Combining every season — this one is larger, give it a moment.
Team reportLa Liga 2025/26
Real Madrid
A possession-dominant side, aggressive high-press out of possession, with a high-volume attack. Finished #2 of 20 on 86 points (27-5-6, 77:35). Tactical profile below is built from 38 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 finished #2 of 20 on 86 points (27W-5D-6L, 2.26 pts/game), scoring 77 and conceding 35 for a goal difference of +42.
On the ball they are a possession-dominant team (63% field tilt, #2 in the league); off it they defend with a aggressive high-press (#10 of 20 for press intensity).
Across the 38 matches of event data, they create 1.88 xG/game (#2) and concede 1.07 (#3), a net of +0.81 xG/game — the underlying performance diverges from 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
38
31
1
6
95
36
+59
94
2
Real Madrid
38
27
5
6
77
35
+42
86
3
Villarreal
38
22
6
10
72
46
+26
72
4
Atletico
38
21
6
11
62
44
+18
69
5
Real Betis
38
15
15
8
59
48
+11
60
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 come alive after the break — first-half net xG +15.00, second-half +15.66. Their strongest period is 16-30' (net +8.32 xG); their most dangerous period to face is 0-15' (net +2.60 xG against them).
xG Race — the season, match by match
Expected goals piling up over 38 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 (+30.7 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.
90
Build-Up
possession control from the back
92
Progression
moving the ball upfield with threat
88
Creation
manufacturing chances
70
Press
winning the ball high & hard
85
Solidity
resisting the opponent's threat
Genetic identity
Real Madrid are defined most by their Progression DNA (92/100 · elite at moving the ball upfield with threat), and least by their Press DNA (70/100). 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 63% field tilt, ranking #2 of 20 in the league — a genuinely territory-dominant side, and their 2.26 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
21.1%
Progression
49.4%
Chance creation
27.4%
Attacking transition
2.1%
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
Júnior ↔ Carreras
463p · 31.9xT
Carreras ↔ Huijsen
379p · 19.0xT
Valverde ↔ Tchouaméni
301p · 9.2xT
Tchouaméni ↔ Carreras
297p · 11.9xT
Valverde ↔ Güler
291p · 11.9xT
Valverde ↔ Asencio
263p · 12.1xT
Tchouaméni ↔ Huijsen
261p · 8.7xT
Tchouaméni ↔ Güler
253p · 9.3xT
Courtois ↔ Asencio
250p · 10.4xT
Alexander-Arnold ↔ Valverde
240p · 13.1xT
Vision AI Model Insight
Busiest connection: Vinícius Júnior ↔ Álvaro Carreras (463 passes). Most dangerous per pass: Vinícius Júnior ↔ Álvaro Carreras (0.07 xT/pass over 463 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 380 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
23659
ACTIONS
Carries
the ball moved with a player
5910
ACTIONS
Vision AI Model Insight
Their single busiest zone is the centre of the middle third (12.1% 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: 23% defensive, 51% middle, 26% 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 (45%). The bars under the pitch put the league’s share beside theirs.
Real Madrid
45%24%31%
League
39%22%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. Real Madrid made 2409 this season (63.4 a match); 37% 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
894
23.5
37%
30%
Left half-space
446
11.7
19%
15%
Centre
281
7.4
12%
12%
Right half-space
261
6.9
11%
14%
Right wing
527
13.9
22%
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.
5554build-up passes attempted
87%completed
27%reach the middle third
1%go long into the final third
Pass types
1752CB · 36%
278Long Wide · 6%
39Long Central · 1%
1994Wide → FB · 41%
751Central · 16%
Sequence success — build-up
960build-up sequences this season
16%succeed — reach the next third or a shot
35%turned over with nothing to show
CB1752 · 36%
Long Wide278 · 6%
Long Central39 · 1%
Wide → FB1994 · 41%
Central751 · 16%
◆ Vision AI Insight
Real Madrid build predominantly down the right, with Courtois as the fulcrum (925 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 → RHS def (311×).
