Combining every season — this one is larger, give it a moment.
Team reportLa Liga 2026
Real Madrid
A balanced-territory side, aggressive high-press out of possession, with a high-volume attack. Currently #3 of 20 on 24 points (8-0-2, 25:10). Tactical profile below is built from 10 matches of ball-by-ball event data, contextualised against the league.
In one read, what kind of team is Real Madrid and how good was the season?
Read. Real Madrid currently sit #3 of 20 on 24 points (8W-0D-2L, 2.40 pts/game), scoring 25 and conceding 10 for a goal difference of +15.
On the ball they are a balanced-territory team (52% field tilt, #5 in the league); off it they defend with a aggressive high-press (#13 of 20 for press intensity).
Across the 10 matches of event data, they create 2.05 xG/game (#2) and concede 1.06 (#4), a net of +0.99 xG/game — the underlying performance matches the actual goal difference.
Season Context — the league table
The question
Where did Real Madrid actually finish, and against whom?
The authoritative final table (full season), centred on Real Madrid.
#
team
P
W
D
L
GF
GA
GD
Pts
1
Barcelona
10
9
0
1
40
11
+29
27
2
Atletico
10
8
1
1
20
8
+12
25
3
Real Madrid
10
8
0
2
25
10
+15
24
4
Real Betis
10
6
2
2
16
14
+2
20
5
Real Sociedad
10
6
1
3
19
18
+1
19
6
Sevilla
9
4
2
3
14
16
-2
14
Match Momentum — when they create and concede danger
The question
Across a whole game, which periods does Real Madrid 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
Real Madrid tend to fade after a strong start — first-half net xG +6.97, second-half +2.94. Their strongest period is 31-45' (net +4.36 xG); their most dangerous period to face is 46-60' (net -0.37 xG against them).
xG Race — the season, match by match
Expected goals piling up over 10 matches: Real Madrid 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 (+9.9 xG). Hover a point for that match.
Real MadridOpponents against Real Madrid
Tactical DNA
The football question
In five genes, what is Real Madrid'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.
82
Build-Up
possession control from the back
78
Progression
moving the ball upfield with threat
85
Creation
manufacturing chances
55
Press
winning the ball high & hard
80
Solidity
resisting the opponent's threat
Genetic identity
Real Madrid are defined most by their Creation DNA (85/100 · elite at manufacturing chances), and least by their Press DNA (55/100). 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
87%
Possession
91%
Field tilt
91%
Disrupt
Central progression ~proxy
91%
Dribble tendency
57%
Progressive passes
96%
Finish
Final-third patience
70%
Shot quality
83%
Open-play xG
91%
Press
Forward press ~proxy
39%
High regains
83%
Pressing efficiency ~proxy
70%
Defend
Def-mid press ~proxy
48%
High line ~proxy
83%
PAdj defensive actions
17%
Real Madrid is defined most by Possess (90% in the league) and weakest on Defend (49%). 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 Real Madrid actually control, relative to the rest of the league?
Field tilt = share of final-third touches between the two sides across Real Madrid'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; Real Madrid highlighted. Hover any dot for the team.
Vision AI Model Insight
Real Madrid post 52% field tilt, ranking #5 of 20 in the league — comfortably above the league mid-point for territorial control, and their 2.40 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 Real Madrid has the ball, how much of that time is spent building, progressing and creating?
The share of Real Madrid'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
24.4%
Progression
49.1%
Chance creation
24.8%
Attacking transition
1.7%
Vision AI Model Insight
Progression is where Real Madrid spends most of its time on the ball (49% 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
Cucurella ↔ Huijsen
139p · -1.4xT
Konaté ↔ Huijsen
137p · -0.1xT
Courtois ↔ Konaté
103p · 0.1xT
Valverde ↔ Güler
72p · 1.4xT
Courtois ↔ Huijsen
71p · -0.3xT
Valverde ↔ Konaté
67p · 0.1xT
Valverde ↔ Huijsen
64p · 0.3xT
Bellingham ↔ Huijsen
63p · 0.9xT
Mbappé ↔ Cucurella
61p · 0.9xT
Dumfries ↔ Konaté
60p · -0.5xT
Vision AI Model Insight
Busiest connection: Marc Cucurella ↔ Dean Huijsen (139 passes). Most dangerous per pass: Kylian Mbappé ↔ Arda Güler (0.05 xT/pass over 57 passes) — the combination that hurts opponents most when it fires, not just the one used most.
