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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.

balanced-territoryaggressive high-presshigh-volume attack
W
L
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W
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L
W
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L
W
W
W
24
points
#3 of 20 · 10 games
25:10
goals
+15 GD
8-0-2
record
W-D-L
2.40
points/game
#3 in the league
2.05
xG for/game
rank #2
1.06
xG against/game
rank #4
#13
press rank
of 20 · most aggressive
52%
field tilt
#5
Real Madrid
League average
Expected goals for / game
2.051.35
Expected goals against / game
1.061.35
Field tilt
52%50%
Pass retention
88%82%
Overview

The team

Who they are and how the season went.

Who Are They? — Identity

The question
In one read, what kind of team is 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.

#teamPWDLGFGAGDPts
1Barcelona109014011+2927
2Atletico10811208+1225
3Real Madrid108022510+1524
4Real Betis106221614+220
5Real Sociedad106131918+119
6Sevilla94231416-214

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)
even0-15'16-30'31-45'46-60'61-75'76-90+'+4.36-0.37
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.

07152220.510.6Espanyol (A) 2-1 · xG 2.54 to 0.76Real Sociedad (H) 4-1 · xG 2.18 to 0.65Malaga (H) 4-0 · xG 2.55 to 0.79Real Betis (A) 0-1 · xG 2.98 to 0.81Rayo Vallecano (H) 4-1 · xG 2.87 to 1.18Elche (A) 3-2 · xG 2.91 to 1.04Atletico (A) 1-2 · xG 0.37 to 2.15Villarreal (H) 1-0 · xG 0.86 to 0.78Sevilla (H) 4-1 · xG 2.15 to 0.98Barcelona (A) 2-1 · xG 1.13 to 1.48M1M2M3M4M5M6M7M8M9M10
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).

Possess90Disrupt81Finish81Press64Defend49
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.

Keeping the ball6 sections

Field Tilt — Territorial Control

The question
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.

Barcelona: 73% tilt, 2.70 pts/gAtletico: 61% tilt, 2.50 pts/gReal Madrid: 52% tilt, 2.40 pts/gReal Betis: 51% tilt, 2.00 pts/gReal Sociedad: 47% tilt, 1.90 pts/gSevilla: 50% tilt, 1.56 pts/gAthletic Club: 60% tilt, 1.33 pts/gOsasuna: 45% tilt, 1.22 pts/gAlaves: 49% tilt, 1.20 pts/gVillarreal: 64% tilt, 1.20 pts/gCelta Vigo: 50% tilt, 1.20 pts/gDeportivo: 44% tilt, 1.10 pts/gRacing Santander: 47% tilt, 1.10 pts/gElche: 46% tilt, 1.00 pts/gRayo Vallecano: 47% tilt, 1.00 pts/gLevante: 43% tilt, 1.00 pts/gGetafe: 43% tilt, 0.90 pts/gEspanyol: 45% tilt, 0.80 pts/gValencia: 39% tilt, 0.60 pts/gMalaga: 46% tilt, 0.50 pts/gReal Madrid39%50%61%73%0.51.22.02.7FIELD TILT %POINTS / GAME
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.

Build-up: 24.4% of the time on the ball24%Progression: 49.1% of the time on the ball49%Chance creation: 24.8% of the time on the ball25%Attacking transition: 1.7% of the time on the ball49%PROGRESSION
PhaseShare of the time
Build-up24.4%
Progression49.1%
Chance creation24.8%
Attacking transition1.7%
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.