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. 2076 progressive passes across 38 matches (54.6/game).
Origin lane
Right wing
26%
Right half-space
10%
Central
12%
Left half-space
15%
Left wing
37%
Progression is left-dominant: 36% right side · 12% central · 53% left.
Who generates it
Vinícius Júnior
232 · 11%
Álvaro Carreras
195 · 9%
Federico Valverde
188 · 9%
Arda Güler
173 · 8%
Trent Alexander-Arnold
146 · 7%
Top two — Vinícius Júnior & Álvaro Carreras — account for 21% of all progression.
PROGRESSION
54.6 prog passes/game
mostly left
→
PENETRATION
27% reach final third
17% reach the box
→
THREAT
the other 73% is recycled
progress ≠ danger
What came of it.27% of progressive passes reached the final third and 17% the box; the rest was recycled in front of the block. 21% 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 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.
11912progression passes attempted
92%completed
18%break into the final third
8%go backward into the defensive third
Pass types
2865CB · 26%
396Long Wide · 4%
71Long Central · 1%
5984Wide → FB · 54%
1684Central · 15%
Sequence success — progression
1576progression sequences this season
32%succeed — reach the next third or a shot
33%turned over with nothing to show
CB2865 · 26%
Long Wide396 · 4%
Long Central71 · 1%
Wide → FB5984 · 54%
Central1684 · 15%
◆ Vision AI Insight
Real Madrid progress predominantly down the left, with Tchouaméni the busiest midfield outlet (1181 progression passes). Only 18% of passes that start in midfield actually break the final-third line — the rest (74%) is recycled back into midfield or (8%) 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 (619×) — press that lane and midfield progression stalls.
Decision Quality — not all completed passes are equal
A 90% pass completion can hide a sideways habit. Grading every pass by the threat it adds separates value creation from safe circulation.
Excellent (high xT gain)
13%
Progressive
2%
Neutral / sideways
47%
Safe / backward
38%
15% of passes are progressive-or-better; the remaining 85% keep possession without advancing it. A control-first side — safe in build-up, reliant on a few players for the incision.
Where does Real Madrid actually progress the ball with their feet, not a pass?
1259 progressive carries this season across the whole squad (21% of 5910 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 63m. Most active carrier: Júnior (164). Filter to one player below.
First (481)Second (297)Third (279)Fourth (202)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.
665shots
71.3xG
0.11xG per shot
44big chances (0.3+ xG)
77goals
#2key passes / game in the league
#3box entries / game in the league
Kind of attack, then how the shot was set up
Shots
Goals
xG
xG / shot
Attacking transition23% of shots
154
19
15.46
0.10
Cross
6
1
0.84
0.14
Cut-back
20
3
2.57
0.13
Long ball
15
2
1.71
0.11
Pass into the box / short pass
85
8
7.12
0.08
Own creation (carry, dribble, rebound)
28
5
3.21
0.11
Settled attack56% of shots
370
40
35.22
0.10
Cross
44
6
5.21
0.12
Cut-back
33
2
3.66
0.11
Long ball
16
1
1.82
0.11
Pass into the box / short pass
217
27
20.07
0.09
Own creation (carry, dribble, rebound)
60
4
4.46
0.07
Set piece21% of shots
141
18
20.60
0.15
Corner
108
6
8.90
0.08
Free kick
19
0
0.58
0.03
Penalty
14
12
11.12
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
Kylian Mbappé
62
6.60
5
Arda Güler
49
5.67
7
Vinícius Júnior
66
5.65
6
Federico Valverde
39
4.19
7
Brahim Díaz
29
2.80
6
Who takes them
Player
Shots
xG
Goals
Kylian Mbappé
146
20.80
25
Vinícius Júnior
101
13.62
16
Jude Bellingham
51
7.00
6
Arda Güler
49
3.86
4
Gonzalo García
21
3.69
6
Vision AI Model Insight
The biggest source of Real Madrid's chances, by xG, is settled attack (35.2 of 71.3 xG, 370 of 665 shots), mostly pass into the box / short pass. 154 of the 665 shots (23%) came within 15 seconds of winning the ball. 141 of the 665 shots came from set pieces; 77 goals from 71.3 xG (+5.7).