Touch Density — where Real Madrid had the ball
Every touch across 98 matches. Brighter means more touches.
Which Zones Do They Attack Through?
The question
Across the whole pitch, where does Real Madrid 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
5482
ACTIONS
Carries
the ball moved with a player
1334
ACTIONS
Vision AI Model Insight
Their single busiest zone is the left wing of the middle third (11.4% 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: 29% defensive, 49% middle, 22% 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. Real Madrid leaned on their left (47%). The bars under the pitch put the league’s share beside theirs.
Real Madrid
47%22%31%
League
38%23%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. Real Madrid made 527 this season (52.7 a match); 36% 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
190
19.0
36%
29%
Left half-space
100
10.0
19%
15%
Centre
56
5.6
11%
12%
Right half-space
50
5.0
9%
15%
Right wing
131
13.1
25%
29%
Build-up2 sections
Build-Up — the pass map
The football question
How does Real Madrid 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.
1600build-up passes attempted
89%completed
28%reach the middle third
1%go long into the final third
Pass types
605CB · 42%
66Long Wide · 5%
7Long Central · 0%
586Wide → FB · 41%
162Central · 11%
Sequence success — build-up
71build-up sequences this season
20%succeed — reach the next third or a shot
34%turned over with nothing to show
CB605 · 42%
Long Wide66 · 5%
Long Central7 · 0%
Wide → FB586 · 41%
Central162 · 11%
◆ Vision AI Insight
Real Madrid build predominantly down the right, with Courtois as the fulcrum (252 build-up passes). Only 29% 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 (102×).
Pass Explorer
Every pass of Real Madrid’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. 497 progressive passes across 10 matches (49.7/game).
Origin lane
Right wing
24%
Right half-space
13%
Central
11%
Left half-space
16%
Left wing
35%
Progression is left-dominant: 37% right side · 11% central · 52% left.
Who generates it
Arda Güler
70 · 14%
Dean Huijsen
50 · 10%
Marc Cucurella
40 · 8%
Federico Valverde
39 · 8%
Trent Alexander-Arnold
36 · 7%
Top two — Arda Güler & Dean Huijsen — account for 24% of all progression.
PROGRESSION
49.7 prog passes/game
mostly left
→
PENETRATION
29% reach final third
20% reach the box
→
THREAT
the other 71% is recycled
progress ≠ danger
What came of it.29% of progressive passes reached the final third and 20% the box; the rest was recycled in front of the block. 24% 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 wingbalanced
Progression — the pass map
The football question
Once Real Madrid 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.
2678progression passes attempted
92%completed
18%break into the final third
11%go backward into the defensive third
Pass types
684CB · 28%
115Long Wide · 5%
20Long Central · 1%
1339Wide → FB · 54%
312Central · 13%
Sequence success — progression
125progression sequences this season
31%succeed — reach the next third or a shot
25%turned over with nothing to show
CB684 · 28%
Long Wide115 · 5%
Long Central20 · 1%
Wide → FB1339 · 54%
Central312 · 13%
◆ Vision AI Insight
Real Madrid progress predominantly down the left, with Huijsen the busiest midfield outlet (279 progression passes). Only 18% of passes that start in midfield actually break the final-third line — the rest (71%) is recycled back into midfield or (11%) played backward, the hallmark of a side that carries progression itself through midfield rather than just passing through it. Their most-repeated pattern is LHS mid → LW 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)
15%
Progressive
2%
Neutral / sideways
46%
Safe / backward
38%
16% of passes are progressive-or-better; the remaining 84% keep possession without advancing it. A control-first side — safe in build-up, reliant on a few players for the incision.
