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Team reportBundesliga 2025/26

Borussia Dortmund

A possession-dominant side, aggressive high-press out of possession, with a high-volume attack. Finished #2 of 21 on 90 points (27-9-5, 82:38). Tactical profile below is built from 41 matches of ball-by-ball event data, contextualised against the league.

possession-dominantaggressive high-presshigh-volume attack
W
W
L
L
W
L
W
W
W
W
W
W
D
D
W
90
points
#2 of 21 · 41 games
82:38
goals
+44 GD
27-9-5
record
W-D-L
2.20
points/game
#2 in the league
1.63
xG for/game
rank #5
1.13
xG against/game
rank #2
#6
press rank
of 21 · most aggressive
59%
field tilt
#2
Borussia Dortmund
League average
Expected goals for / game
1.631.43
Expected goals against / game
1.131.52
Field tilt
59%49%
Pass retention
82%79%
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 Borussia Dortmund and how good was the season?
Read. Borussia Dortmund finished #2 of 21 on 90 points (27W-9D-5L, 2.20 pts/game), scoring 82 and conceding 38 for a goal difference of +44. On the ball they are a possession-dominant team (59% field tilt, #2 in the league); off it they defend with a aggressive high-press (#6 of 21 for press intensity). Across the 41 matches of event data, they create 1.63 xG/game (#5) and concede 1.13 (#2), a net of +0.50 xG/game — the underlying performance diverges from the actual goal difference.

Season Context — the league table

The question
Where did Borussia Dortmund actually finish, and against whom?

The authoritative final table (full season), centred on Borussia Dortmund.

#teamPWDLGFGAGDPts
1Bayern41337114742+105106
2Borussia Dortmund4127958238+4490
3RBL41245128658+2877
4Leverkusen41219118459+2572
5Hoffenheim41217137966+1370

Match Momentum — when they create and concede danger

The question
Across a whole game, which periods does Borussia Dortmund 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+'+6.07
Vision AI Model Insight
Borussia Dortmund tend to fade after a strong start — first-half net xG +10.64, second-half +9.72. Their strongest period is 31-45' (net +6.07 xG); their most dangerous period to face is 16-30' (net +0.40 xG against them).

xG Race — the season, match by match

Expected goals piling up over 41 matches: Borussia Dortmund 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 (+20.4 xG). Hover a point for that match.

024487266.946.5St. Pauli (A) 3-3 · xG 1.41 to 1.71Union Berlin (H) 3-0 · xG 1.92 to 0.80Heidenheim (A) 2-0 · xG 2.00 to 0.46Wolfsburg (H) 1-0 · xG 0.87 to 0.98Mainz (A) 2-0 · xG 1.18 to 0.57RBL (H) 1-1 · xG 0.96 to 1.08Bayern (A) 1-2 · xG 1.24 to 2.04Koln (H) 1-0 · xG 1.88 to 0.76Augsburg (A) 1-0 · xG 0.42 to 0.82Hamburg (A) 1-1 · xG 1.04 to 0.63Stuttgart (H) 3-3 · xG 3.38 to 1.59Leverkusen (A) 2-1 · xG 0.84 to 1.00Hoffenheim (H) 2-0 · xG 1.07 to 1.43Freiburg (A) 1-1 · xG 1.02 to 1.64Borussia Monchengladbach (H) 2-0 · xG 1.10 to 0.39Eintracht Frankfurt (A) 3-3 · xG 2.10 to 1.79Werder Bremen (H) 3-0 · xG 2.11 to 0.55St. Pauli (H) 3-2 · xG 2.21 to 0.81Union Berlin (A) 3-0 · xG 1.86 to 1.05Heidenheim (H) 3-2 · xG 3.75 to 1.85Wolfsburg (A) 2-1 · xG 1.62 to 0.97Mainz (H) 4-0 · xG 2.04 to 1.80RBL (A) 2-2 · xG 1.44 to 2.52Bayern (H) 2-3 · xG 1.06 to 2.13Koln (A) 2-1 · xG 1.38 to 1.57Augsburg (H) 2-0 · xG 2.41 to 0.50Hamburg (H) 3-2 · xG 5.33 to 0.90Stuttgart (A) 2-0 · xG 0.39 to 0.71Leverkusen (H) 0-1 · xG 1.04 to 0.64Hoffenheim (A) 1-2 · xG 0.72 to 2.21Freiburg (H) 4-0 · xG 2.53 to 0.75Borussia Monchengladbach (A) 0-1 · xG 0.52 to 2.42Eintracht Frankfurt (H) 3-2 · xG 1.31 to 1.06Werder Bremen (A) 2-0 · xG 2.23 to 0.62Hamburg (H) 2-0 · xG 1.48 to 0.70Hoffenheim (A) 3-2 · xG 1.72 to 1.24Paderborn (H) 3-0 · xG 1.52 to 0.50Stuttgart (A) 1-0 · xG 1.49 to 0.81Werder Bremen (H) 2-2 · xG 1.95 to 0.73Union Berlin (A) 0-0 · xG 0.81 to 0.83Eintracht Frankfurt (H) 1-0 · xG 1.53 to 0.90M1M6M11M16M21M26M31M36M41
Borussia DortmundOpponents against Borussia Dortmund

Tactical DNA

The football question
In five genes, what is Borussia Dortmund'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.

