Combining every season — this one is larger, give it a moment.
Team reportBundesliga 2026
Leverkusen
A possession-dominant side, aggressive high-press out of possession, with a high-volume attack. Currently #4 of 18 on 13 points (4-1-2, 16:12). Tactical profile below is built from 7 matches of ball-by-ball event data, contextualised against the league.
In one read, what kind of team is Leverkusen and how good was the season?
Read. Leverkusen currently sit #4 of 18 on 13 points (4W-1D-2L, 1.86 pts/game), scoring 16 and conceding 12 for a goal difference of +4.
On the ball they are a possession-dominant team (57% field tilt, #4 in the league); off it they defend with a aggressive high-press (#9 of 18 for press intensity).
Across the 7 matches of event data, they create 1.75 xG/game (#6) and concede 1.46 (#6), a net of +0.30 xG/game — the underlying performance matches the actual goal difference.
Season Context — the league table
The question
Where did Leverkusen actually finish, and against whom?
The authoritative final table (full season), centred on Leverkusen.
#
team
P
W
D
L
GF
GA
GD
Pts
1
Bayern
7
5
2
0
25
6
+19
17
2
Borussia Dortmund
7
5
2
0
12
4
+8
17
3
Freiburg
7
4
1
2
16
9
+7
13
4
Leverkusen
7
4
1
2
16
12
+4
13
5
RBL
7
4
0
3
20
11
+9
12
6
Werder Bremen
7
3
3
1
15
14
+1
12
7
Mainz
7
3
1
3
16
13
+3
10
Match Momentum — when they create and concede danger
The question
Across a whole game, which periods does Leverkusen 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
Leverkusen tend to come alive after the break — first-half net xG -0.09, second-half +2.17. Their strongest period is 61-75' (net +2.35 xG); their most dangerous period to face is 46-60' (net -0.61 xG against them).
xG Race — the season, match by match
Expected goals piling up over 7 matches: Leverkusen 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 (+2.1 xG). Hover a point for that match.
LeverkusenOpponents against Leverkusen
Tactical DNA
The football question
In five genes, what is Leverkusen'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.
81
Build-Up
possession control from the back
64
Progression
moving the ball upfield with threat
78
Creation
manufacturing chances
64
Press
winning the ball high & hard
67
Solidity
resisting the opponent's threat
Genetic identity
Leverkusen are defined most by their Build-Up DNA (81/100 · strong at possession control from the back), and least by their Progression DNA (64/100). Percentiles vs the 18 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
76%
Possession
90%
Field tilt
81%
Disrupt
Central progression ~proxy
90%
Dribble tendency
10%
Progressive passes
71%
Finish
Final-third patience
81%
Shot quality
62%
Open-play xG
86%
Press
Forward press ~proxy
48%
High regains
67%
Pressing efficiency ~proxy
57%
Defend
Def-mid press ~proxy
52%
High line ~proxy
52%
PAdj defensive actions
43%
Leverkusen is defined most by Possess (83% in the league) and weakest on Defend (49%). 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 Leverkusen actually control, relative to the rest of the league?
Field tilt = share of final-third touches between the two sides across Leverkusen'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; Leverkusen highlighted. Hover any dot for the team.
Vision AI Model Insight
Leverkusen post 57% field tilt, ranking #4 of 18 in the league — comfortably above the league mid-point for territorial control, and their 1.86 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 Leverkusen has the ball, how much of that time is spent building, progressing and creating?
The share of Leverkusen's time on the ball spent in each phase of play, in the order the phases happen. A phase is where the ball is on the pitch (own third, middle third, final third); a transition or a set piece skips the build-up and is counted on its own.