Marc Cucurella ↔ Dean Huijsen: 139 passes, -1.4 combined xTIbrahima Konaté ↔ Dean Huijsen: 137 passes, -0.1 combined xTThibaut Courtois ↔ Ibrahima Konaté: 103 passes, 0.1 combined xTFederico Valverde ↔ Arda Güler: 72 passes, 1.4 combined xTThibaut Courtois ↔ Dean Huijsen: 71 passes, -0.3 combined xTFederico Valverde ↔ Ibrahima Konaté: 67 passes, 0.1 combined xTFederico Valverde ↔ Dean Huijsen: 64 passes, 0.3 combined xTJude Bellingham ↔ Dean Huijsen: 63 passes, 0.9 combined xTKylian Mbappé ↔ Marc Cucurella: 61 passes, 0.9 combined xTDenzel Dumfries ↔ Ibrahima Konaté: 60 passes, -0.5 combined xTMarc Cucurella ↔ Yan Diomande: 59 passes, 1.0 combined xTKylian Mbappé ↔ Federico Valverde: 59 passes, 1.2 combined xTVinícius Júnior ↔ Marc Cucurella: 58 passes, 1.2 combined xTMarc Cucurella ↔ Jude Bellingham: 58 passes, 0.6 combined xTKylian Mbappé ↔ Arda Güler: 57 passes, 3.0 combined xTDenzel Dumfries ↔ Arda Güler: 55 passes, 1.1 combined xTBernardo Silva ↔ Federico Valverde: 54 passes, 0.1 combined xTBernardo Silva ↔ Dean Huijsen: 50 passes, 0.1 combined xTFederico Valverde ↔ Marc Cucurella: 48 passes, -0.7 combined xTBernardo Silva ↔ Ibrahima Konaté: 48 passes, 0.0 combined xTVinícius Júnior ↔ Jude Bellingham: 47 passes, 1.8 combined xTAurélien Tchouaméni ↔ Marc Cucurella: 46 passes, 0.1 combined xTKylian Mbappé ↔ Vinícius Júnior: 46 passes, 1.3 combined xTIbrahima Konaté ↔ Aurélien Tchouaméni: 45 passes, 0.2 combined xTKylian Mbappé — isolate this player's connectionsMbappéFederico Valverde — isolate this player's connectionsValverdeJude Bellingham — isolate this player's connectionsBellinghamArda Güler — isolate this player's connectionsGülerYan Diomande — isolate this player's connectionsDiomandeIbrahima Konaté — isolate this player's connectionsKonatéMarc Cucurella — isolate this player's connectionsCucurellaEduardo Camavinga — isolate this player's connectionsCamavingaThibaut Courtois — isolate this player's connectionsCourtoisBernardo Silva — isolate this player's connectionsSilvaVinícius Júnior — isolate this player's connectionsJúniorDean Huijsen — isolate this player's connectionsHuijsenFran García — isolate this player's connectionsGarcíaAurélien Tchouaméni — isolate this player's connectionsTchouaméniDani Ceballos — isolate this player's connectionsCeballosDenzel Dumfries — isolate this player's connectionsDumfries
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
3%5%11%5%5%8%8%11%11%11%5%3%3%5%6%PASSES
Carries
the ball moved with a player
1334
ACTIONS
3%6%10%6%3%8%8%10%11%12%6%3%2%3%9%CARRIES
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.

47%Left side22%Middle31%Right sideATTACKING →
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.

Left wing: 190 entries (36%)19036%LEFT WINGLeft half-space: 100 entries (19%)10019%LEFT HALF-SPACECentre: 56 entries (11%)5611%CENTRERight half-space: 50 entries (9%)509%RIGHT HALF-SPACERight wing: 131 entries (25%)13125%RIGHT WINGATTACKING ↑
ChannelEntriesPer matchShareLeague share
Left wing19019.036%29%
Left half-space10010.019%15%
Centre565.611%12%
Right half-space505.09%15%
Right wing13113.125%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%
left: 308 passes, most from Huijsen308right: 297 passes, most from Konaté297
Long Wide66 · 5%
left: 39 passes, most from Courtois39right: 27 passes, most from Courtois27
Long Central7 · 0%
right: 4 passes, most from Valverde4left: 3 passes, most from Courtois3
Wide → FB586 · 41%
left: 335 passes, most from Cucurella335right: 251 passes, most from Courtois251
Central162 · 11%
left: 89 passes, most from Courtois89right: 73 passes, most from Courtois73
◆ 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.