83
Build-Up
possession control from the back
86
Progression
moving the ball upfield with threat
71
Creation
manufacturing chances
81
Press
winning the ball high & hard
90
Solidity
resisting the opponent's threat
Genetic identity
Borussia Dortmund are defined most by their Solidity DNA (90/100 · elite at resisting the opponent's threat), and least by their Creation DNA (71/100). Percentiles vs the 21 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).

Possess75Disrupt79Finish81Press83Defend75
Possess
Deep buildup
67%
Possession
67%
Field tilt
90%
Disrupt
Central progression ~proxy
86%
Dribble tendency
67%
Progressive passes
86%
Finish
Final-third patience
90%
Shot quality
81%
Open-play xG
71%
Press
Forward press ~proxy
71%
High regains
86%
Pressing efficiency ~proxy
90%
Defend
Def-mid press ~proxy
81%
High line ~proxy
90%
PAdj defensive actions
52%
Borussia Dortmund is defined most by Press (83% in the league) and weakest on Possess (75%). Percentiles vs the 21 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 Borussia Dortmund actually control, relative to the rest of the league?

Field tilt = share of final-third touches between the two sides across Borussia Dortmund'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; Borussia Dortmund highlighted. Hover any dot for the team.

Bayern: 73% tilt, 2.59 pts/gBorussia Dortmund: 59% tilt, 2.20 pts/gRBL: 55% tilt, 1.88 pts/gLeverkusen: 57% tilt, 1.76 pts/gHoffenheim: 57% tilt, 1.71 pts/gStuttgart: 58% tilt, 1.66 pts/gFreiburg: 48% tilt, 1.46 pts/gElversberg: 37% tilt, 1.43 pts/gEintracht Frankfurt: 51% tilt, 1.27 pts/gAugsburg: 46% tilt, 1.27 pts/gMainz: 51% tilt, 1.22 pts/gHamburg: 39% tilt, 1.15 pts/gPaderborn: 46% tilt, 1.14 pts/gUnion Berlin: 45% tilt, 1.10 pts/gWerder Bremen: 48% tilt, 1.07 pts/gBorussia Monchengladbach: 42% tilt, 1.00 pts/gKoln: 47% tilt, 0.98 pts/gSchalke: 38% tilt, 0.86 pts/gWolfsburg: 41% tilt, 0.85 pts/gSt. Pauli: 49% tilt, 0.76 pts/gHeidenheim: 39% tilt, 0.76 pts/gBorussia Dortmund37%49%61%73%0.81.42.02.6FIELD TILT %POINTS / GAME
Vision AI Model Insight
Borussia Dortmund post 59% field tilt, ranking #2 of 21 in the league — a genuinely territory-dominant side, and their 2.20 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 Borussia Dortmund has the ball, how much of that time is spent building, progressing and creating?

The share of Borussia Dortmund'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: 21.0% of the time on the ball21%Progression: 50.3% of the time on the ball50%Chance creation: 26.6% of the time on the ball27%Attacking transition: 2.0% of the time on the ball50%PROGRESSION
PhaseShare of the time
Build-up21.0%
Progression50.3%
Chance creation26.6%
Attacking transition2.0%
Vision AI Model Insight
Progression is where Borussia Dortmund spends most of its time on the ball (50% 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.

Waldemar Anton ↔ Nico Schlotterbeck: 830 passes, 15.7 combined xTRamy Bensebaini ↔ Waldemar Anton: 415 passes, 8.8 combined xTWaldemar Anton ↔ Gregor Kobel: 408 passes, 18.1 combined xTGregor Kobel ↔ Nico Schlotterbeck: 370 passes, 14.2 combined xTWaldemar Anton ↔ Julian Ryerson: 329 passes, 19.2 combined xTRamy Bensebaini ↔ Nico Schlotterbeck: 323 passes, 11.1 combined xTRamy Bensebaini ↔ Daniel Svensson: 322 passes, 17.4 combined xTNico Schlotterbeck ↔ Daniel Svensson: 287 passes, 17.0 combined xTWaldemar Anton ↔ Jobe Bellingham: 245 passes, 9.4 combined xTFelix Nmecha ↔ Nico Schlotterbeck: 240 passes, 9.9 combined xTWaldemar Anton ↔ Felix Nmecha: 235 passes, 8.5 combined xTMarcel Sabitzer ↔ Julian Ryerson: 216 passes, 13.5 combined xTMarcel Sabitzer ↔ Waldemar Anton: 189 passes, 8.2 combined xTNico Schlotterbeck ↔ Jobe Bellingham: 186 passes, 5.9 combined xTRamy Bensebaini ↔ Gregor Kobel: 168 passes, 7.7 combined xTFelix Nmecha ↔ Daniel Svensson: 157 passes, 9.5 combined xTJulian Brandt ↔ Julian Ryerson: 133 passes, 9.0 combined xTRamy Bensebaini ↔ Felix Nmecha: 125 passes, 4.9 combined xTFelix Nmecha ↔ Jobe Bellingham: 122 passes, 4.6 combined xTJulian Brandt ↔ Nico Schlotterbeck: 121 passes, 10.2 combined xTJulian Ryerson ↔ Jobe Bellingham: 120 passes, 6.8 combined xTNico Schlotterbeck ↔ Maximilian Beier: 113 passes, 12.2 combined xTRamy Bensebaini ↔ Maximilian Beier: 111 passes, 9.5 combined xTJulian Brandt ↔ Daniel Svensson: 107 passes, 7.1 combined xTGregor Kobel — isolate this player's connectionsKobelDaniel Svensson — isolate this player's connectionsSvenssonMaximilian Beier — isolate this player's connectionsBeierSerhou Guirassy — isolate this player's connectionsGuirassyWaldemar Anton — isolate this player's connectionsAntonJulian Ryerson — isolate this player's connectionsRyersonFelix Nmecha — isolate this player's connectionsNmechaJobe Bellingham — isolate this player's connectionsBellinghamNico Schlotterbeck — isolate this player's connectionsSchlotterbFábio Silva — isolate this player's connectionsSilvaMarcel Sabitzer — isolate this player's connectionsSabitzerCarney Chukwuemeka — isolate this player's connectionsChukwuemekJulian Brandt — isolate this player's connectionsBrandtKarim Adeyemi — isolate this player's connectionsAdeyemiRamy Bensebaini — isolate this player's connectionsBensebainiYan Couto — isolate this player's connectionsCouto
width = passes between the paircolour = xT per pass: low → high