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
25CB · 5%
25Long Wide · 5%
51Long Central · 10%
195Wide → FB · 39%
210Central · 42%
Sequence success — final-third
1546final-third sequences this season
27%succeed — produce a shot
28%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
CB25 · 5%
Long Wide25 · 5%
Long Central51 · 10%
Wide → FB195 · 39%
Central210 · 42%
Who creates — volume × threat
70Güler
69Júnior
64Mbappé
43Valverde
34Bellingham
30Díaz
28Alexander-Arnold
506key passes #2 of 20
15.5box entries/game #3 of 20
53.4final-3rd entries/game #1 of 20
1.88xG/game #2 of 20
77goals #2 of 20
+5.7finishing vs xG #9 of 20
◆ Vision AI Insight
Real Madrid lean left — 48% of their key passes come from that flank and its half-space (243 of 506), most often from Júnior (57). They funnel chances into the c of the final third, most often via LHS att → C att (75 key passes, most often from Júnior). Güler is the primary source overall. They generate 1.88 xG a game and finish it clinical — they beat their xG, led by Kylian Mbappé (25G), Vinícius Júnior (16G), Jude Bellingham (6G).
Crosses & Cutbacks — squad-wide
The question
How does Real Madrid deliver into the box from wide areas, and what comes of it?
412 crosses: 160 found a team-mate (39%), 57 led to a shot (14%). 218 cut-backs: 116 found a team-mate (53%), 52 led to a shot (24%). 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?
412 crosses this season (the same crosses as the map above), split into fifteen territories — five channels (both wings, both half-spaces and the centre) by three depths from the halfway line to the byline. 24% come from one territory, the advanced left wing; 23 come from the half-spaces and 0 from the centre.
Territory
Crosses
Share
Led to a shot
advanced · left wing
100
24%
16
byline · left wing
99
24%
10
byline · right wing
76
18%
12
advanced · right wing
72
17%
11
deep · left wing
25
6%
2
deep · right wing
17
4%
5
byline · right half-space
10
2%
0
deep · left half-space
4
1%
0
advanced · left half-space
4
1%
0
byline · left half-space
3
1%
1
advanced · right half-space
2
0%
0
Finishing3 sections
Shot Map
665 shots for 71.3 xG (1.88/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. 17.5 shots/game at 0.107 xG/shot. Box entries 16/game (#3). Threat created 13.2 xT/game (#2).
Goal Sequences
The move behind each of Real Madrid’s 77 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 — 77 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
59 · 77%
Set pieces
6 · 8%
Penalties
12 · 16%
By finish
Right foot
56 · 73%
Left foot
13 · 17%
Header
8 · 10%
Vision AI Model Insight
77 goals scored. 77% come in open play; set pieces add 8%. 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
1144
Failed forward pass
738
Failed take-on
538
Lost in the defensive third
740
Their weakest zone is the right half-space in the attacking third: 22% of 883 pass attempts there are lost (196 times). Separately, 41% 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.
65
Pressing Index /100 · strong
Intensity52
PPDA, high-press share, high-zone actions
Height80
how high up the pitch they defend
Effectiveness78
high recoveries & high-regain share
Disruption50
opponent completion & progression allowed
Every defensive action & where it happens
Where they win the ball back — by lane
RW
16%
RHS
16%
C
24%
LHS
22%
LW
22%
By third (high / mid / low regain)
Def 48%
Mid 37%
Att 15%
Read
Ranked #10 of 20 for press intensity, this is a high press. 52% of defensive actions happen in the middle & attacking thirds, at an average height of 38. Of recoveries, 55% are won high up the pitch.