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%
left: 350 passes, most from Huijsen350right: 334 passes, most from Konaté334
Long Wide115 · 5%
left: 58 passes, most from Güler58right: 57 passes, most from Valverde57
Long Central20 · 1%
right: 13 passes, most from Valverde13left: 7 passes, most from Güler7
Wide → FB1339 · 54%
left: 844 passes, most from Cucurella844right: 495 passes, most from Valverde495
Central312 · 13%
left: 176 passes, most from Valverde176right: 136 passes, most from Valverde136
◆ 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.

Carries Map — progressive ball-carries, squad-wide

The question
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
Attacking transition: 48 shots (29%)29%48Settled attack: 85 shots (51%)51%85Set piece: 35 shots (21%)21%35168SHOTS
Kind of attack, then how the shot was set upShotsGoalsxGxG / shot
Attacking transition 29% of shots4866.490.14
Cross410.440.11
Cut-back521.320.26
Long ball300.360.12
Pass into the box / short pass3033.850.13
Own creation (carry, dribble, rebound)600.520.09
Settled attack 51% of shots85148.890.10
Cross741.090.16
Cut-back911.080.12
Long ball810.650.08
Pass into the box / short pass4454.380.10
Own creation (carry, dribble, rebound)1731.690.10
Set piece 21% of shots3535.150.15
Corner2912.700.09
Free kick300.070.02
Penalty322.380.79

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

PlayerSet-upsxG createdGoals
Arda Güler232.872
Kylian Mbappé162.272
Jude Bellingham131.913
Vinícius Júnior121.842
Yan Diomande101.122

Who takes them

PlayerShotsxGGoals
Kylian Mbappé529.7411
Vinícius Júnior232.571
Arda Güler181.922
Jude Bellingham191.433
Carlos Espí41.012
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%
left: 7 passes, most from Alexander-Arnold7right: 4 passes, most from Alexander-Arnold4
Long Wide8 · 6%
left: 5 passes, most from Tchouaméni5right: 3 passes, most from Júnior3
Long Central10 · 8%
left: 7 passes, most from Güler7right: 3 passes, most from Güler3
Wide → FB59 · 44%
left: 38 passes, most from Bellingham38right: 21 passes, most from Güler21
Central45 · 34%
left: 28 passes, most from Güler28right: 17 passes, most from Güler17

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.

deep right wing: 7 crosses (7%)7%7deep right half-space: 0 crosses (0%)deep centre: 0 crosses (0%)deep left half-space: 0 crosses (0%)deep left wing: 8 crosses (9%)9%8advanced right wing: 17 crosses (18%)18%17advanced right half-space: 1 crosses (1%)1%1advanced centre: 0 crosses (0%)advanced left half-space: 2 crosses (2%)2%2advanced left wing: 17 crosses (18%)18%17byline right wing: 14 crosses (15%)15%14byline right half-space: 0 crosses (0%)byline centre: 0 crosses (0%)byline left half-space: 0 crosses (0%)byline left wing: 28 crosses (30%)30%28
TerritoryCrossesShareLed to a shot
byline · left wing2830%2
advanced · right wing1718%3
advanced · left wing1718%2
byline · right wing1415%2
deep · left wing89%2
deep · right wing77%0
advanced · left half-space22%1
advanced · right half-space11%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

26 of 183 pass attempts lost here (14%).14%26/183Dumfries27 of 268 pass attempts lost here (10%).10%27/268Konaté57 of 597 pass attempts lost here (10%).10%57/597Courtois33 of 295 pass attempts lost here (11%).11%33/295Huijsen31 of 257 pass attempts lost here (12%).12%31/257Cucurella46 of 421 pass attempts lost here (11%).11%46/421Dumfries39 of 452 pass attempts lost here (9%).9%39/452Konaté39 of 594 pass attempts lost here (7%).7%39/594Valverde36 of 594 pass attempts lost here (6%).6%36/594Bellingham48 of 617 pass attempts lost here (8%).8%48/617Cucurella43 of 247 pass attempts lost here (17%).17%43/247Alexander-51 of 189 pass attempts lost here (27%).27%51/189Mbappé31 of 159 pass attempts lost here (19%).19%31/159Bellingham72 of 270 pass attempts lost here (27%).27%72/270Júnior84 of 339 pass attempts lost here (25%).25%84/339Júniorattacking →

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.