Phase
Share of the time
Build-up
24.0%
Progression
48.2%
Chance creation
24.5%
Attacking transition
3.3%
Vision AI Model Insight
Progression is where Leverkusen spends most of its time on the ball (48% 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
García ↔ Medina
165p · 0.3xT
García ↔ Tapsoba
129p · 0.1xT
García ↔ Gutiérrez
127p · 0.7xT
Medina ↔ Gutiérrez
116p · -0.4xT
Tapsoba ↔ Quansah
116p · -0.8xT
Medina ↔ Tapsoba
110p · -0.1xT
García ↔ Fernández
105p · 1.0xT
Flekken ↔ Medina
85p · -0.2xT
García ↔ Quansah
80p · -0.1xT
García ↔ Maza
63p · 1.1xT
Vision AI Model Insight
Busiest connection: Aleix García ↔ Facundo Medina (165 passes). Most dangerous per pass: Miguel Gutiérrez ↔ Ibrahim Maza (0.04 xT/pass over 44 passes) — the combination that hurts opponents most when it fires, not just the one used most.
Touch Density — where Leverkusen had the ball
Every touch across 63 matches. Brighter means more touches.
Which Zones Do They Attack Through?
The question
Across the whole pitch, where does Leverkusen 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
4068
ACTIONS
Carries
the ball moved with a player
1044
ACTIONS
Vision AI Model Insight
Their single busiest zone is the centre of the middle third (13.1% of all actions). 63% of actions run through the inside lanes (centre + both half-spaces) versus 37% down the two wings — an inside-dominant, half-space build. By third: 26% defensive, 51% middle, 23% 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. Leverkusen leaned on their left (41%). The bars under the pitch put the league’s share beside theirs.
Leverkusen
41%26%33%
League
39%25%36%
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. Leverkusen made 379 this season (54.1 a match); 26% 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
100
14.3
26%
28%
Left half-space
63
9.0
17%
17%
Centre
66
9.4
17%
14%
Right half-space
50
7.1
13%
15%
Right wing
100
14.3
26%
26%
Build-up2 sections
Build-Up — the pass map
The football question
How does Leverkusen 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.
1054build-up passes attempted
83%completed
28%reach the middle third
2%go long into the final third
Pass types
352CB · 40%
52Long Wide · 6%
12Long Central · 1%
346Wide → FB · 40%
112Central · 13%
Sequence success — build-up
55build-up sequences this season
18%succeed — reach the next third or a shot
40%turned over with nothing to show
CB352 · 40%
Long Wide52 · 6%
Long Central12 · 1%
Wide → FB346 · 40%
Central112 · 13%
◆ Vision AI Insight
Leverkusen build predominantly down the left, with Flekken as the fulcrum (182 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 (51×).
Pass Explorer
Every pass of Leverkusen’s last 7 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. 338 progressive passes across 7 matches (48.3/game).
Origin lane
Right wing
27%
Right half-space
13%
Central
13%
Left half-space
15%
Left wing
33%
Progression is left-dominant: 39% right side · 13% central · 48% left.
Who generates it
Aleix García
62 · 18%
Miguel Gutiérrez
57 · 17%
Facundo Medina
43 · 13%
Ibrahim Maza
33 · 10%
Jarell Quansah
27 · 8%
Top two — Aleix García & Miguel Gutiérrez — account for 35% of all progression.
PROGRESSION
48.3 prog passes/game
mostly left
→
PENETRATION
29% reach final third
26% reach the box
→
THREAT
the other 71% is recycled
progress ≠ danger
What came of it.29% of progressive passes reached the final third and 26% the box; the rest was recycled in front of the block. 35% 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 Leverkusen 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.
2052progression passes attempted
91%completed
15%break into the final third
10%go backward into the defensive third
Pass types
552CB · 30%
68Long Wide · 4%
12Long Central · 1%
908Wide → FB · 49%
321Central · 17%
Sequence success — progression
87progression sequences this season
30%succeed — reach the next third or a shot
33%turned over with nothing to show
CB552 · 30%
Long Wide68 · 4%
Long Central12 · 1%
Wide → FB908 · 49%
Central321 · 17%
◆ Vision AI Insight
Leverkusen progress predominantly down the left, with García the busiest midfield outlet (388 progression passes). Only 15% of passes that start in midfield actually break the final-third line — the rest (75%) 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 LW mid → LHS mid (105×) — 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)
12%
Progressive
1%
Neutral / sideways
49%
Safe / backward
38%
14% of passes are progressive-or-better; the remaining 86% keep possession without advancing it. A control-first side — safe in build-up, reliant on a few players for the incision.
Where does Leverkusen actually progress the ball with their feet, not a pass?
