18
6
Napoli
9
4
2
3
17
11
+6
14
7
Frosinone
8
4
2
2
17
13
+4
14
8
Juventus
9
4
2
3
11
10
+1
14
Match Momentum — when they create and concede danger
The question
Across a whole game, which periods does AC Milan 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
AC Milan tend to fade after a strong start — first-half net xG +3.42, second-half +2.98. Their strongest period is 46-60' (net +2.42 xG); their most dangerous period to face is 76-90+' (net -0.60 xG against them).
xG Race — the season, match by match
Expected goals piling up over 9 matches: AC Milan 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 (+6.4 xG). Hover a point for that match.
AC MilanOpponents against AC Milan
Tactical DNA
The football question
In five genes, what is AC Milan'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
58
Progression
moving the ball upfield with threat
48
Creation
manufacturing chances
65
Press
winning the ball high & hard
90
Solidity
resisting the opponent's threat
Genetic identity
AC Milan are defined most by their Solidity DNA (90/100 · elite at resisting the opponent's threat), and least by their Creation DNA (48/100). 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
91%
Possession
74%
Field tilt
57%
Disrupt
Central progression ~proxy
39%
Dribble tendency
43%
Progressive passes
78%
Finish
Final-third patience
74%
Shot quality
96%
Open-play xG
70%
Press
Forward press ~proxy
26%
High regains
52%
Pressing efficiency ~proxy
30%
Defend
Def-mid press ~proxy
17%
High line ~proxy
17%
PAdj defensive actions
17%
AC Milan is defined most by Finish (80% in the league) and weakest on Defend (17%). Percentiles vs the 23 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 AC Milan actually control, relative to the rest of the league?
Field tilt = share of final-third touches between the two sides across AC Milan'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; AC Milan highlighted. Hover any dot for the team.
Vision AI Model Insight
AC Milan post 52% field tilt, ranking #8 of 20 in the league — comfortably above the league mid-point for territorial control, and their 2.00 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 AC Milan has the ball, how much of that time is spent building, progressing and creating?
The share of AC Milan'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
31.7%
Progression
52.8%
Chance creation
14.4%
Attacking transition
1.1%
Vision AI Model Insight
Progression is where AC Milan 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
Pavlovic ↔ Winter
160p · -0.8xT
Winter ↔ Gila
157p · -1.0xT
Maignan ↔ Pavlovic
146p · 0.7xT
Chukwueze ↔ Gila
129p · -1.1xT
Estupiñán ↔ Pavlovic
113p · -0.8xT
Modric ↔ Pavlovic
106p · 0.4xT
Maignan ↔ Winter
106p · -0.6xT
Modric ↔ Winter
93p · 0.7xT
Musah ↔ Winter
86p · -1.3xT
Pavlovic ↔ Jashari
82p · 0.5xT
Vision AI Model Insight
Busiest connection: Strahinja Pavlovic ↔ Koni De Winter (160 passes). Most dangerous per pass: Luka Modric ↔ Christian Pulisic (0.02 xT/pass over 43 passes) — the combination that hurts opponents most when it fires, not just the one used most.
Touch Density — where AC Milan had the ball
Every touch across 88 matches. Brighter means more touches.
Which Zones Do They Attack Through?
The question
Across the whole pitch, where does AC Milan 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
5719
ACTIONS
Carries
the ball moved with a player
1463
ACTIONS
Vision AI Model Insight
Their single busiest zone is the centre of the middle third (11.9% 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: 32% defensive, 53% middle, 15% 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. AC Milan leaned on their left (41%). The bars under the pitch put the league’s share beside theirs.
AC Milan
41%23%36%
League
38%24%38%
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. AC Milan made 343 this season (38.1 a match); 30% 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
102
11.3
30%
29%
Left half-space
59
6.6
17%
15%
Centre
38
4.2
11%
13%
Right half-space
53
5.9
15%
15%
Right wing
91
10.1
27%
28%
Build-up2 sections
Build-Up — the pass map
The football question
How does AC Milan 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.