Where does Real Madrid actually progress the ball with their feet, not a pass?
308 progressive carries this season across the whole squad (23% of 1329 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 47m. Most active carrier: Huijsen (41). Filter to one player below.
First (105)Second (74)Third (68)Fourth (61)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 Real Madrid'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.
168shots
20.5xG
0.12xG per shot
16big chances (0.3+ xG)
23goals
#3key 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
48
6
6.49
0.14
Cross
4
1
0.44
0.11
Cut-back
5
2
1.32
0.26
Long ball
3
0
0.36
0.12
Pass into the box / short pass
30
3
3.85
0.13
Own creation (carry, dribble, rebound)
6
0
0.52
0.09
Settled attack51% of shots
85
14
8.89
0.10
Cross
7
4
1.09
0.16
Cut-back
9
1
1.08
0.12
Long ball
8
1
0.65
0.08
Pass into the box / short pass
44
5
4.38
0.10
Own creation (carry, dribble, rebound)
17
3
1.69
0.10
Set piece21% of shots
35
3
5.15
0.15
Corner
29
1
2.70
0.09
Free kick
3
0
0.07
0.02
Penalty
3
2
2.38
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
Arda Güler
23
2.87
2
Kylian Mbappé
16
2.27
2
Jude Bellingham
13
1.91
3
Vinícius Júnior
12
1.84
2
Yan Diomande
10
1.12
2
Who takes them
Player
Shots
xG
Goals
Kylian Mbappé
52
9.74
11
Vinícius Júnior
23
2.57
1
Arda Güler
18
1.92
2
Jude Bellingham
19
1.43
3
Carlos Espí
4
1.01
2
Vision AI Model Insight
The biggest source of Real Madrid's chances, by xG, is settled attack (8.9 of 20.5 xG, 85 of 168 shots), mostly pass into the box / short pass. 48 of the 168 shots (29%) came within 15 seconds of winning the ball. 35 of the 168 shots came from set pieces; 23 goals from 20.5 xG (+2.5).
Chance Creation — the pass map
The football question
How does Real Madrid 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
11CB · 8%
8Long Wide · 6%
10Long Central · 8%
59Wide → FB · 44%
45Central · 34%
Sequence success — final-third
130final-third sequences this season
30%succeed — produce a shot
26%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
CB11 · 8%
Long Wide8 · 6%
Long Central10 · 8%
Wide → FB59 · 44%
Central45 · 34%
Who creates — volume × threat
33Güler
15Mbappé
15Bellingham
13Júnior
11Diomande
8Valverde
8Alexander-Arnold
133key passes #3 of 20
18.0box entries/game #4 of 20
44.9final-3rd entries/game #4 of 20
2.05xG/game #2 of 20
23goals #2 of 20
+2.5finishing vs xG #4 of 20
◆ Vision AI Insight
Real Madrid lean left — 44% of their key passes come from that flank and its half-space (59 of 133), most often from Güler (14). They funnel chances into the c of the final third, most often via LHS att → C att (15 key passes, most often from Júnior). Güler is the primary source overall. They generate 2.05 xG a game and finish it clinical — they beat their xG, led by Kylian Mbappé (11G), Jude Bellingham (3G), Arda Güler (2G).
Crosses & Cutbacks — squad-wide
The question
How does Real Madrid deliver into the box from wide areas, and what comes of it?
94 crosses: 34 found a team-mate (36%), 13 led to a shot (14%). 52 cut-backs: 23 found a team-mate (44%), 12 led to a shot (23%). 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 Real Madrid actually cross from?
94 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. 30% come from one territory, the byline left wing; 3 come from the half-spaces and 0 from the centre.
Territory
Crosses
Share
Led to a shot
byline · left wing
28
30%
2
advanced · right wing
17
18%
3
advanced · left wing
17
18%
2
byline · right wing
14
15%
2
deep · left wing
8
9%
2
deep · right wing
7
7%
0
advanced · left half-space
2
2%
1
advanced · right half-space
1
1%
1
Finishing3 sections
Shot Map
168 shots for 20.5 xG (2.05/game, #2 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.