247 progressive carries this season across the whole squad (25% of 977 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 59m. Most active carrier: Gutiérrez (29). Filter to one player below.
First (82)Second (69)Third (54)Fourth (42)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 Leverkusen'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.
127shots
12.3xG
0.10xG per shot
3big chances (0.3+ xG)
16goals
#6key 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 transition20% of shots
25
4
2.77
0.11
Cross
3
2
0.47
0.16
Cut-back
5
0
0.87
0.17
Long ball
1
0
0.12
0.12
Pass into the box / short pass
6
1
0.54
0.09
Own creation (carry, dribble, rebound)
10
1
0.77
0.08
Settled attack59% of shots
75
10
7.46
0.10
Cross
8
0
1.04
0.13
Cut-back
13
2
1.42
0.11
Long ball
4
1
0.24
0.06
Pass into the box / short pass
31
4
3.08
0.10
Own creation (carry, dribble, rebound)
19
3
1.68
0.09
Set piece21% of shots
27
2
2.04
0.08
Corner
26
2
2.01
0.08
Free kick
1
0
0.02
0.02
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
Ibrahim Maza
17
2.02
1
Miguel Gutiérrez
12
1.66
3
Moussa Diaby
6
0.79
0
Patrik Schick
7
0.71
2
Jarell Quansah
4
0.54
1
Who takes them
Player
Shots
xG
Goals
Patrik Schick
25
3.52
6
Ibrahim Maza
22
2.12
2
Christian Kofane
8
1.00
2
Equi Fernández
5
0.84
1
Moussa Diaby
11
0.76
1
Vision AI Model Insight
The biggest source of Leverkusen's chances, by xG, is settled attack (7.5 of 12.3 xG, 75 of 127 shots), mostly pass into the box / short pass. 25 of the 127 shots (20%) came within 15 seconds of winning the ball. 27 of the 127 shots came from set pieces; 16 goals from 12.3 xG (+3.7).
Chance Creation — the pass map
The football question
How does Leverkusen 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
4CB · 5%
5Long Wide · 6%
13Long Central · 15%
21Wide → FB · 24%
43Central · 50%
Sequence success — final-third
98final-third sequences this season
35%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
CB4 · 5%
Long Wide5 · 6%
Long Central13 · 15%
Wide → FB21 · 24%
Central43 · 50%
Who creates — volume × threat
17García
16Maza
12Gutiérrez
7Schick
6Quansah
6Fernández
6Diaby
86key passes #6 of 18
19.4box entries/game #3 of 18
42.3final-3rd entries/game #3 of 18
1.75xG/game #6 of 18
16goals #4 of 18
+3.7finishing vs xG #5 of 18
◆ Vision AI Insight
Leverkusen lean left — 42% of their key passes come from that flank and its half-space (36 of 86), most often from Gutiérrez (11). They funnel chances into the c of the final third, most often via RW att → C att (12 key passes, most often from García). García is the primary source overall. They generate 1.75 xG a game and finish it clinical — they beat their xG, led by Patrik Schick (6G), Ibrahim Maza (2G), Christian Kofane (2G).
Crosses & Cutbacks — squad-wide
The question
How does Leverkusen deliver into the box from wide areas, and what comes of it?
128 crosses: 40 found a team-mate (31%), 11 led to a shot (9%). 42 cut-backs: 29 found a team-mate (69%), 19 led to a shot (45%). 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 Leverkusen actually cross from?
128 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. 27% come from one territory, the advanced left wing; 5 come from the half-spaces and 0 from the centre.
Territory
Crosses
Share
Led to a shot
advanced · left wing
35
27%
3
advanced · right wing
30
23%
1
byline · left wing
25
20%
5
byline · right wing
21
16%
1
deep · left wing
7
5%
1
deep · right wing
5
4%
0
advanced · left half-space
2
2%
0
byline · right half-space
2
2%
0
byline · left half-space
1
1%
0
Finishing3 sections
Shot Map
127 shots for 12.3 xG (1.75/game, #6 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.
High-volume attack. 18.1 shots/game at 0.097 xG/shot. Box entries 19/game (#3). Threat created 12.6 xT/game (#6).