1824build-up passes attempted
89%completed
24%reach the middle third
1%go long into the final third
Pass types
784CB · 49%
62Long Wide · 4%
10Long Central · 1%
586Wide → FB · 36%
173Central · 11%
Sequence success — build-up
87build-up sequences this season
10%succeed — reach the next third or a shot
32%turned over with nothing to show
CB784 · 49%
Long Wide62 · 4%
Long Central10 · 1%
Wide → FB586 · 36%
Central173 · 11%
◆ Vision AI Insight
AC Milan build predominantly down the left, with Pavlovic as the fulcrum (332 build-up passes). Only 25% 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 LHS def → C def (111×).
Pass Explorer
Every pass of AC Milan’s last 9 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. 409 progressive passes across 9 matches (45.4/game).
Origin lane
Right wing
25%
Right half-space
14%
Central
13%
Left half-space
11%
Left wing
36%
Progression is left-dominant: 40% right side · 13% central · 47% left.
Who generates it
Samuel Chukwueze
45 · 11%
Luka Modric
44 · 11%
Mario Gila
38 · 9%
Strahinja Pavlovic
30 · 7%
Mike Maignan
29 · 7%
Top two — Samuel Chukwueze & Luka Modric — account for 22% of all progression.
PROGRESSION
45.4 prog passes/game
mostly left
→
PENETRATION
23% reach final third
19% reach the box
→
THREAT
the other 77% is recycled
progress ≠ danger
What came of it.23% of progressive passes reached the final third and 19% the box; the rest was recycled in front of the block. 22% of the progression ran through two players, mostly down the left 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 wingbalanced
Right half-spacebalanced
Centralbalanced
Left half-spacebalanced
Left wingefficient
Progression — the pass map
The football question
Once AC Milan 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.
3034progression passes attempted
90%completed
9%break into the final third
12%go backward into the defensive third
Pass types
843CB · 31%
78Long Wide · 3%
12Long Central · 0%
1411Wide → FB · 52%
382Central · 14%
Sequence success — progression
170progression sequences this season
24%succeed — reach the next third or a shot
36%turned over with nothing to show
CB843 · 31%
Long Wide78 · 3%
Long Central12 · 0%
Wide → FB1411 · 52%
Central382 · 14%
◆ Vision AI Insight
AC Milan progress predominantly down the left, with Modric the busiest midfield outlet (330 progression passes). Only 9% of passes that start in midfield actually break the final-third line — the rest (78%) is recycled back into midfield or (12%) played backward, the hallmark of a side that carries progression itself through midfield rather than just passing through it. Their most-repeated pattern is LW mid → LHS mid (172×) — 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)
10%
Progressive
1%
Neutral / sideways
48%
Safe / backward
41%
11% of passes are progressive-or-better; the remaining 89% keep possession without advancing it. A control-first side — safe in build-up, reliant on a few players for the incision.
Where does AC Milan actually progress the ball with their feet, not a pass?
290 progressive carries this season across the whole squad (20% of 1434 tracked carries total), grouped into up to 4 spatial patterns by where each carry starts and ends together — not just its origin zone. Median 16m, longest 60m. Most active carrier: Pavlovic (37). Filter to one player below.
First (81)Second (80)Third (76)Fourth (53)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 AC Milan'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.