Every pressing metric, ranked in the league
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#10/20
lower = more intense; rank is what matters
Defensive actions / game
112
#20/20
High-press share
22%
#4/20
def actions won high up
Defensive-action height (0–100)
38.4
#4/20
avg pitch height of actions
High recoveries / game
22.8
#4/20
ball won in mid+att third
High-recovery share
55%
#4/20
share of recoveries won high
Opponent completion allowed
82%
#13/20
lower = more disruptive
Opponent progressive allowed
8.8%
#7/20
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 1203 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 5.3 opponent passes in the first half-hour and after 4.1 after the hour mark (sooner).
PASSES THEY GET
4.7passes
median 3 · before Real Madrid win it
TIME THEY KEEP IT
13sec
median 6s · 31.7 wins per game
How many passes the opponent got in before the ball was won
45%
27%
14%
13%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
5.2
15s
15–30′
5.3
16s
30–45+′
4.8
13s
45–60′
4.4
12s
60–75′
4.3
12s
75–90+′
3.9
12s
average passesaverage seconds
Phases of play
Every moment of the match read twice — what Real Madrid were doing with the
ball, and what the opposition were doing about it at that exact moment. Built from the
real action sequence in time order, so one possession is split across the phases it
genuinely passed through rather than labelled once by where it began.
7,311phase segments analysed
13.0smedian time before the opposition engage
2.0their passes per defensive action when pressed
#4of 20 for share of defending spent pressing high
High press 9.1%Mid block 15.4%Low block 46.0%Unopposed 29.5%
What opponents do against Real Madrid
The mirror image, and the part no other section can show: whether teams come
out to press Real Madrid or sit in and invite them on. A side that is always faced with a low
block is playing a different sport from one that is pressed every week.
High press 3.8%Mid block 9.2%Low block 57.6%Unopposed 29.4%
Which phases actually produce
Phase
Segments
Avg length
Avg actions
Ends in shot
Turned over
Build-up
1,935
6.1s
3.5
0.1%
40.8%
Progression
3,032
9.2s
5.3
0.6%
34.4%
Chance creation
2,179
7.1s
4.1
28.2%
45.1%
Attacking transition
165
7.3s
4.6
3.6%
27.3%
Most common routes through the phases
How a possession actually travelled, in order. The chain is the tactical signature — a move that climbs all three thirds is a different thing from one that never leaves the first.
Route
Times
Ends in shot
build_up
613
0.2%
progression
523
0.2%
progression → chance_creation
381
31.2%
chance_creation
370
43.5%
build_up → progression
267
0.0%
build_up → progression → chance_creation
256
32.8%
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) 5,554 passes
nothing unusual in this zone
Width of use-0.4919.98
Wide-channel share-0.410.28
Side bias+0.180.84
Pass length-0.4121.23
Long-ball share-0.680.16
Forward gain per pass-0.579.00
Backward-pass share-0.020.20
Deep-pass share-0.580.23
Completion+0.820.86
Seconds between passes-0.373.11
Side used
left 27% · central 48% · right 25%
Progression (middle third) 11,912 passes
safer
Width of use-0.4624.61
Wide-channel share-0.370.38
Side bias+0.622.53
Pass length+0.1217.40
Long-ball share-0.560.08
Forward gain per pass-0.373.04
Backward-pass share-0.310.32
Deep-pass share-0.660.13
Completion+1.200.92
Seconds between passes-0.443.00
Side used
left 37% · central 33% · right 30%
Final third 6,193 passes
safer, more recycling, circulating
Width of use-0.7429.78
Wide-channel share-0.790.52
Side bias+0.835.38
Pass length-0.3514.93
Long-ball share-0.540.07
Forward gain per pass-1.01-0.30
Backward-pass share+1.030.45
Deep-pass share-0.650.07
Completion+1.300.80
Seconds between passes-0.183.26
Side used
left 48% · central 19% · 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
#3 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
130attacking transition sequences this season
25%succeed — produce a shot
33%turned over with nothing to show
Sequence success — defensive transition
388defensive transition sequences this season
74%succeed — reach the next third or a shot
4%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 (#3 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? 1437 opponent progressive passes and 409 shots faced (40.6 xG against) across the season.