Top 10 combinations

Anton ↔ Schlotterbeck
830p · 15.7xT
Bensebaini ↔ Anton
415p · 8.8xT
Anton ↔ Kobel
408p · 18.1xT
Kobel ↔ Schlotterbeck
370p · 14.2xT
Anton ↔ Ryerson
329p · 19.2xT
Bensebaini ↔ Schlotterbeck
323p · 11.1xT
Bensebaini ↔ Svensson
322p · 17.4xT
Schlotterbeck ↔ Svensson
287p · 17.0xT
Anton ↔ Bellingham
245p · 9.4xT
Nmecha ↔ Schlotterbeck
240p · 9.9xT
Vision AI Model Insight
Busiest connection: Waldemar Anton ↔ Nico Schlotterbeck (830 passes). Most dangerous per pass: Nico Schlotterbeck ↔ Maximilian Beier (0.11 xT/pass over 113 passes) — the combination that hurts opponents most when it fires, not just the one used most.

Touch Density — where Borussia Dortmund had the ball

Every touch across 369 matches. Brighter means more touches.

Which Zones Do They Attack Through?

The question
Across the whole pitch, where does Borussia Dortmund 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
22245
ACTIONS
3%4%10%3%3%9%11%13%11%8%8%4%4%4%6%PASSES
Carries
the ball moved with a player
5033
ACTIONS
3%5%11%4%2%8%10%17%12%7%7%3%3%3%5%CARRIES
Vision AI Model Insight
Their single busiest zone is the centre of the middle third (13.9% of all actions). 65% of actions run through the inside lanes (centre + both half-spaces) versus 35% down the two wings — an inside-dominant, half-space build. By third: 24% defensive, 51% middle, 25% 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. Borussia Dortmund leaned on their right (39%). The bars under the pitch put the league’s share beside theirs.

34%Left side27%Middle39%Right sideATTACKING →
Borussia Dortmund
34%27%39%
League
38%24%38%

Final-Third Entries

Every completed pass or carry that took the ball from outside the final third into it, by the channel it arrived in. Borussia Dortmund made 2118 this season (51.7 a match); 31% came into the right 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: 499 entries (24%)49924%LEFT WINGLeft half-space: 322 entries (15%)32215%LEFT HALF-SPACECentre: 348 entries (16%)34816%CENTRERight half-space: 291 entries (14%)29114%RIGHT HALF-SPACERight wing: 658 entries (31%)65831%RIGHT WINGATTACKING ↑
ChannelEntriesPer matchShareLeague share
Left wing49912.224%27%
Left half-space3227.915%16%
Centre3488.516%14%
Right half-space2917.114%15%
Right wing65816.031%28%
Build-up2 sections

Build-Up — the pass map

The football question
How does Borussia Dortmund 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.

5319build-up passes attempted
81%completed
31%reach the middle third
2%go long into the final third

Pass types

2106CB · 49%
238Long Wide · 6%
61Long Central · 1%
1382Wide → FB · 32%
514Central · 12%

Sequence success — build-up

1012build-up sequences this season
11%succeed — reach the next third or a shot
42%turned over with nothing to show
CB2106 · 49%
right: 1072 passes, most from Anton1072left: 1034 passes, most from Schlotterbeck1034
Long Wide238 · 6%
right: 124 passes, most from Kobel124left: 114 passes, most from Kobel114
Long Central61 · 1%
left: 40 passes, most from Kobel40right: 21 passes, most from Kobel21
Wide → FB1382 · 32%
right: 741 passes, most from Kobel741left: 641 passes, most from Kobel641
Central514 · 12%
right: 273 passes, most from Kobel273left: 241 passes, most from Kobel241
◆ Vision AI Insight
Borussia Dortmund build predominantly down the right, with Kobel as the fulcrum (1064 build-up passes). Only 33% 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 (333×).

Pass Explorer

Every pass of Borussia Dortmund’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. 2227 progressive passes across 41 matches (54.3/game).

Origin lane

Right wing
30%
Right half-space
14%
Central
16%
Left half-space
14%
Left wing
26%
Progression is right-dominant: 44% right side · 16% central · 40% left.