113shots
15.1xG
0.13xG per shot
14big chances (0.3+ xG)
17goals
#14key passes / game in the league
#12box entries / game in the league
Kind of attack, then how the shot was set up
Shots
Goals
xG
xG / shot
Attacking transition28% of shots
32
4
3.47
0.11
Cross
6
1
0.69
0.11
Cut-back
1
0
0.13
0.13
Long ball
1
1
0.10
0.10
Pass into the box / short pass
13
1
1.63
0.13
Own creation (carry, dribble, rebound)
11
1
0.93
0.08
Settled attack58% of shots
66
11
8.02
0.12
Cross
10
0
1.31
0.13
Cut-back
4
0
0.44
0.11
Long ball
7
2
0.78
0.11
Pass into the box / short pass
32
7
3.29
0.10
Own creation (carry, dribble, rebound)
13
2
2.21
0.17
Set piece13% of shots
15
2
3.59
0.24
Corner
13
1
2.00
0.15
Penalty
2
1
1.59
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
Samuel Chukwueze
11
1.49
2
Christian Pulisic
9
0.92
1
Davide Bartesaghi
5
0.78
0
Alexis Saelemaekers
4
0.77
1
Fikayo Tomori
1
0.48
0
Who takes them
Player
Shots
xG
Goals
Gonçalo Ramos
25
4.20
2
Adrien Rabiot
9
1.06
4
Mario Gila
2
1.03
1
Christian Pulisic
9
0.99
2
Alphadjo Cisse
7
0.98
3
Vision AI Model Insight
The biggest source of AC Milan's chances, by xG, is settled attack (8.0 of 15.1 xG, 66 of 113 shots), mostly pass into the box / short pass. 32 of the 113 shots (28%) came within 15 seconds of winning the ball. 15 of the 113 shots came from set pieces; 17 goals from 15.1 xG (+1.9).
Chance Creation — the pass map
The football question
How does AC Milan 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
7CB · 8%
4Long Wide · 5%
8Long Central · 10%
25Wide → FB · 30%
39Central · 47%
Sequence success — final-third
69final-third sequences this season
29%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
CB7 · 8%
Long Wide4 · 5%
Long Central8 · 10%
Wide → FB25 · 30%
Central39 · 47%
Who creates — volume × threat
12Chukwueze
10Pulisic
9Modric
7Loftus-Cheek
6Bartesaghi
6Rabiot
5Gila
83key passes #14 of 20
12.3box entries/game #12 of 20
28.9final-3rd entries/game #14 of 20
1.68xG/game #5 of 20
17goals #4 of 20
+1.9finishing vs xG #8 of 20
◆ Vision AI Insight
AC Milan lean left — 43% of their key passes come from that flank and its half-space (36 of 83), most often from Modric (7). They funnel chances into the c of the final third, most often via LW att → C att (12 key passes, most often from Modric). Chukwueze is the primary source overall. They generate 1.68 xG a game and finish it clinical — they beat their xG, led by Adrien Rabiot (4G), Alphadjo Cissè (4G), Diego Moreira (3G).
Crosses & Cutbacks — squad-wide
The question
How does AC Milan deliver into the box from wide areas, and what comes of it?
116 crosses: 38 found a team-mate (33%), 17 led to a shot (15%). 18 cut-backs: 12 found a team-mate (67%), 5 led to a shot (28%). 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 AC Milan actually cross from?
116 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 byline left wing; 6 come from the half-spaces and 0 from the centre.
Territory
Crosses
Share
Led to a shot
byline · left wing
31
27%
5
byline · right wing
28
24%
7
advanced · right wing
19
16%
2
advanced · left wing
15
13%
3
deep · left wing
10
9%
0
deep · right wing
7
6%
0
byline · left half-space
2
2%
0
deep · right half-space
1
1%
0
advanced · right half-space
1
1%
0
advanced · left half-space
1
1%
0
byline · right half-space
1
1%
0
Finishing3 sections
Shot Map
113 shots for 15.1 xG (1.68/game, #5 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.
High-volume attack. 12.6 shots/game at 0.133 xG/shot. Box entries 12/game (#12). Threat created 11.0 xT/game (#9).
Goal Sequences
The move behind each of AC Milan’s 16 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 — 16 goals traced.
How Do They Score?
The question
Where do the goals actually come from — open play, dead balls, which foot?