Goal Sequences
The move behind each of Leverkusen’s 16 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 — 16 goals traced.
How Do They Score?
The question
Where do the goals actually come from — open play, dead balls, which foot?
Every goal Leverkusen scored across the loaded matches, broken down by how it was created and finished.
By situation
Open play
14 · 88%
Set pieces
2 · 12%
Penalties
0 · 0%
By finish
Right foot
8 · 50%
Left foot
6 · 38%
Header
2 · 12%
Vision AI Model Insight
16 goals scored. 88% come in open play; set pieces add 12%. 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
275
Failed forward pass
192
Failed take-on
62
Lost in the defensive third
180
Their weakest zone is the right half-space in the attacking third: 31% of 154 pass attempts there are lost (48 times). Separately, 43% 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 Leverkusen 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.
45
Pressing Index /100 · average
Intensity37
PPDA, high-press share, high-zone actions
Height50
how high up the pitch they defend
Effectiveness58
high recoveries & high-regain share
Disruption31
opponent completion & progression allowed
Every defensive action & where it happens
Where they win the ball back — by lane
RW
17%
RHS
20%
C
22%
LHS
23%
LW
18%
By third (high / mid / low regain)
Def 47%
Mid 38%
Att 15%
Read
Ranked #9 of 18 for press intensity, this is a high press. 53% of defensive actions happen in the middle & attacking thirds, at an average height of 39. Of recoveries, 54% are won high up the pitch.
Every pressing metric, ranked in the league
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#9/18
lower = more intense; rank is what matters
Defensive actions / game
145
#17/18
High-press share
23%
#10/18
def actions won high up
Defensive-action height (0–100)
38.7
#9/18
avg pitch height of actions
High recoveries / game
24.3
#7/18
ball won in mid+att third
High-recovery share
54%
#8/18
share of recoveries won high
Opponent completion allowed
80%
#10/18
lower = more disruptive
Opponent progressive allowed
10.4%
#15/18
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 Leverkusen win it back with a defensive action?
Counted over the 251 opposition possessions that Leverkusen 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 Leverkusen step in. Leverkusen win it after 4.2 opponent passes in the first half-hour and after 4.4 after the hour mark (about the same).
PASSES THEY GET
4.3passes
median 3 · before Leverkusen win it
TIME THEY KEEP IT
12sec
median 7s · 35.9 wins per game
How many passes the opponent got in before the ball was won
42%
31%
18%
9%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
4.0
10s
15–30′
4.4
14s
30–45+′
4.4
13s
45–60′
3.9
11s
60–75′
5.0
13s
75–90+′
4.1
10s
average passesaverage seconds
Attitude in each third
Every other style number here says how much Leverkusen 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 Leverkusen are genuinely unusual, and a verdict is only written where something clears
0.85 sd — a side that is ordinary in a third is told so rather than given a label
for average play.
Build-up (own third) 1,054 passes
nothing unusual in this zone
Width of use-0.0620.88
Wide-channel share+0.040.31
Side bias+0.090.49
Pass length-0.6720.28
Long-ball share-0.360.19
Forward gain per pass-0.529.19
Backward-pass share+0.400.22
Deep-pass share-0.470.24
Completion+0.340.82
Seconds between passes-0.543.00
Side used
left 27% · central 47% · right 26%
Progression (middle third) 2,052 passes
narrow, central, played inside, safer
Width of use-1.0823.29
Wide-channel share-1.050.33
Side bias+0.401.64
Pass length-0.7116.24
Long-ball share-0.610.08
Forward gain per pass-0.642.64
Backward-pass share+0.320.35
Deep-pass share-0.800.12
Completion+0.970.90
Seconds between passes-0.443.00
Side used
left 35% · central 35% · right 30%
Final third 962 passes
nothing unusual in this zone
Width of use-0.5030.38
Wide-channel share-0.260.56
Side bias+0.291.55
Pass length-0.1915.18
Long-ball share-0.080.10
Forward gain per pass-0.060.96
Backward-pass share+0.070.39
Deep-pass share+0.110.09
Completion+0.020.70
Seconds between passes-0.533.00
Side used
left 43% · central 19% · right 38%
Reference measured across roughly 3,500 match-teams
in this competition, per third, so the comparison is with Leverkusen'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 Leverkusen 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 share15%