Who generates it

Julian Ryerson
290 · 13%
Nico Schlotterbeck
285 · 13%
Daniel Svensson
195 · 9%
Waldemar Anton
160 · 7%
Gregor Kobel
159 · 7%
Top two — Julian Ryerson & Nico Schlotterbeck — account for 26% of all progression.
PROGRESSION
54.3 prog passes/game
mostly right
→
PENETRATION
27% reach final third
20% reach the box
→
THREAT
the other 73% is recycled
progress ≠ danger
What came of it. 27% of progressive passes reached the final third and 20% the box; the rest was recycled in front of the block. 26% of the progression ran through two players, mostly down the right side.

Which lanes actually create the threat

Share of passes played from each lane against its share of the threat (xT) they add. Efficient = more threat than volume, sterile = lots of the ball, little damage.

Per lane — volume vs threat
Right wingbalanced
Right half-spacebalanced
Centralbalanced
Left half-spacebalanced
Left wingbalanced

Progression — the pass map

The football question
Once Borussia Dortmund 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.

11260progression passes attempted
87%completed
17%break into the final third
10%go backward into the defensive third

Pass types

4452CB · 46%
245Long Wide · 3%
37Long Central · 0%
3703Wide → FB · 38%
1336Central · 14%

Sequence success — progression

1976progression sequences this season
28%succeed — reach the next third or a shot
39%turned over with nothing to show
CB4452 · 46%
left: 2383 passes, most from Schlotterbeck2383right: 2069 passes, most from Anton2069
Long Wide245 · 3%
right: 155 passes, most from Ryerson155left: 90 passes, most from Svensson90
Long Central37 · 0%
right: 21 passes, most from Ryerson21left: 16 passes, most from Ryerson16
Wide → FB3703 · 38%
right: 2081 passes, most from Ryerson2081left: 1622 passes, most from Svensson1622
Central1336 · 14%
left: 679 passes, most from Nmecha679right: 657 passes, most from Nmecha657
◆ Vision AI Insight
Borussia Dortmund progress predominantly down the right, with Anton the busiest midfield outlet (1634 progression passes). Only 17% of passes that start in midfield actually break the final-third line — the rest (74%) is recycled back into midfield or (10%) played backward, the hallmark of a side that carries progression itself through midfield rather than just passing through it. Their most-repeated pattern is RHS mid → C mid (613×) — 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
1%
Neutral / sideways
47%
Safe / backward
37%
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 Borussia Dortmund actually progress the ball with their feet, not a pass?

1076 progressive carries this season across the whole squad (22% of 4995 tracked carries total), grouped into up to 4 spatial patterns by where each carry starts and ends together — not just its origin zone. Median 14m, longest 59m. Most active carrier: Anton (157). Filter to one player below.

First (354)Second (340)Third (209)Fourth (173)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 Borussia Dortmund'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.

551shots
66.9xG
0.12xG per shot
43big chances (0.3+ xG)
80goals
#7key passes / game in the league
#4box entries / game in the league
Attacking transition: 139 shots (25%)25%139Settled attack: 296 shots (54%)54%296Set piece: 116 shots (21%)21%116551SHOTS
Kind of attack, then how the shot was set upShotsGoalsxGxG / shot
Attacking transition 25% of shots1391916.260.12
Cross1031.450.15
Cut-back2543.250.13
Long ball1122.140.19
Pass into the box / short pass6977.210.10
Own creation (carry, dribble, rebound)2432.210.09
Settled attack 54% of shots2964131.500.11
Cross57138.250.14
Cut-back4545.970.13
Long ball520.260.05
Pass into the box / short pass1231811.370.09
Own creation (carry, dribble, rebound)6645.650.09
Set piece 21% of shots1162019.100.16
Corner951411.580.12
Free kick1200.370.03
Penalty967.150.79

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
Julian Ryerson416.159
Julian Brandt294.034
Maximilian Beier303.437
Serhou Guirassy333.414
Yan Couto172.382

Who takes them

PlayerShotsxGGoals
Serhou Guirassy10718.5319
Maximilian Beier677.9911
Felix Nmecha434.314
Nico Schlotterbeck373.926
Julian Brandt373.757
Vision AI Model Insight
The biggest source of Borussia Dortmund's chances, by xG, is settled attack (31.5 of 66.9 xG, 296 of 551 shots), mostly pass into the box / short pass. 139 of the 551 shots (25%) came within 15 seconds of winning the ball. 116 of the 551 shots came from set pieces; 80 goals from 66.9 xG (+13.1).

Chance Creation — the pass map

The football question
How does Borussia Dortmund 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

40CB · 10%
13Long Wide · 3%
47Long Central · 12%
94Wide → FB · 24%
205Central · 51%

Sequence success — final-third

1446final-third sequences this season
21%succeed — produce a shot
39%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
CB40 · 10%
right: 25 passes, most from Schlotterbeck25left: 15 passes, most from Bensebaini15
Long Wide13 · 3%
right: 8 passes, most from Ryerson8left: 5 passes, most from Bellingham5
Long Central47 · 12%
left: 31 passes, most from Ryerson31right: 16 passes, most from Ryerson16
Wide → FB94 · 24%
left: 50 passes, most from Nmecha50right: 44 passes, most from Ryerson44
Central205 · 51%
right: 107 passes, most from Ryerson107left: 98 passes, most from Ryerson98

Who creates — volume × threat

67Ryerson
39Svensson
33Beier
30Guirassy
29Brandt
26Nmecha
17Silva
399key passes #7 of 21
17.0box entries/game #4 of 21
41.7final-3rd entries/game #3 of 21
1.63xG/game #5 of 21
80goals #4 of 21
+13.1finishing vs xG #3 of 21
◆ Vision AI Insight
Borussia Dortmund lean right — 39% of their key passes come from that flank and its half-space (156 of 399), most often from Ryerson (45). They funnel chances into the c of the final third, most often via RW att → C att (65 key passes, most often from Ryerson). Ryerson is the primary source overall. They generate 1.63 xG a game and finish it clinical — they beat their xG, led by Serhou Guirassy (19G), Maximilian Beier (11G), Karim Adeyemi (7G).