Every goal AC Milan scored across the loaded matches, broken down by how it was created and finished.
By situation
Open play
15 · 88%
Set pieces
1 · 6%
Penalties
1 · 6%
By finish
Right foot
8 · 47%
Left foot
8 · 47%
Header
1 · 6%
Vision AI Model Insight
17 goals scored. 88% 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
196
Failed forward pass
141
Failed take-on
106
Lost in the defensive third
209
Their weakest zone is the left half-space in the attacking third: 26% of 161 pass attempts there are lost (42 times). Separately, 27% 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 AC Milan 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.
52
Pressing Index /100 · average
Intensity37
PPDA, high-press share, high-zone actions
Height55
how high up the pitch they defend
Effectiveness50
high recoveries & high-regain share
Disruption75
opponent completion & progression allowed
Every defensive action & where it happens
Where they win the ball back — by lane
RW
16%
RHS
21%
C
22%
LHS
23%
LW
18%
By third (high / mid / low regain)
Def 46%
Mid 40%
Att 14%
Read
Ranked #6 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, 53% are won high up the pitch.
Every pressing metric, ranked in the league
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#6/20
lower = more intense; rank is what matters
Defensive actions / game
143
#12/20
High-press share
20%
#15/20
def actions won high up
Defensive-action height (0–100)
38.7
#9/20
avg pitch height of actions
High recoveries / game
22.7
#8/20
ball won in mid+att third
High-recovery share
53%
#12/20
share of recoveries won high
Opponent completion allowed
82%
#6/20
lower = more disruptive
Opponent progressive allowed
8.2%
#4/20
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 AC Milan win it back with a defensive action?
Counted over the 298 opposition possessions that AC Milan 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 AC Milan step in. AC Milan win it after 3.8 opponent passes in the first half-hour and after 4.7 after the hour mark (later).
PASSES THEY GET
4.6passes
median 2 · before AC Milan win it
TIME THEY KEEP IT
13sec
median 5s · 33.1 wins per game
How many passes the opponent got in before the ball was won
50%
23%
14%
12%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
3.8
11s
15–30′
3.9
10s
30–45+′
5.7
18s
45–60′
5.0
15s
60–75′
3.7
10s
75–90+′
5.3
16s
average passesaverage seconds
Attitude in each third
Every other style number here says how much AC Milan 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 AC Milan 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) 1,824 passes
circulating, playing short, safer
Width of use+0.2121.43
Wide-channel share+0.270.32
Side bias+0.401.66
Pass length-0.7420.03
Long-ball share-0.940.14
Forward gain per pass-1.096.73
Backward-pass share+1.010.24
Deep-pass share-1.080.18
Completion+1.050.88
Seconds between passes-0.363.11
Side used
left 30% · central 44% · right 26%
Progression (middle third) 3,034 passes
playing short, circulating, shorter passing
Width of use-0.0525.47
Wide-channel share-0.040.40
Side bias+0.291.20
Pass length-1.0715.73
Long-ball share-0.860.07
Forward gain per pass-1.281.67
Backward-pass share+0.860.37
Deep-pass share-1.320.10
Completion+0.840.90
Seconds between passes-0.193.11
Side used
left 36% · central 31% · right 33%
Final third 861 passes
safer
Width of use-0.2930.90
Wide-channel share-0.300.56
Side bias+0.422.52
Pass length+0.0115.48
Long-ball share-0.370.08
Forward gain per pass-0.81-0.04
Backward-pass share+0.610.43
Deep-pass share-0.610.07
Completion+0.860.77
Seconds between passes+0.213.56
Side used
left 46% · central 16% · right 39%
Reference measured across roughly 3,500 match-teams
in this competition, per third, so the comparison is with AC Milan'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 AC Milan 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 share15%
#17 of 20 · reaches final third in ≤3 passes
Patient build-up share36%
#4 of 20 · spells of 6+ passes
Tempo (possession length)22.4s
#18 of 20 · shorter = faster tempo
Passes per possession5.8
#4 of 20
Counter-attack shots /game1.04
#8 of 20 · shot within 12s of a regain in own half
Press→shot /game2.36
#19 of 20 · shot within 8s of a high regain
Speed to final third after regain14.6s
#19 of 20 · lower = faster break
Sequence success — attacking transition
9attacking transition sequences this season
22%succeed — produce a shot
44%turned over with nothing to show
Sequence success — defensive transition
22defensive transition sequences this season
68%succeed — reach the next third or a shot
0%turned over with nothing to show
Vision AI Model Insight
AC Milan are a patient build-up side: 15% of possessions reach the final third in 3 passes or fewer (#17 of 20), against 36% built patiently over 6+ passes (#4 of 20). Winning the ball back leads to a shot inside 12 seconds 1.04 times a game (#8), and a high regain converts to a shot within 8 seconds 2.36 times a game (#19 of 20) — not yet a major source of chances.