High-volume attack. 16.8 shots/game at 0.122 xG/shot. Box entries 18/game (#4). Threat created 13.0 xT/game (#4).
Goal Sequences
The move behind each of Real Madrid’s 25 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 — 25 goals traced.
How Do They Score?
The question
Where do the goals actually come from — open play, dead balls, which foot?
Every goal Real Madrid scored across the loaded matches, broken down by how it was created and finished.
By situation
Open play
20 · 87%
Set pieces
1 · 4%
Penalties
2 · 9%
By finish
Right foot
15 · 65%
Left foot
6 · 26%
Header
2 · 9%
Vision AI Model Insight
23 goals scored. 87% come in open play; set pieces add 4%. 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
281
Failed forward pass
170
Failed take-on
123
Lost in the defensive third
174
Their weakest zone is the right half-space in the attacking third: 27% of 189 pass attempts there are lost (51 times). Separately, 42% 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 Real Madrid 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.
44
Pressing Index /100 · average
Intensity47
PPDA, high-press share, high-zone actions
Height60
how high up the pitch they defend
Effectiveness30
high recoveries & high-regain share
Disruption45
opponent completion & progression allowed
Every defensive action & where it happens
Where they win the ball back — by lane
RW
14%
RHS
18%
C
24%
LHS
21%
LW
23%
By third (high / mid / low regain)
Def 49%
Mid 35%
Att 16%
Read
Ranked #13 of 20 for press intensity, this is a high press. 51% of defensive actions happen in the middle & attacking thirds, at an average height of 39. Of recoveries, 50% are won high up the pitch.
Every pressing metric, ranked in the league
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#13/20
lower = more intense; rank is what matters
Defensive actions / game
133
#20/20
High-press share
24%
#6/20
def actions won high up
Defensive-action height (0–100)
38.8
#8/20
avg pitch height of actions
High recoveries / game
20.0
#12/20
ball won in mid+att third
High-recovery share
50%
#16/20
share of recoveries won high
Opponent completion allowed
86%
#20/20
lower = more disruptive
Opponent progressive allowed
8.0%
#2/20
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 Real Madrid win it back with a defensive action?
Counted over the 308 opposition possessions that Real Madrid 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 Real Madrid step in. Real Madrid win it after 6.6 opponent passes in the first half-hour and after 6.8 after the hour mark (about the same).
PASSES THEY GET
6.8passes
median 4 · before Real Madrid win it
TIME THEY KEEP IT
21sec
median 11s · 30.8 wins per game
How many passes the opponent got in before the ball was won
34%
28%
14%
25%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
6.4
18s
15–30′
6.9
19s
30–45+′
6.9
22s
45–60′
7.1
24s
60–75′
6.2
18s
75–90+′
7.2
22s
average passesaverage seconds
Attitude in each third
Every other style number here says how much Real Madrid 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 Real Madrid 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) 1,600 passes
safer, kept on the floor
Width of use-0.2120.56
Wide-channel share-0.290.29
Side bias+0.612.44
Pass length-0.4820.99
Long-ball share-0.910.14
Forward gain per pass-0.708.43
Backward-pass share+0.180.21
Deep-pass share-0.650.22
Completion+1.120.89
Seconds between passes-0.213.20
Side used
left 30% · central 46% · right 24%
Progression (middle third) 2,678 passes
safer, left-sided
Width of use-0.0125.54
Wide-channel share-0.020.40
Side bias+1.114.44
Pass length+0.7318.24
Long-ball share-0.160.09
Forward gain per pass-0.293.17
Backward-pass share+0.270.35
Deep-pass share+0.070.16
Completion+1.170.92
Seconds between passes-0.223.10
Side used
left 40% · central 31% · right 29%
Final third 1,204 passes
circulating, played inside, left-sided
Width of use-0.6130.11
Wide-channel share-0.920.51
Side bias+0.926.04
Pass length+0.0015.47
Long-ball share-0.440.08
Forward gain per pass-0.95-0.23
Backward-pass share+0.900.45
Deep-pass share-0.200.08
Completion+0.650.75
Seconds between passes+0.673.90
Side used
left 50% · central 18% · right 33%
Reference measured across roughly 3,500 match-teams