Crosses & Cutbacks — squad-wide

The question
How does Borussia Dortmund deliver into the box from wide areas, and what comes of it?

634 crosses: 224 found a team-mate (35%), 72 led to a shot (11%). 183 cut-backs: 101 found a team-mate (55%), 67 led to a shot (37%). 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 Borussia Dortmund actually cross from?

634 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. 35% come from one territory, the byline right wing; 32 come from the half-spaces and 0 from the centre.

deep right wing: 31 crosses (5%)5%31deep right half-space: 0 crosses (0%)deep centre: 0 crosses (0%)deep left half-space: 1 crosses (0%)0%1deep left wing: 17 crosses (3%)3%17advanced right wing: 126 crosses (20%)20%126advanced right half-space: 4 crosses (1%)1%4advanced centre: 0 crosses (0%)advanced left half-space: 4 crosses (1%)1%4advanced left wing: 100 crosses (16%)16%100byline right wing: 221 crosses (35%)35%221byline right half-space: 11 crosses (2%)2%11byline centre: 0 crosses (0%)byline left half-space: 12 crosses (2%)2%12byline left wing: 107 crosses (17%)17%107
TerritoryCrossesShareLed to a shot
byline · right wing22135%35
advanced · right wing12620%14
byline · left wing10717%8
advanced · left wing10016%10
deep · right wing315%1
deep · left wing173%0
byline · left half-space122%2
byline · right half-space112%0
advanced · right half-space41%1
advanced · left half-space41%1
deep · left half-space10%0
Finishing3 sections

Shot Map

551 shots for 66.9 xG (1.63/game, #5 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.

High-volume attack. 13.4 shots/game at 0.121 xG/shot. Box entries 17/game (#4). Threat created 12.9 xT/game (#3).

Goal Sequences

The move behind each of Borussia Dortmund’s 82 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 — 82 goals traced.

How Do They Score?

The question
Where do the goals actually come from — open play, dead balls, which foot?

Every goal Borussia Dortmund scored across the loaded matches, broken down by how it was created and finished.

By situation

Open play
60 · 75%
Set pieces
14 · 18%
Penalties
6 · 8%

By finish

Right foot
46 · 58%
Left foot
18 · 22%
Header
16 · 20%
Vision AI Model Insight
80 goals scored. 75% come in open play; set pieces add 18%. 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

197 of 711 pass attempts lost here (28%).28%197/711Anton139 of 908 pass attempts lost here (15%).15%139/908Kobel367 of 2229 pass attempts lost here (16%).16%367/2229Kobel108 of 770 pass attempts lost here (14%).14%108/770Kobel207 of 701 pass attempts lost here (30%).30%207/701Svensson310 of 1911 pass attempts lost here (16%).16%310/1911Ryerson270 of 2347 pass attempts lost here (12%).12%270/2347Anton308 of 2939 pass attempts lost here (10%).10%308/2939Anton318 of 2349 pass attempts lost here (14%).14%318/2349Schlotterb281 of 1714 pass attempts lost here (16%).16%281/1714Schlotterb485 of 1693 pass attempts lost here (29%).29%485/1693Ryerson260 of 932 pass attempts lost here (28%).28%260/932Ryerson157 of 858 pass attempts lost here (18%).18%157/858Guirassy262 of 957 pass attempts lost here (27%).27%262/957Svensson338 of 1226 pass attempts lost here (28%).28%338/1226Svenssonattacking →

How they lose it

Incomplete pass — attacking third
1502
Failed forward pass
973
Failed take-on
428
Lost in the defensive third
1018
Their weakest zone is the left wing in the defensive third: 30% of 701 pass attempts there are lost (207 times). Separately, 37% 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.

Pressing3 sections

Pressing — the full picture

The football question
How hard does Borussia Dortmund 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.

82
Pressing Index
/100 · elite
Intensity75
PPDA, high-press share, high-zone actions
Height90
how high up the pitch they defend
Effectiveness88
high recoveries & high-regain share
Disruption76
opponent completion & progression allowed

Every defensive action & where it happens

Where they win the ball back — by lane

RW
19%
RHS
19%
C
25%
LHS
21%
LW
16%
By third (high / mid / low regain)
Def 46%
Mid 38%
Att 15%
Read
Ranked #6 of 21 for press intensity, this is a high press. 54% of defensive actions happen in the middle & attacking thirds, at an average height of 39. Of recoveries, 60% are won high up the pitch.

Every pressing metric, ranked in the league

metricvaluerankmeaning
PPDA (opp passes per def action)—#6/21lower = more intense; rank is what matters
Defensive actions / game139#18/21
High-press share24%#4/21def actions won high up
Defensive-action height (0–100)39.6#2/21avg pitch height of actions
High recoveries / game26.0#3/21ball won in mid+att third
High-recovery share60%#2/21share of recoveries won high
Opponent completion allowed80%#8/21lower = more disruptive
Opponent progressive allowed8.4%#2/21of 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 Borussia Dortmund win it back with a defensive action?