#18 of 18 · reaches final third in ≤3 passes
Patient build-up share38%
#2 of 18 · spells of 6+ passes
Tempo (possession length)22.4s
#17 of 18 · shorter = faster tempo
Passes per possession6.2
#2 of 18
Counter-attack shots /game1.15
#8 of 18 · shot within 12s of a regain in own half
Press→shot /game3.29
#10 of 18 · shot within 8s of a high regain
Speed to final third after regain14.5s
#18 of 18 · lower = faster break
Sequence success — attacking transition
13attacking transition sequences this season
15%succeed — produce a shot
54%turned over with nothing to show
Sequence success — defensive transition
12defensive transition sequences this season
75%succeed — reach the next third or a shot
0%turned over with nothing to show
Vision AI Model Insight
Leverkusen are a patient build-up side: 15% of possessions reach the final third in 3 passes or fewer (#18 of 18), against 38% built patiently over 6+ passes (#2 of 18). Winning the ball back leads to a shot inside 12 seconds 1.15 times a game (#8), and a high regain converts to a shot within 8 seconds 3.29 times a game (#10 of 18) — 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 Leverkusen? 311 opponent progressive passes and 90 shots faced (10.2 xG against) across the season.
Opponent threat by lane (attacking into Leverkusen)
Right wing
29%
Right half-space
13%
Central
16%
Left half-space
12%
Left wing
30%
Where the danger came from. Opponents generated most of their threat through the left wing (30% of the danger conceded).
How Do They Concede?
The question
Where do the goals against Leverkusen actually come from — open play, dead balls, and who supplies them?
Every goal Leverkusen conceded across the loaded matches — the opponent's shots in Leverkusen'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 (65 total)
By situation
Open play
9 · 75%
Set pieces
2 · 17%
Penalties
1 · 8%
Vision AI Model Insight
12 goals conceded. 75% come in open play; set pieces account for only 17%, so dead-ball defending is not the main issue. 8% are headers — aerial defending is not where the damage comes from. Their supply line runs mainly through the right wing of the attacking third (20% 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
14 g · 10.2 xG
Set pieces
2 g · 2.0 xG
Penalties
0 g
12% of goals come from set pieces (2 of 16), worth 2.0 xG from 27 shots. Set pieces are a secondary threat; the danger is in open play.
Where do Leverkusen's corners go?
47 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 (29)
Delivered into the box 93%Short 7%
Left side (18)
Delivered into the box 100%Short 0%
Where opponents attack Leverkusen from set pieces
14 shots from opponent corners against Leverkusen, 2 conceded, split by the flag the corner came from — arrows from the flag, dots where the shot was taken (gold = goal), shaded by the % of that flag's shots in each zone. Tap or hover a shot for who took it, the minute and the match; filter to one match and the shading recomputes for just that game.
Right flag (8)
Shot 8Goals 1
Left flag (6)
Shot 6Goals 1
Tactical Strengths & Weaknesses
Strengths (top-third vs league)
box entries /game — 19.43 (83th pct)
pass retention % — 84.12 (83th pct)
shots /game — 18.14 (83th pct)
field tilt % — 57.12 (78th pct)
xG created /game — 1.75 (67th 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
81%
RBL
77%
Borussia Dortmund
57%
Mainz
41%
Stuttgart
41%
Performance vs Opponent Styles
The question
Not just how Leverkusen 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 Leverkusen's own passing behaviour to the result.
Results
opponent type
goals for/g
goals against/g
points/g
vs high-pressing opponents (2m)
1.00
1.50
1.50
vs mid-block opponents (2m)
3.00
0.50
3.00
vs low-block opponents (3m)
2.67
2.67
1.33
Behaviour — how Leverkusen actually play against each
opponent type
retention
directness (long balls)
prog. passes/g
xT/g
box entries/g
vs high-pressing opponents
83%
13%
42
10.9
11.5
vs mid-block opponents
85%
12%
46
12.0
18.0
vs low-block opponents
84%
9%
54
14.3
25.7
Vision AI Model Insight
Facing a high press, retention drops to 83% vs 84% against a low block (2pt lower under pressure), and directness (long-ball share) sits at 13% when pressed vs 9% against a low block — a mild adjustment, not a wholesale change.