Opponent threat by lane (attacking into Real Madrid)
Right wing
30%
Right half-space
12%
Central
20%
Left half-space
11%
Left wing
26%
Where the danger came from. Opponents generated most of their threat through the right wing (30% 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 (315 total)
By situation
Open play
27 · 79%
Set pieces
3 · 9%
Penalties
4 · 12%
Vision AI Model Insight
34 goals conceded. 79% come in open play; set pieces account for only 9%, so dead-ball defending is not the main issue. 15% are headers — aerial defending is not where the damage comes from. Their supply line runs mainly through the right wing of the attacking third (19% 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
59 g · 50.7 xG
Set pieces
6 g · 9.5 xG
Penalties
12 g
8% of goals come from set pieces (6 of 77), worth 9.5 xG from 127 shots. Set pieces are a secondary threat; the danger is in open play.
Where do Real Madrid's corners go?
239 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 (117)
Delivered into the box 94%Short 6%
Left side (122)
Delivered into the box 92%Short 8%
Where opponents attack Real Madrid from set pieces
51 shots from opponent corners against Real Madrid, 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 (32)
Shot 32Goals 1
Left flag (19)
Shot 19Goals 0
Tactical Strengths & Weaknesses
Strengths (top-third vs league)
progression /game — 54.63 (95th pct)
xG created /game — 1.88 (90th pct)
field tilt % — 63.08 (90th pct)
threat created /game — 13.22 (90th pct)
pass retention % — 88.18 (90th pct)
Weaknesses (bottom-third vs league)
none stand out
Style Archetype & Similar Teams
Team archetype: High-press possession side. Their closest stylistic matches in the league (cosine similarity on style metrics) — sides that play a similar way.
Barcelona
92%
Atletico
80%
Athletic Club
55%
Real Betis
27%
Elche
10%
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 (12m)
1.58
0.67
2.42
vs mid-block opponents (12m)
2.17
1.42
1.67
vs low-block opponents (14m)
2.29
0.71
2.64
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%
13%
45
11.4
14.0
vs mid-block opponents
88%
9%
56
13.4
15.8
vs low-block opponents
90%
8%
62
14.6
16.5
Vision AI Model Insight
Facing a high press, retention drops to 85% vs 90% against a low block (5pt lower under pressure), and directness (long-ball share) sits at 13% when pressed vs 8% against a low block — a real style switch: they go noticeably more direct when pressed rather than trying to play through it.
Scouting Summary — the team in brief
In brief. Real Madrid are possession-dominant with a aggressive high-press.
Their attack creates 1.88 xG a game and they concede 1.07 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 38 matches this season
PPDAat Barcelona · 0-2 · 2026-05-1017.0avg 6.8
Pass accuracyat Real Sociedad · 2-1 · 2025-09-1376%avg 88%
Defensive actionsat Athletic Club · 3-0 · 2025-12-03157avg 112
Passesvs Barcelona · 2-1 · 2025-10-26312avg 623
Expected goalsvs Barcelona · 2-1 · 2025-10-263.66avg 1.88
Field tiltat Real Sociedad · 2-1 · 2025-09-1335%avg 63%
Progressive carriesat Real Sociedad · 2-1 · 2025-09-1324avg 58
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 12 matches added up: where the value came from, who produced it, and the six biggest single moves. OBV only measures what a player does with the ball: it shows who added the most on-ball value, not who was the most important player (defending off the ball, positioning and pressing are not in it).