Counted over the 1405 opposition possessions that Borussia Dortmund 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 Borussia Dortmund step in. Borussia Dortmund win it after 4.8 opponent passes in the first half-hour and after 3.9 after the hour mark (sooner).

PASSES THEY GET
4.5passes
median 3 · before Borussia Dortmund win it
TIME THEY KEEP IT
12sec
median 5s · 34.3 wins per game
How many passes the opponent got in before the ball was won
44%
30%
14%
12%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
4.7
12s
15–30′
4.8
13s
30–45+′
5.4
15s
45–60′
4.1
11s
60–75′
4.0
11s
75–90+′
3.8
11s
average passesaverage seconds

Phases of play

Every moment of the match read twice — what Borussia Dortmund were doing with the ball, and what the opposition were doing about it at that exact moment. Built from the real action sequence in time order, so one possession is split across the phases it genuinely passed through rather than labelled once by where it began.

6,637phase segments analysed
9.0smedian time before the opposition engage
2.0their passes per defensive action when pressed
#4of 18 for share of defending spent pressing high

With the ball

Share of possession time spent in each phase.

Build-up 21.0%Progression 50.3%Chance creation 26.6%Attacking transition 2.0%

Without the ball

How Borussia Dortmund defend when the opponent has it.

High press 9.6%Mid block 14.2%Low block 46.9%Unopposed 29.3%

What opponents do against Borussia Dortmund

The mirror image, and the part no other section can show: whether teams come out to press Borussia Dortmund 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 6.1%Mid block 11.6%Low block 54.2%Unopposed 28.1%

Which phases actually produce

PhaseSegmentsAvg lengthAvg actionsEnds in shotTurned over
Build-up1,8964.4s2.90.1%46.1%
Progression2,8477.1s4.30.5%46.1%
Chance creation1,7756.0s3.522.8%53.5%
Attacking transition1196.6s4.32.5%30.3%

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.

RouteTimesEnds in shot
progression7340.0%
build_up7210.0%
chance_creation40334.2%
progression → chance_creation36522.7%
build_up → progression3200.0%
build_up → progression → chance_creation18323.5%

Attitude in each third

Every other style number here says how much Borussia Dortmund 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 Borussia Dortmund 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) 5,319 passes

nothing unusual in this zone

Width of use-0.6219.72
Wide-channel share-0.400.28
Side bias-0.17-0.48
Pass length-0.1122.29
Long-ball share-0.190.21
Forward gain per pass-0.0511.26
Backward-pass share-0.400.18
Deep-pass share-0.060.28
Completion+0.160.81
Seconds between passes-0.143.24
Side used

left 25% · central 49% · right 26%

Progression (middle third) 11,260 passes

narrow, central, played inside

Width of use-0.9323.59
Wide-channel share-0.890.34
Side bias-0.22-0.81
Pass length+0.2017.51
Long-ball share-0.190.09
Forward gain per pass+0.083.72
Backward-pass share-0.280.32
Deep-pass share-0.050.16
Completion+0.150.85
Seconds between passes-0.333.05
Side used

left 32% · central 34% · right 34%

Final third 5,666 passes

nothing unusual in this zone

Width of use-0.6330.06
Wide-channel share-0.450.55
Side bias-0.44-3.56
Pass length-0.0715.36
Long-ball share-0.210.09
Forward gain per pass-0.031.00
Backward-pass share+0.130.40
Deep-pass share-0.230.08
Completion+0.210.71
Seconds between passes+0.023.41
Side used

left 35% · central 20% · right 44%

Reference measured across roughly 3,500 match-teams in this competition, per third, so the comparison is with Borussia Dortmund'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 Borussia Dortmund 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%
#10 of 21 · reaches final third in ≤3 passes
Patient build-up share30%
#6 of 21 · spells of 6+ passes
Tempo (possession length)18.2s
#15 of 21 · shorter = faster tempo
Passes per possession4.9
#4 of 21
Counter-attack shots /game0.98
#16 of 21 · shot within 12s of a regain in own half
Press→shot /game3.17
#14 of 21 · shot within 8s of a high regain
Speed to final third after regain11.3s
#10 of 21 · lower = faster break

Sequence success — attacking transition

137attacking transition sequences this season
15%succeed — produce a shot
47%turned over with nothing to show

Sequence success — defensive transition

446defensive transition sequences this season
68%succeed — reach the next third or a shot
9%turned over with nothing to show
Vision AI Model Insight
Borussia Dortmund are a patient build-up side: 18% of possessions reach the final third in 3 passes or fewer (#10 of 21), against 30% built patiently over 6+ passes (#6 of 21). Winning the ball back leads to a shot inside 12 seconds 0.98 times a game (#16), and a high regain converts to a shot within 8 seconds 3.17 times a game (#14 of 21) — not yet a major source of chances.
Defending the box2 sections

Vulnerability Map — how opponents hurt them

The report flipped: where do opponents create their threat against Borussia Dortmund? 1623 opponent progressive passes and 440 shots faced (46.5 xG against) across the season.

Opponent threat by lane (attacking into Borussia Dortmund)

Right wing
28%
Right half-space
13%
Central
18%
Left half-space
13%
Left wing
28%
Where the danger came from. Opponents generated most of their threat through the right wing (28% of the danger conceded).

How Do They Concede?