Pressing2 sections

Pressing — the full picture

The football question
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

metricvaluerankmeaning
PPDA (opp passes per def action)—#13/20lower = more intense; rank is what matters
Defensive actions / game133#20/20
High-press share24%#6/20def actions won high up
Defensive-action height (0–100)38.8#8/20avg pitch height of actions
High recoveries / game20.0#12/20ball won in mid+att third
High-recovery share50%#16/20share of recoveries won high
Opponent completion allowed86%#20/20lower = more disruptive
Opponent progressive allowed8.0%#2/20of 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

← opponent shot at own goal

Every opponent key pass against them (101 total)

7%6%4%11%21%12%24%13%Mikel OyarzabalSergio GómezLuka SucicGonçalo GuedesSergio GómezBeñat TurrientesLuken BeitiaTakefusa KuboCalaJavi HernándezRoberto FernándezChupeDavid LarrubiaJoaquín MuñozDavid LarrubiaDavid LarrubiaChupeIzan MerinoHaitam AbaidaIzan MerinoDavid LarrubiaCarlos DotorIscoAntonyPablo FornalsHéctor BellerínGnangoroPedro DíazGnangoroAdrià PedrosaSergio CamelloUnai LópezSergio CamelloAdrià PedrosaSergio CamelloPedro DíazAdrià PedrosaGiorgi TsitaishviliAdrià PedrosaÓscar ValentínPape GueyeAlberto MoleiroPablo FornalsPablo FornalsLee Kang-InJonathan DavidMarcos LlorenteMarcos LlorenteKokeGiuliano SimeoneDávid HanckoKokeAdemola LookmanAdemola LookmanKokeMarcos LlorenteMorten HjulmandAnthony GordonJoão CanceloRaphinhaPedriFermín LópezPedriAnthony GordonJoão CanceloPedriRodriAlejandro BaldeAnthony GordonAnthony GordonJoão CanceloJoão CanceloRaphinhaAlex FreemanAlberto MoleiroIlias AkhomachAlex FreemanAyoze PérezGeorges MikautadzeCarlos RomeroCarlos RomeroAlberto MoleiroAyoze PérezMartim NetoBuba SangaréGermán ValeraRoy RevivoGermán ValeraTete MorenteGonzalo VillarThomas LemarLucas CepedaJosanYoussouf FofanaGabriel SuazoRubén VargasPeque FernándezJuan IglesiasChidera EjukeLucas StassinGabriel Suazo← own goal

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.

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

Set-Piece Threat

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

Open play
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)

15%15%35%10%10%5%
Delivered into the box 90%Short 10%

Left side (44)

5%36%18%16%2%7%5%7%
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)

11%11%33%22%11%11%
Shot 9Goals 2

Left flag (9)

33%11%33%22%
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 typegoals for/ggoals against/gpoints/g
vs high-pressing opponents (3m)2.331.332.00
vs mid-block opponents (3m)3.001.333.00
vs low-block opponents (4m)2.250.502.25

Behaviour — how Real Madrid actually play against each

opponent typeretentiondirectness (long balls)prog. passes/gxT/gbox entries/g
vs high-pressing opponents85%12%329.512.3
vs mid-block opponents89%9%6114.718.0
vs low-block opponents88%11%5414.322.2
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.]
Squad

Players

Who drives the numbers.

Against Their Own Norm

For each of ten match numbers, the one match this season that departed furthest from 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%
Pass accuracyvs Villarreal · 1-0 · 2026-10-1081%avg 87%
Expected goalsat Atletico · 1-2 · 2026-09-200.37avg 2.05
Progressive passesat Atletico · 1-2 · 2026-09-2021avg 50
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)ActionsOBVper 100
Kylian Mbappé566+5.98+1.06
Arda Güler552+3.16+0.57
Jude Bellingham584+2.02+0.35
Dean Huijsen703+1.29+0.18
Federico Valverde643+1.20+0.19
Yan Diomande458+1.10+0.24
Vinícius Júnior372+1.03+0.28
Carlos Espí18+0.91+5.06
123456
Biggest single movesOBV
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.

