Combining every season — this one is larger, give it a moment.
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.
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.
#
team
P
W
D
L
GF
GA
GD
Pts
1
Bayern
41
33
7
1
147
42
+105
106
2
Borussia Dortmund
41
27
9
5
82
38
+44
90
3
RBL
41
24
5
12
86
58
+28
77
4
Leverkusen
41
21
9
11
84
59
+25
72
5
Hoffenheim
41
21
7
13
79
66
+13
70
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)
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.
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).
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.
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.
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.
Phase
Share of the time
Build-up
21.0%
Progression
50.3%
Chance creation
26.6%
Attacking transition
2.0%
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.
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
Carries
the ball moved with a player
5033
ACTIONS
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.
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.
Channel
Entries
Per match
Share
League share
Left wing
499
12.2
24%
27%
Left half-space
322
7.9
15%
16%
Centre
348
8.5
16%
14%
Right half-space
291
7.1
14%
15%
Right wing
658
16.0
31%
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%
Long Wide238 · 6%
Long Central61 · 1%
Wide → FB1382 · 32%
Central514 · 12%
◆ 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.
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. 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%
Long Wide245 · 3%
Long Central37 · 0%
Wide → FB3703 · 38%
Central1336 · 14%
◆ 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.
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
Kind of attack, then how the shot was set up
Shots
Goals
xG
xG / shot
Attacking transition25% of shots
139
19
16.26
0.12
Cross
10
3
1.45
0.15
Cut-back
25
4
3.25
0.13
Long ball
11
2
2.14
0.19
Pass into the box / short pass
69
7
7.21
0.10
Own creation (carry, dribble, rebound)
24
3
2.21
0.09
Settled attack54% of shots
296
41
31.50
0.11
Cross
57
13
8.25
0.14
Cut-back
45
4
5.97
0.13
Long ball
5
2
0.26
0.05
Pass into the box / short pass
123
18
11.37
0.09
Own creation (carry, dribble, rebound)
66
4
5.65
0.09
Set piece21% of shots
116
20
19.10
0.16
Corner
95
14
11.58
0.12
Free kick
12
0
0.37
0.03
Penalty
9
6
7.15
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
Julian Ryerson
41
6.15
9
Julian Brandt
29
4.03
4
Maximilian Beier
30
3.43
7
Serhou Guirassy
33
3.41
4
Yan Couto
17
2.38
2
Who takes them
Player
Shots
xG
Goals
Serhou Guirassy
107
18.53
19
Maximilian Beier
67
7.99
11
Felix Nmecha
43
4.31
4
Nico Schlotterbeck
37
3.92
6
Julian Brandt
37
3.75
7
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%
Long Wide13 · 3%
Long Central47 · 12%
Wide → FB94 · 24%
Central205 · 51%
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.
Territory
Crosses
Share
Led to a shot
byline · right wing
221
35%
35
advanced · right wing
126
20%
14
byline · left wing
107
17%
8
advanced · left wing
100
16%
10
deep · right wing
31
5%
1
deep · left wing
17
3%
0
byline · left half-space
12
2%
2
byline · right half-space
11
2%
0
advanced · right half-space
4
1%
1
advanced · left half-space
4
1%
1
deep · left half-space
1
0%
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
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.
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
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#6/21
lower = more intense; rank is what matters
Defensive actions / game
139
#18/21
High-press share
24%
#4/21
def actions won high up
Defensive-action height (0–100)
39.6
#2/21
avg pitch height of actions
High recoveries / game
26.0
#3/21
ball won in mid+att third
High-recovery share
60%
#2/21
share of recoveries won high
Opponent completion allowed
80%
#8/21
lower = more disruptive
Opponent progressive allowed
8.4%
#2/21
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 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
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
Phase
Segments
Avg length
Avg actions
Ends in shot
Turned over
Build-up
1,896
4.4s
2.9
0.1%
46.1%
Progression
2,847
7.1s
4.3
0.5%
46.1%
Chance creation
1,775
6.0s
3.5
22.8%
53.5%
Attacking transition
119
6.6s
4.3
2.5%
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.
Route
Times
Ends in shot
progression
734
0.0%
build_up
721
0.0%
chance_creation
403
34.2%
progression → chance_creation
365
22.7%
build_up → progression
320
0.0%
build_up → progression → chance_creation
183
23.5%
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
Every opponent key pass against them (332 total)
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.
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
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)
Delivered into the box 100%Short 0%
Left side (114)
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)
Shot 32Goals 1
Left flag (34)
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 type
goals for/g
goals against/g
points/g
vs high-pressing opponents (13m)
1.77
0.85
2.15
vs mid-block opponents (13m)
2.31
1.23
2.15
vs low-block opponents (15m)
1.93
0.73
2.27
Behaviour — how Borussia Dortmund actually play against each
opponent type
retention
directness (long balls)
prog. passes/g
xT/g
box entries/g
vs high-pressing opponents
75%
17%
46
10.5
14.5
vs mid-block opponents
82%
11%
56
13.3
17.4
vs low-block opponents
85%
9%
60
14.4
18.9
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.]
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.
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)
Actions
OBV
per 100
Serhou Guirassy
363
+2.35
+0.65
Julian Ryerson
770
+1.98
+0.26
Felix Nmecha
468
+1.95
+0.42
Maximilian Beier
501
+1.89
+0.38
Nico Schlotterbeck
1042
+1.73
+0.17
Waldemar Anton
1258
+1.50
+0.12
Daniel Svensson
606
+1.44
+0.24
Joey Veerman
322
+1.42
+0.44
Biggest single moves
OBV
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.
Player
Corners taken
Into the box
Set-piece shots
Goals
xG
Free-kick shots
Penalties
Julian Ryerson
88
82 (93%)
4
—
0.18
—
—
Daniel Svensson
70
65 (93%)
2
—
0.07
—
—
Serhou Guirassy
1
0 (0%)
20
2
3.65
1
1/3
Nico Schlotterbeck
6
6 (100%)
21
4
2.75
—
—
Julian Brandt
3
2 (67%)
10
1
0.42
4
—
Ramy Bensebaini
—
—
9
2
1.41
—
2/2
Maximilian Beier
2
0 (0%)
8
1
0.40
1
—
Waldemar Anton
—
—
7
1
1.67
—
—
Joey Veerman
12
12 (100%)
2
—
0.16
1
—
Marcel Sabitzer
6
4 (67%)
4
—
0.12
3
—
Felix Nmecha
—
—
5
1
0.25
1
0/1
Pascal Groß
12
7 (58%)
1
—
0.03
—
—
Yan Couto
15
11 (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.