The question
Where do the goals against Borussia Dortmund actually come from — open play, dead balls, and who supplies them?

Every goal Borussia Dortmund conceded across the loaded matches — the opponent's shots in Borussia Dortmund'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 (332 total)

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By situation

Open play
30 · 79%
Set pieces
3 · 8%
Penalties
5 · 13%
Vision AI Model Insight
38 goals conceded. 79% come in open play; set pieces account for only 8%, so dead-ball defending is not the main issue. 16% are headers — aerial defending is not where the damage comes from. Their supply line runs mainly through the left 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.

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
60 g · 47.8 xG
Set pieces
14 g · 12.0 xG
Penalties
6 g
18% of goals come from set pieces (14 of 80), worth 12.0 xG from 107 shots. Set pieces are a secondary threat; the danger is in open play.

Where do Borussia Dortmund's corners go?

223 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 (109)

6%6%17%6%6%13%28%17%2%
Delivered into the box 100%Short 0%

Left side (114)

4%5%6%7%13%42%18%4%
Delivered into the box 100%Short 0%

Where opponents attack Borussia Dortmund from set pieces

66 shots from opponent corners against Borussia Dortmund, 3 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)

12%9%16%22%22%19%
Shot 32Goals 1

Left flag (34)

6%12%29%24%12%18%
Shot 34Goals 2

Tactical Strengths & Weaknesses

Strengths (top-third vs league)
  • xG conceded /game — 1.13 (90th pct)
  • field tilt % — 58.53 (90th pct)
  • progression /game — 54.32 (86th pct)
  • threat created /game — 12.86 (86th pct)
  • box entries /game — 17.00 (81th 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.

Bayern
90%
Stuttgart
83%
Leverkusen
79%
Hoffenheim
78%
RBL
76%

Performance vs Opponent Styles

The question
Not just how Borussia Dortmund 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 Borussia Dortmund's own passing behaviour to the result.

Results

opponent typegoals for/ggoals against/gpoints/g
vs high-pressing opponents (13m)1.770.852.15
vs mid-block opponents (13m)2.311.232.15
vs low-block opponents (15m)1.930.732.27

Behaviour — how Borussia Dortmund actually play against each

opponent typeretentiondirectness (long balls)prog. passes/gxT/gbox entries/g
vs high-pressing opponents75%17%4610.514.5
vs mid-block opponents82%11%5613.317.4
vs low-block opponents85%9%6014.418.9
Vision AI Model Insight
Facing a high press, retention drops to 75% vs 85% against a low block (10pt lower under pressure), and directness (long-ball share) sits at 17% when pressed vs 9% 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. Borussia Dortmund are possession-dominant with a aggressive high-press. Their attack creates 1.63 xG a game and they concede 1.13 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 Borussia Dortmund’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.

Borussia Dortmund
across 41 matches this season
Expected goalsvs Hamburg · 3-2 · 2026-03-215.33avg 1.63
Final-third entriesat Heidenheim · 2-0 · 2025-09-13109avg 52
Progressive passesat Heidenheim · 2-0 · 2025-09-1395avg 54
Progressive carriesat Heidenheim · 2-0 · 2025-09-13101avg 49
Defensive actionsat Hoffenheim · 3-2 · 2026-09-05204avg 139
Threat from passingvs Koln · 1-0 · 2025-10-2511.88avg 7.09
Field tiltat Stuttgart · 2-0 · 2026-04-0420%avg 59%
Passesat Heidenheim · 2-0 · 2025-09-13834avg 543

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 Borussia Dortmund’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).

+21.6on-ball value, 12 matches
Where the value came from
Passes6734 actions+9.95
Carries1479 actions-1.06
Dribbles185 actions+0.88
Shots167 actions+7.52
Ball wins753 actions+4.27
Most on-ball value (OBV)ActionsOBVper 100
Serhou Guirassy363+2.35+0.65
Julian Ryerson770+1.98+0.26
Felix Nmecha468+1.95+0.42
Maximilian Beier501+1.89+0.38
Nico Schlotterbeck1042+1.73+0.17
Waldemar Anton1258+1.50+0.12
Daniel Svensson606+1.44+0.24
Joey Veerman322+1.42+0.44
123456
Biggest single movesOBV
1  Serhou Guirassy · shot+0.460
2  Maximilian Beier · pass+0.433
3  Julian Ryerson · pass+0.420
4  Maximilian Beier · shot+0.395
5  Felix Nmecha · take-on+0.357
6  Fábio Silva · shot+0.328

Set Pieces by Player

Who delivers Borussia Dortmund’s dead balls and who finishes them this season. 223 corners taken, most by Julian Ryerson (88); 107 shots from corners and free kicks, 14 goals, led by Nico Schlotterbeck (4 goals, 2.75 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
Julian Ryerson8882 (93%)4—0.18——
Daniel Svensson7065 (93%)2—0.07——
Serhou Guirassy10 (0%)2023.6511/3
Nico Schlotterbeck66 (100%)2142.75——
Julian Brandt32 (67%)1010.424—
Ramy Bensebaini——921.41—2/2
Maximilian Beier20 (0%)810.401—
Waldemar Anton——711.67——
Joey Veerman1212 (100%)2—0.161—
Marcel Sabitzer64 (67%)4—0.123—
Felix Nmecha——510.2510/1
Pascal Groß127 (58%)1—0.03——
Yan Couto1511 (73%)——0.00——
Karim Adeyemi——4—0.14——