Defending the box2 sections
Vulnerability Map — how opponents hurt them
The report flipped: where do opponents create their threat against AC Milan? 309 opponent progressive passes and 88 shots faced (8.7 xG against) across the season.
Opponent threat by lane (attacking into AC Milan)
Right wing
22%
Right half-space
12%
Central
20%
Left half-space
13%
Left wing
32%
Where the danger came from. Opponents generated most of their threat through the left wing (32% of the danger conceded).
How Do They Concede?
The question
Where do the goals against AC Milan actually come from — open play, dead balls, and who supplies them?
Every goal AC Milan conceded across the loaded matches — the opponent's shots in AC Milan'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 (64 total)
By situation
Open play
9 · 90%
Set pieces
1 · 10%
Penalties
0 · 0%
Vision AI Model Insight
10 goals conceded. 90% come in open play; set pieces account for only 10%, so dead-ball defending is not the main issue. 20% 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 (28% 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
15 g · 11.5 xG
Set pieces
1 g · 2.0 xG
Penalties
1 g
6% of goals come from set pieces (1 of 17), worth 2.0 xG from 13 shots. Set pieces are a secondary threat; the danger is in open play.
Where do AC Milan's corners go?
27 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 (8)
Delivered into the box 100%Short 0%
Left side (19)
Delivered into the box 89%Short 11%
Where opponents attack AC Milan from set pieces
6 shots from opponent corners against AC Milan, 1 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 (3)
Shot 3Goals 0
Left flag (3)
Shot 3Goals 1
Tactical Strengths & Weaknesses
Strengths (top-third vs league)
xG conceded /game — 0.96 (90th pct)
pass retention % — 87.53 (85th pct)
xG created /game — 1.68 (75th pct)
press intensity (PPDA) — 4.77 (70th pct)
Weaknesses (bottom-third vs league)
shots /game — 12.56 (30th 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.
Lazio
79%
Como
76%
Napoli
60%
Juventus
58%
Inter
54%
Performance vs Opponent Styles
The question
Not just how AC Milan 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 AC Milan's own passing behaviour to the result.
Results
opponent type
goals for/g
goals against/g
points/g
vs high-pressing opponents (3m)
1.67
0.33
2.33
vs mid-block opponents (3m)
2.33
1.67
2.00
vs low-block opponents (3m)
2.00
1.67
1.67
Behaviour — how AC Milan actually play against each
opponent type
retention
directness (long balls)
prog. passes/g
xT/g
box entries/g
vs high-pressing opponents
85%
10%
46
10.8
11.3
vs mid-block opponents
88%
9%
42
10.9
12.7
vs low-block opponents
89%
9%
48
11.4
13.0
Vision AI Model Insight
Facing a high press, retention drops to 85% vs 89% against a low block (4pt lower under pressure), and directness (long-ball share) sits at 10% when pressed vs 9% against a low block — their passing style barely changes regardless of how the opponent sets up, for better or worse.