in this competition, per third, so the comparison is with Real Madrid'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 Real Madrid 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 share18%
#11 of 20 · reaches final third in ≤3 passes
Patient build-up share39%
#2 of 20 · spells of 6+ passes
Tempo (possession length)24.9s
#19 of 20 · shorter = faster tempo
Passes per possession6.5
#2 of 20
Counter-attack shots /game1.25
#5 of 20 · shot within 12s of a regain in own half
Press→shot /game3.62
#4 of 20 · shot within 8s of a high regain
Speed to final third after regain14.6s
#18 of 20 · lower = faster break
Sequence success — attacking transition
10attacking transition sequences this season
30%succeed — produce a shot
30%turned over with nothing to show
Sequence success — defensive transition
27defensive transition sequences this season
74%succeed — reach the next third or a shot
7%turned over with nothing to show
Vision AI Model Insight
Real Madrid are a patient build-up side: 18% of possessions reach the final third in 3 passes or fewer (#11 of 20), against 39% built patiently over 6+ passes (#2 of 20). Winning the ball back leads to a shot inside 12 seconds 1.25 times a game (#5), and a high regain converts to a shot within 8 seconds 3.62 times a game (#4 of 20) — a genuine transition weapon.
Defending the box2 sections
Vulnerability Map — how opponents hurt them
The report flipped: where do opponents create their threat against Real Madrid? 410 opponent progressive passes and 133 shots faced (10.6 xG against) across the season.
Opponent threat by lane (attacking into Real Madrid)
Right wing
32%
Right half-space
14%
Central
12%
Left half-space
13%
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 Real Madrid actually come from — open play, dead balls, and who supplies them?
Every goal Real Madrid conceded across the loaded matches — the opponent's shots in Real Madrid'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 (101 total)
By situation
Open play
7 · 70%
Set pieces
2 · 20%
Penalties
1 · 10%
Vision AI Model Insight
10 goals conceded. 70% come in open play; set pieces account for only 20%, so dead-ball defending is not the main issue. 10% 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
20 g · 15.4 xG
Set pieces
1 g · 2.8 xG
Penalties
2 g
4% of goals come from set pieces (1 of 23), worth 2.8 xG from 32 shots. Set pieces are a secondary threat; the danger is in open play.
Where do Real Madrid's corners go?
64 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 (20)
Delivered into the box 90%Short 10%
Left side (44)
Delivered into the box 95%Short 5%
Where opponents attack Real Madrid from set pieces
18 shots from opponent corners against Real Madrid, 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 (9)
Shot 9Goals 2
Left flag (9)
Shot 9Goals 0
Tactical Strengths & Weaknesses
Strengths (top-third vs league)
xG created /game — 2.05 (90th pct)
pass retention % — 87.91 (90th pct)
shots /game — 16.80 (85th pct)
xG conceded /game — 1.06 (80th pct)
box entries /game — 18.00 (80th pct)
Weaknesses (bottom-third vs league)
press intensity (PPDA) — 6.04 (35th 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.
Atletico
78%
Villarreal
78%
Barcelona
74%
Real Betis
56%
Athletic Club
49%
Performance vs Opponent Styles
The question
Not just how Real Madrid 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 Real Madrid's own passing behaviour to the result.
Results
opponent type
goals for/g
goals against/g
points/g
vs high-pressing opponents (3m)
2.33
1.33
2.00
vs mid-block opponents (3m)
3.00
1.33
3.00
vs low-block opponents (4m)
2.25
0.50
2.25
Behaviour — how Real Madrid actually play against each
opponent type
retention
directness (long balls)
prog. passes/g
xT/g
box entries/g
vs high-pressing opponents
85%
12%
32
9.5
12.3
vs mid-block opponents
89%
9%
61
14.7
18.0
vs low-block opponents
88%
11%
54
14.3
22.2
Vision AI Model Insight
Facing a high press, retention drops to 85% vs 88% against a low block (2pt lower under pressure), and directness (long-ball share) sits at 12% when pressed vs 11% 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. Real Madrid are balanced-territory with a aggressive high-press.