PlayerCorners takenInto the boxSet-piece shotsGoalsxGFree-kick shotsPenalties
Arda Güler3225 (78%)5—0.121—
Kylian Mbappé21 (50%)711.4312/3
Trent Alexander-Arnold1412 (86%)1—0.021—
Dean Huijsen——4—0.37——
Jude Bellingham10 (0%)3—0.28——
Vinícius Júnior61 (17%)1—0.03——
Federico Valverde——3—0.08——
Ibrahima Konaté——3—0.16——
Antonio Rüdiger——2—0.11——
Bernardo Silva53 (60%)——0.00——
Yan Diomande44 (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.

playerposminVision AI Scorecapability blocks
Kylian MbappéST9418.2Box Threat 94%Finishing 91%Ball-Carrying 89%Aerial Threat 0%
Yan DiomandeAM5808.2Ball-Carrying 94%Box Threat 56%Chance Creation 50%
Federico ValverdeCM8227.8Progression 86%Ball-Carrying 69%Successful Dribble 60%Chance Creation 47%
Arda GülerAM6007.6Chance Creation 68%Ball-Carrying 65%Box Threat 60%
Vinícius JúniorW6237.5Box Threat 88%Ball-Carrying 71%Chance Creation 44%Wide Delivery 40%
Jude BellinghamAM8077.3Box Threat 94%Ball-Carrying 81%Chance Creation 44%
Dean HuijsenCB7587.2Ball-Carrying 93%Progression 88%Ball-Winning 41%Aerial & Box Defending 26%
Ibrahima KonatéCB8587.0Ball-Carrying 81%Progression 63%Ball-Winning 47%Aerial & Box Defending 24%
Thibaut CourtoisGK8546.4Shot-Stopping 64%
Marc CucurellaFB6995.0Ball-Winning 52%Ball-Carrying 36%Wide Delivery 26%

Top Creators

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

playerposminkey P /90box entriesxT
Arda GülerM6004.954017.7
Kylian MbappéF9411.43277.6
Vinícius JúniorM6231.88214.7
Jude BellinghamM8071.67188.9
Yan DiomandeM5801.71186.8
Federico ValverdeM8220.881311.6
Trent Alexander-ArnoldD2842.54127.7
Bernardo SilvaM4021.1274.8

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.

playerposmincrosses (completed)cutbacks (completed)
Vinícius JúniorW62312 (17%)15 (40%)
Marc CucurellaFB6992 (0%)6 (17%)
Denzel DumfriesFB4783 (0%)2 (50%)
Álvaro CarrerasFB2591 (100%)2 (100%)

Defensive Volume Leaders

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

playerposmindef actionstacklesinterceptrecoveries
Jude BellinghamM80714620445
Marc CucurellaD69913023741
Dean HuijsenD758126101324
Ibrahima KonatéD85812413430
Federico ValverdeM822100131234
Arda GülerM600839135
Aurélien TchouaméniM4708012928
Yan DiomandeM5807513524

Full Squad Data

Every player with 100+ minutes (18 players).

playerposappsmingoalsxGkey PprogdefxT
Kylian MbappéF10941119.71527487.6
Ibrahima KonatéD985800.503012410.0
Thibaut CourtoisG985400.0017226.3
Federico ValverdeM1082200.383910011.6
Jude BellinghamM1080731.415281468.9
Dean HuijsenD875800.525012613.0
Marc CucurellaD1069900.03401308.8
Vinícius JúniorM862312.61330664.7
Arda GülerM1060021.933708317.7
Yan DiomandeM1058020.71135756.8
Denzel DumfriesD847800.4412472.3
Aurélien TchouaméniM647000.2514804.7
Bernardo SilvaM840200.2512674.8
Antonio RüdigerD432810.218483.8
Eduardo CamavingaM931800.0117604.4
Trent Alexander-ArnoldD628400.1836337.7
Álvaro CarrerasD525910.4621344.1
Brahim DíazM413300.124140.9

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).
Vision AI · building report