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.

playerposminVision AI Scorecapability blocks
Waldemar AntonCB37277.5Ball-Carrying 79%Progression 66%Ball-Winning 62%Aerial & Box Defending 22%
Nico SchlotterbeckCB31417.4Progression 84%Ball-Carrying 62%Ball-Winning 53%Aerial & Box Defending 12%
Gregor KobelGK39216.5Shot-Stopping 65%
Felix NmechaCM27775.8Successful Dribble 75%Ball-Carrying 69%Progression 51%Chance Creation 30%
Joane GadouFB5375.7Ball-Carrying 92%Ball-Winning 50%Wide Delivery 0%
Serhou GuirassyST30655.7Box Threat 64%Ball-Carrying 54%Finishing 36%Aerial Threat 0%
Daniel SvenssonCM32505.4Progression 55%Successful Dribble 40%Chance Creation 40%Ball-Carrying 39%
Maximilian BeierAM25725.0Box Threat 76%Ball-Carrying 15%Chance Creation 10%
Jobe BellinghamCM24604.8Ball-Carrying 58%Successful Dribble 53%Progression 52%Chance Creation 28%
Julian RyersonW28983.9Chance Creation 46%Wide Delivery 31%Ball-Carrying 31%Box Threat 0%

Top Creators

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

playerposminkey P /90box entriesxT
Julian RyersonM28982.0812358.5
Daniel SvenssonM32501.087236.1
Maximilian BeierM25721.155317.4
Felix NmechaM27770.845028.5
Julian BrandtM17141.524318.7
Serhou GuirassyF30650.88387.5
Nico SchlotterbeckD31410.404172.6
Fábio SilvaF11901.29317.7

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)
Julian RyersonW289864 (41%)58 (24%)

Defensive Volume Leaders

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

playerposmindef actionstacklesinterceptrecoveries
Waldemar AntonD37277608649188
Nico SchlotterbeckD31415856633164
Daniel SvenssonM32504505429123
Jobe BellinghamM24603865748140
Felix NmechaM27773714834131
Julian RyersonM28983653721148
Serhou GuirassyF30653482066
Ramy BensebainiD1762337312184

Full Squad Data

Every player with 100+ minutes (28 players).

playerposappsmingoalsxGkey PprogdefxT
Gregor KobelG41392100.0015912840.2
Waldemar AntonD39372722.31116076055.0
Daniel SvenssonM40325032.03919545036.1
Nico SchlotterbeckD33314163.91428558572.6
Serhou GuirassyF4030651918.530243487.5
Julian RyersonM37289800.96729036558.5
Felix NmechaM36277744.3269837128.5
Maximilian BeierM392572118.03310528717.4
Jobe BellinghamM39246001.5157438622.7
Marcel SabitzerM32214011.5177424220.4
Ramy BensebainiD24176253.61112933735.5
Julian BrandtM29171473.82910315318.7
Karim AdeyemiM28126673.61428975.9
Fábio SilvaF34119043.217321217.7
Carney ChukwuemekaM30109222.014421379.7
Yan CoutoM19107820.71710713018.1
Emre CanD966932.505715111.5
Pascal GroßM1164200.611518012.7
Luca ReggianiD856110.3232816.5
Joane GadouD753700.1044938.7
Niklas SüleD1151800.1232678.4
Samuele InácioM843411.1814452.7
Aaron AnselminoD639310.1019784.0
Ethan NwaneriM633800.6613392.5
Joey VeermanM631500.3723676.8
Konstantinos KaretsasF520000.6412322.9
Filippo ManeD520000.0016333.4
Salih ÖzcanM1113400.224171.5

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.

Gregor Kobel
Goalkeeper · Goalkeeper
minutes3921
goals · xG0 · 0.0
prog passes /903.6
key passes /900.00
def actions /902.9
pass accuracy74%
Waldemar Anton
Stopper / marker · Defender
minutes3727
goals · xG2 · 2.3
prog passes /903.9
key passes /900.27
def actions /9018.4
pass accuracy90%
Daniel Svensson
Ball-winner / destroyer · Midfielder
minutes3250
goals · xG3 · 2.0
prog passes /905.4
key passes /901.08
def actions /9012.5
pass accuracy77%
Nico Schlotterbeck
Ball-playing defender · Defender
minutes3141
goals · xG6 · 3.9
prog passes /908.2
key passes /900.40
def actions /9016.8
pass accuracy86%
Serhou Guirassy
Poacher / finisher · Forward
minutes3065
goals · xG19 · 18.5
prog passes /900.7
key passes /900.88
def actions /9010.2
pass accuracy75%
Julian Ryerson
Advanced creator · Attacking mid
minutes2898
goals · xG0 · 0.9
prog passes /909.0
key passes /902.08
def actions /9011.3
pass accuracy72%
Felix Nmecha
Ball-winner / destroyer · Midfielder
minutes2777
goals · xG4 · 4.3
prog passes /903.2
key passes /900.84
def actions /9012.0
pass accuracy87%
Maximilian Beier
Poacher / finisher · Forward
minutes2572
goals · xG11 · 8.0
prog passes /903.7
key passes /901.15
def actions /9010.0
pass accuracy69%
Jobe Bellingham
Ball-winner / destroyer · Midfielder
minutes2460
goals · xG0 · 1.5
prog passes /902.7
key passes /900.55
def actions /9014.1
pass accuracy86%
Vision AI · building report