Their attack creates 2.05 xG a game and they concede 1.06 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 Real Madrid’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.
Real Madrid
across 10 matches this season
Defensive actionsat Barcelona · 2-1 · 2026-10-25166avg 133
Field tiltat Barcelona · 2-1 · 2026-10-2528%avg 52%
Final-third entriesat Real Betis · 0-1 · 2026-09-0478avg 53
PPDAat Espanyol · 2-1 · 2026-08-223.8avg 6.0
Threat from passingat Elche · 3-2 · 2026-09-159.94avg 6.53
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 Real Madrid’s last 10 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).
+19.7on-ball value, 10 matches
Where the value came from
Passes5482 actions+5.94
Carries1329 actions+0.87
Dribbles257 actions+1.77
Shots168 actions+8.46
Ball wins577 actions+2.68
Most on-ball value (OBV)
Actions
OBV
per 100
Kylian Mbappé
566
+5.98
+1.06
Arda Güler
552
+3.16
+0.57
Jude Bellingham
584
+2.02
+0.35
Dean Huijsen
703
+1.29
+0.18
Federico Valverde
643
+1.20
+0.19
Yan Diomande
458
+1.10
+0.24
Vinícius Júnior
372
+1.03
+0.28
Carlos Espí
18
+0.91
+5.06
Biggest single moves
OBV
1 Kylian Mbappé · shot
+0.792
2 Kylian Mbappé · shot
+0.728
3 Vinícius Júnior · pass
+0.647
4 Kylian Mbappé · shot
+0.608
5 Arda Güler · pass
+0.567
6 Arda Güler · pass
+0.481
Set Pieces by Player
Who delivers Real Madrid’s dead balls and who finishes them this season. 64 corners taken, most by Arda Güler (32); 32 shots from corners and free kicks, 1 goals, led by Kylian Mbappé (1 goals, 1.43 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
Arda Güler
32
25 (78%)
5
—
0.12
1
—
Kylian Mbappé
2
1 (50%)
7
1
1.43
1
2/3
Trent Alexander-Arnold
14
12 (86%)
1
—
0.02
1
—
Dean Huijsen
—
—
4
—
0.37
—
—
Jude Bellingham
1
0 (0%)
3
—
0.28
—
—
Vinícius Júnior
6
1 (17%)
1
—
0.03
—
—
Federico Valverde
—
—
3
—
0.08
—
—
Ibrahima Konaté
—
—
3
—
0.16
—
—
Antonio Rüdiger
—
—
2
—
0.11
—
—
Bernardo Silva
5
3 (60%)
—
—
0.00
—
—
Yan Diomande
4
4 (100%)
—
—
0.00
—
—
Carlos Espí
—
—
1
—
0.07
—
—
Aurélien Tchouaméni
—
—
1
—
0.05
—
—
Endrick
—
—
1
—
0.04
—
—
Top Performers
Ranked by Vision AI Score — the same tactical behavioural evaluation each player's own report page shows (min 270 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
Arda Güler
M
600
4.95
40
17.7
Kylian Mbappé
F
941
1.43
27
7.6
Vinícius Júnior
M
623
1.88
21
4.7
Jude Bellingham
M
807
1.67
18
8.9
Yan Diomande
M
580
1.71
18
6.8
Federico Valverde
M
822
0.88
13
11.6
Trent Alexander-Arnold
D
284
2.54
12
7.7
Bernardo Silva
M
402
1.12
7
4.8
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)
Vinícius Júnior
W
623
12 (17%)
15 (40%)
Marc Cucurella
FB
699
2 (0%)
6 (17%)
Denzel Dumfries
FB
478
3 (0%)
2 (50%)
Álvaro Carreras
FB
259
1 (100%)
2 (100%)
Defensive Volume Leaders
Who does the defensive work — tackles, interceptions, recoveries.