+18.9on-ball value, 12 matches
Where the value came from
Passes7516 actions+7.42
Carries1859 actions-1.97
Dribbles251 actions-0.15
Shots186 actions+9.93
Ball wins635 actions+3.70
Most on-ball value (OBV)
Actions
OBV
per 100
Vinícius Júnior
600
+4.90
+0.82
Kylian Mbappé
393
+1.80
+0.46
Jude Bellingham
556
+1.71
+0.31
Aurélien Tchouaméni
717
+1.60
+0.22
Arda Güler
394
+1.15
+0.29
Eduardo Camavinga
445
+1.07
+0.24
Dean Huijsen
684
+1.06
+0.16
Federico Valverde
706
+1.03
+0.15
Biggest single moves
OBV
1 Vinícius Júnior · shot
+0.788
2 Jude Bellingham · shot
+0.614
3 Arda Güler · shot
+0.435
4 Vinícius Júnior · shot
+0.324
5 Vinícius Júnior · shot
+0.293
6 Arda Güler · pass
+0.292
Set Pieces by Player
Who delivers Real Madrid’s dead balls and who finishes them this season. 239 corners taken, most by Arda Güler (99); 127 shots from corners and free kicks, 6 goals, led by Raúl Asencio (2 goals, 0.45 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
99
76 (77%)
6
1
0.56
1
—
Kylian Mbappé
8
1 (12%)
13
—
0.56
6
8/9
Trent Alexander-Arnold
51
46 (90%)
7
—
0.18
4
—
Federico Valverde
2
1 (50%)
20
—
0.50
4
—
Aurélien Tchouaméni
—
—
14
1
0.90
—
—
Franco Mastantuono
21
15 (71%)
6
—
0.31
1
—
Vinícius Júnior
14
2 (14%)
3
—
1.35
—
4/5
Rodrygo
25
17 (68%)
4
—
0.24
2
—
Dean Huijsen
—
—
10
1
1.35
—
—
Antonio Rüdiger
—
—
9
—
0.88
—
—
Brahim Díaz
15
6 (40%)
3
—
0.16
—
—
Éder Militão
—
—
8
1
0.59
—
—
Raúl Asencio
—
—
7
2
0.45
—
—
Jude Bellingham
—
—
6
—
0.97
—
—
Top Performers
Ranked by Vision AI Score — the same tactical behavioural evaluation each player's own report page shows (min 300 minutes). Coloured chips are that player's strongest capability blocks.
Chance creation leaders — key passes /90 and box entries.
player
pos
min
key P /90
box entries
xT
Vinícius Júnior
F
2920
2.13
88
30.6
Arda Güler
M
2057
3.06
79
40.4
Kylian Mbappé
F
2718
2.12
78
21.0
Federico Valverde
M
2890
1.34
53
44.7
Trent Alexander-Arnold
D
1223
2.06
44
28.3
Jude Bellingham
M
1997
1.53
36
21.8
Brahim Díaz
M
1350
2.00
36
10.5
Álvaro Carreras
D
2450
1.03
21
41.4
Wide Delivery Leaders
Crosses and cutbacks — wingers and full-backs only (by real average position, not the raw D/M position code), ranked by combined volume.
player
pos
min
crosses (completed)
cutbacks (completed)
Vinícius Júnior
W
2920
38 (34%)
80 (60%)
Trent Alexander-Arnold
FB
1223
25 (28%)
25 (36%)
Álvaro Carreras
FB
2450
16 (25%)
15 (47%)
Defensive Volume Leaders
Who does the defensive work — tackles, interceptions, recoveries.
player
pos
min
def actions
tackles
intercept
recoveries
Aurélien Tchouaméni
M
2734
442
68
47
144
Álvaro Carreras
D
2450
410
41
39
155
Dean Huijsen
D
2195
353
31
19
74
Federico Valverde
M
2890
334
41
24
142
Jude Bellingham
M
1997
289
51
14
105
Eduardo Camavinga
M
1625
242
65
14
84
Arda Güler
M
2057
213
46
9
101
Éder Militão
D
1203
200
23
11
56
Full Squad Data
Every player with 100+ minutes (27 players).