Scouting Summary — the team in brief
In brief. AC Milan are balanced-territory with a aggressive high-press.
Their attack creates 1.68 xG a game and they concede 0.96 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 AC Milan’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.
AC Milan
across 9 matches this season
Field tiltat Juventus · 1-1 · 2026-09-0635%avg 52%
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 AC Milan’s last 9 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).
+15.8on-ball value, 9 matches
Where the value came from
Passes5719 actions+4.47
Carries1434 actions+0.79
Dribbles168 actions+0.06
Shots113 actions+7.47
Ball wins543 actions+3.00
Most on-ball value (OBV)
Actions
OBV
per 100
Gonçalo Ramos
301
+2.99
+0.99
Christian Pulisic
345
+1.68
+0.49
Strahinja Pavlovic
976
+1.59
+0.16
Samuel Chukwueze
455
+1.31
+0.29
Diego Moreira
287
+1.07
+0.37
Adrien Rabiot
476
+1.03
+0.22
Ardon Jashari
398
+0.91
+0.23
Pervis Estupiñán
361
+0.77
+0.21
Biggest single moves
OBV
1 Gonçalo Ramos · shot
+0.776
2 Gonçalo Ramos · shot
+0.734
3 Adrien Rabiot · pass
+0.545
4 Fikayo Tomori · pass
+0.468
5 Ardon Jashari · shot
+0.449
6 Alphadjo Cisse · shot
+0.368
Set Pieces by Player
Who delivers AC Milan’s dead balls and who finishes them this season. 27 corners taken, most by Luka Modric (13); 13 shots from corners and free kicks, 1 goals, led by Mario Gila (1 goals, 1.03 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
Luka Modric
13
11 (85%)
—
—
0.00
—
—
Koni De Winter
—
—
4
—
0.57
—
—
Gonçalo Ramos
—
—
1
—
0.05
—
1/2
Pervis Estupiñán
1
1 (100%)
2
—
0.04
—
—
Samuel Chukwueze
4
1 (25%)
1
—
0.03
—
—
Mario Gila
—
—
2
1
1.03
—
—
Christian Pulisic
2
1 (50%)
1
—
0.09
—
—
Davide Bartesaghi
5
5 (100%)
—
—
0.00
—
—
Strahinja Pavlovic
—
—
1
—
0.13
—
—
Francesco Camarda
—
—
1
—
0.06
—
—
Alphadjo Cissè
1
1 (100%)
—
—
0.00
—
—
Diego Moreira
1
0 (0%)
—
—
0.00
—
—
Top Performers
Ranked by Vision AI Score — the same tactical behavioural evaluation each player's own report page shows (min 243 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
Samuel Chukwueze
M
537
2.01
14
6.4
Luka Modric
M
576
1.41
16
13.6
Christian Pulisic
F
397
2.27
14
5.2
Ruben Loftus-Cheek
M
257
2.45
8
1.9
Davide Bartesaghi
M
278
1.94
9
3.4
Adrien Rabiot
M
509
1.06
5
5.0
Diego Moreira
M
323
0.84
8
4.2
Pervis Estupiñán
M
410
0.66
6
5.2
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)
Samuel Chukwueze
W
537
12 (17%)
7 (43%)
Alphadjo Cissè
W
353
1 (100%)
2 (0%)
Defensive Volume Leaders
Who does the defensive work — tackles, interceptions, recoveries.
player
pos
min
def actions
tackles
intercept
recoveries
Strahinja Pavlovic
D
843
152
22
5
33
Koni De Winter
D
824
134
14
10
31
Gonçalo Ramos
F
823
114
8
1
14
Mario Gila
D
612
108
12
7
29
Luka Modric
M
576
87
7
11
40
Yunus Musah
M
613
72
5
5
32
Pervis Estupiñán
M
410
68
9
8
14
Adrien Rabiot
M
509
62
11
1
19
Full Squad Data
Every player with 100+ minutes (20 players).