player
pos
min
def actions
tackles
intercept
recoveries
Jude Bellingham
M
807
146
20
4
45
Marc Cucurella
D
699
130
23
7
41
Dean Huijsen
D
758
126
10
13
24
Ibrahima Konaté
D
858
124
13
4
30
Federico Valverde
M
822
100
13
12
34
Arda Güler
M
600
83
9
1
35
Aurélien Tchouaméni
M
470
80
12
9
28
Yan Diomande
M
580
75
13
5
24
Full Squad Data
Every player with 100+ minutes (18 players).
player
pos
apps
min
goals
xG
key P
prog
def
xT
Kylian Mbappé
F
10
941
11
9.7
15
27
48
7.6
Ibrahima Konaté
D
9
858
0
0.5
0
30
124
10.0
Thibaut Courtois
G
9
854
0
0.0
0
17
22
6.3
Federico Valverde
M
10
822
0
0.3
8
39
100
11.6
Jude Bellingham
M
10
807
3
1.4
15
28
146
8.9
Dean Huijsen
D
8
758
0
0.5
2
50
126
13.0
Marc Cucurella
D
10
699
0
0.0
3
40
130
8.8
Vinícius Júnior
M
8
623
1
2.6
13
30
66
4.7
Arda Güler
M
10
600
2
1.9
33
70
83
17.7
Yan Diomande
M
10
580
2
0.7
11
35
75
6.8
Denzel Dumfries
D
8
478
0
0.4
4
12
47
2.3
Aurélien Tchouaméni
M
6
470
0
0.2
5
14
80
4.7
Bernardo Silva
M
8
402
0
0.2
5
12
67
4.8
Antonio Rüdiger
D
4
328
1
0.2
1
8
48
3.8
Eduardo Camavinga
M
9
318
0
0.0
1
17
60
4.4
Trent Alexander-Arnold
D
6
284
0
0.1
8
36
33
7.7
Álvaro Carreras
D
5
259
1
0.4
6
21
34
4.1
Brahim Díaz
M
4
133
0
0.1
2
4
14
0.9
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.
Kylian Mbappé
Poacher / finisher · Forward
minutes941
goals · xG11 · 9.7
prog passes /902.6
key passes /901.43
def actions /904.6
pass accuracy83%
Ibrahima Konaté
Stopper / marker · Defender
minutes858
goals · xG0 · 0.5
prog passes /903.1
key passes /900.00
def actions /9013.0
pass accuracy93%
Thibaut Courtois
Goalkeeper · Goalkeeper
minutes854
goals · xG0 · 0.0
prog passes /901.8
key passes /900.00
def actions /902.3
pass accuracy85%
Federico Valverde
Ball-winner / destroyer · Midfielder
minutes822
goals · xG0 · 0.3
prog passes /904.3
key passes /900.88
def actions /9010.9
pass accuracy90%
Jude Bellingham
Ball-winner / destroyer · Midfielder
minutes807
goals · xG3 · 1.4
prog passes /903.1
key passes /901.67
def actions /9016.3
pass accuracy87%
Dean Huijsen
Ball-playing defender · Defender
minutes758
goals · xG0 · 0.5
prog passes /905.9
key passes /900.24
def actions /9015.0
pass accuracy95%
Marc Cucurella
Ball-playing defender · Defender
minutes699
goals · xG0 · 0.0
prog passes /905.2
key passes /900.39
def actions /9016.7
pass accuracy88%
Vinícius Júnior
Ball-winner / destroyer · Midfielder
minutes623
goals · xG1 · 2.6
prog passes /904.3
key passes /901.88
def actions /909.5
pass accuracy72%
Arda Güler
Creative forward · Forward
minutes600
goals · xG2 · 1.9
prog passes /9010.5
key passes /904.95
def actions /9012.4
pass accuracy84%
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: press intensity (PPDA) (35th pct)
Sign a high-energy presser to lift defensive intensity. The data flags this because the press is passive (6.04, 35th percentile vs the league).