player
pos
apps
min
goals
xG
key P
prog
def
xT
Thibaut Courtois
G
32
3032
0
0.0
1
66
86
23.5
Vinícius Júnior
F
36
2920
16
13.6
69
232
164
30.6
Federico Valverde
M
33
2890
5
2.6
43
188
334
44.7
Aurélien Tchouaméni
M
33
2734
1
2.9
22
123
442
39.8
Kylian Mbappé
F
31
2718
25
20.8
64
80
78
21.0
Álvaro Carreras
D
28
2450
2
0.6
28
195
410
41.4
Dean Huijsen
D
28
2195
2
2.0
10
135
353
41.2
Arda Güler
M
33
2057
4
3.9
70
173
213
40.4
Jude Bellingham
M
28
1997
6
7.0
34
63
289
21.8
Raúl Asencio
D
23
1792
2
1.0
5
83
195
23.9
Eduardo Camavinga
M
29
1625
1
1.1
15
86
242
22.1
Antonio Rüdiger
D
18
1553
1
1.1
2
52
183
19.3
Brahim Díaz
M
30
1350
1
1.6
30
48
99
10.5
Trent Alexander-Arnold
D
21
1223
0
0.3
28
146
112
28.3
Éder Militão
D
16
1203
2
1.7
8
28
200
13.8
Franco Mastantuono
M
23
1094
1
2.6
15
48
108
11.3
Gonzalo García
F
30
1088
6
3.7
10
13
97
2.8
Dani Carvajal
D
17
1029
0
0.4
11
92
114
14.4
Fran García
D
13
994
1
0.9
5
47
121
7.9
Rodrygo
M
19
762
1
2.6
17
41
80
8.2
Thiago Pitarch
M
10
600
0
0.4
6
21
90
5.5
Andrii Lunin
G
6
569
0
0.0
0
16
18
4.8
Dani Ceballos
M
16
548
0
0.1
9
29
89
9.6
David Alaba
D
11
463
0
0.2
0
22
53
7.8
Ferland Mendy
D
5
226
0
0.0
0
26
21
3.7
Manuel Ángel
M
4
111
0
0.1
0
12
14
1.8
César Palacios
M
5
101
0
0.2
1
2
13
0.5
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.
Thibaut Courtois
Goalkeeper · Goalkeeper
minutes3032
goals · xG0 · 0.0
prog passes /902.0
key passes /900.03
def actions /902.6
pass accuracy83%
Vinícius Júnior
Creative forward · Forward
minutes2920
goals · xG16 · 13.6
prog passes /907.2
key passes /902.13
def actions /905.1
pass accuracy80%
Federico Valverde
Box-to-box midfielder · Midfielder
minutes2890
goals · xG5 · 2.6
prog passes /905.9
key passes /901.34
def actions /9010.4
pass accuracy89%
Aurélien Tchouaméni
Ball-winner / destroyer · Midfielder
minutes2734
goals · xG1 · 2.9
prog passes /904.0
key passes /900.72
def actions /9014.6
pass accuracy91%
Kylian Mbappé
Creative forward · Forward
minutes2718
goals · xG25 · 20.8
prog passes /902.6
key passes /902.12
def actions /902.6
pass accuracy85%
Álvaro Carreras
Ball-playing defender · Defender
minutes2450
goals · xG2 · 0.6
prog passes /907.2
key passes /901.03
def actions /9015.1
pass accuracy90%
Dean Huijsen
Ball-playing defender · Defender
minutes2195
goals · xG2 · 2.0
prog passes /905.5
key passes /900.41
def actions /9014.5
pass accuracy90%
Arda Güler
Advanced creator · Attacking mid
minutes2057
goals · xG4 · 3.9
prog passes /907.6
key passes /903.06
def actions /909.3
pass accuracy85%
Jude Bellingham
Ball-winner / destroyer · Midfielder
minutes1997
goals · xG6 · 7.0
prog passes /902.8
key passes /901.53
def actions /9013.0
pass accuracy88%
Recruitment Intelligence — profiles to sign
The question
If you were sporting director, which player profiles would move the needle most?
Each recruitment target is derived directly from the team's weakest areas vs the league — the gap, why it shows in the data, and the profile that closes it.
Need — below-average: press intensity (PPDA) (50th pct)
Sign a high-energy presser to lift defensive intensity. The data flags this because the press is passive (6.83, 50th percentile vs the league).
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