player
pos
apps
min
goals
xG
key P
prog
def
xT
Mike Maignan
G
9
843
0
0.0
0
29
37
7.5
Strahinja Pavlovic
D
9
843
0
0.6
2
30
152
10.7
Koni De Winter
D
9
824
0
0.6
0
16
134
5.9
Gonçalo Ramos
F
9
823
2
4.2
4
5
114
2.0
Yunus Musah
M
9
613
0
0.5
2
18
72
5.6
Mario Gila
D
7
612
1
1.0
5
38
108
9.4
Luka Modric
M
9
576
0
0.0
9
44
87
13.6
Samuel Chukwueze
M
7
537
0
0.7
12
45
54
6.4
Adrien Rabiot
M
7
509
4
1.1
6
21
62
5.0
Pervis Estupiñán
M
8
410
0
0.4
3
28
68
5.2
Christian Pulisic
F
6
397
2
1.0
10
19
45
5.2
Alphadjo Cissè
M
7
353
4
1.2
3
9
44
2.1
Diego Moreira
M
5
323
3
0.7
3
26
48
4.2
Ardon Jashari
M
7
313
0
0.6
3
14
42
4.9
Davide Bartesaghi
M
5
278
0
0.7
6
21
53
3.4
Ruben Loftus-Cheek
M
7
257
0
0.7
7
10
23
1.9
Omari Hutchinson
M
5
200
1
0.4
2
9
30
1.0
Alexis Saelemaekers
M
4
191
0
0.2
4
11
43
2.8
Fikayo Tomori
M
3
129
0
0.4
1
8
20
1.2
Sankhoun Diawara
D
2
129
0
0.0
0
7
16
1.1
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.
Mike Maignan
Goalkeeper · Goalkeeper
minutes843
goals · xG0 · 0.0
prog passes /903.1
key passes /900.00
def actions /904.0
pass accuracy82%
Strahinja Pavlovic
Stopper / marker · Defender
minutes843
goals · xG0 · 0.6
prog passes /903.2
key passes /900.21
def actions /9016.2
pass accuracy91%
Koni De Winter
Stopper / marker · Defender
minutes824
goals · xG0 · 0.6
prog passes /901.7
key passes /900.00
def actions /9014.6
pass accuracy95%
Gonçalo Ramos
Poacher / finisher · Forward
minutes823
goals · xG2 · 4.2
prog passes /900.5
key passes /900.44
def actions /9012.5
pass accuracy77%
Yunus Musah
Ball-winner / destroyer · Midfielder
minutes613
goals · xG0 · 0.5
prog passes /902.6
key passes /900.29
def actions /9010.6
pass accuracy92%
Mario Gila
Ball-playing defender · Defender
minutes612
goals · xG1 · 1.0
prog passes /905.6
key passes /900.74
def actions /9015.9
pass accuracy91%
Luka Modric
Deep-lying playmaker · Midfielder
minutes576
goals · xG0 · 0.0
prog passes /906.9
key passes /901.41
def actions /9013.6
pass accuracy91%
Samuel Chukwueze
Advanced creator · Attacking mid
minutes537
goals · xG0 · 0.7
prog passes /907.5
key passes /902.01
def actions /909.1
pass accuracy76%
Adrien Rabiot
Ball-winner / destroyer · Midfielder
minutes509
goals · xG4 · 1.1
prog passes /903.7
key passes /901.06
def actions /9011.0
pass accuracy87%
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: shots /game (30th pct)
Sign a shot-taking forward to raise shot volume. The data flags this because shot volume is low (12.56, 30th percentile vs the league).
Need — below-average: box entries /game (40th pct)
Sign a penetrative wide player or runner to create box entries. The data flags this because few entries into the box (12.33, 40th percentile vs the league).