Scouting Summary — the team in brief
In brief. Leverkusen are possession-dominant with a aggressive high-press.
Their attack creates 1.75 xG a game and they concede 1.46 a game.
[Event-data based — no tracking, so off-ball structure and marking scheme need video.]
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 Leverkusen’s last 7 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).
+14.7on-ball value, 7 matches
Where the value came from
Passes4068 actions+5.47
Carries977 actions+1.09
Dribbles111 actions+0.39
Shots127 actions+5.70
Ball wins427 actions+2.00
Most on-ball value (OBV)
Actions
OBV
per 100
Ibrahim Maza
466
+2.85
+0.61
Patrik Schick
161
+2.58
+1.60
Miguel Gutiérrez
615
+2.00
+0.32
Facundo Medina
621
+1.06
+0.17
Moussa Diaby
237
+1.03
+0.44
Edmond Tapsoba
659
+0.84
+0.13
Equi Fernández
412
+0.83
+0.20
Afonso Moreira
269
+0.75
+0.28
Biggest single moves
OBV
1 Patrik Schick · shot
+0.403
2 Patrik Schick · shot
+0.398
3 Miguel Gutiérrez · pass
+0.379
4 Patrik Schick · shot
+0.354
5 Ibrahim Maza · pass
+0.331
6 Patrik Schick · shot
+0.298
Set Pieces by Player
Who delivers Leverkusen’s dead balls and who finishes them this season. 47 corners taken, most by Aleix García (39); 27 shots from corners and free kicks, 2 goals, led by Patrik Schick (1 goals, 0.65 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
Aleix García
39
32 (82%)
2
—
0.06
1
—
Ibrahim Maza
3
1 (33%)
5
—
0.25
—
—
Patrik Schick
—
—
5
1
0.65
—
—
Christian Kofane
—
—
3
—
0.25
—
—
Moussa Diaby
—
—
3
—
0.15
—
—
Facundo Medina
—
—
2
1
0.22
—
—
Jarell Quansah
—
—
2
—
0.15
—
—
Edmond Tapsoba
—
—
2
—
0.16
—
—
Miguel Gutiérrez
4
0 (0%)
—
—
0.00
—
—
Malik Tillman
—
—
1
—
0.07
—
—
Afonso Moreira
—
—
1
—
0.04
—
—
Victor Boniface
—
—
1
—
0.05
—
—
Jonas Hofmann
1
0 (0%)
—
—
0.00
—
—
Top Performers
Ranked by Vision AI Score — the same tactical behavioural evaluation each player's own report page shows (min 189 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
Aleix García
M
652
2.35
24
17.2
Ibrahim Maza
M
624
2.31
24
7.2
Miguel Gutiérrez
D
653
1.65
21
11.7
Moussa Diaby
M
397
1.36
15
3.3
Jarell Quansah
D
541
1.00
8
5.9
Afonso Moreira
M
512
0.53
10
3.5
Patrik Schick
F
545
1.16
6
0.9
Equi Fernández
M
438
1.23
5
5.3
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)
Miguel Gutiérrez
FB
653
25 (40%)
3 (67%)
Moussa Diaby
W
397
8 (38%)
17 (53%)
Jarell Quansah
FB
541
3 (67%)
0
Defensive Volume Leaders
Who does the defensive work — tackles, interceptions, recoveries.
player
pos
min
def actions
tackles
intercept
recoveries
Edmond Tapsoba
D
664
123
9
5
37
Facundo Medina
D
560
98
6
7
29
Equi Fernández
M
438
93
20
4
32
Aleix García
M
652
86
8
11
37
Miguel Gutiérrez
D
653
85
10
4
36
Jarell Quansah
D
541
79
9
1
22
Ibrahim Maza
M
624
78
9
4
25
Afonso Moreira
M
512
76
6
3
18
Full Squad Data
Every player with 100+ minutes (16 players).
player
pos
apps
min
goals
xG
key P
prog
def
xT
Mark Flekken
G
7
664
0
0.0
0
17
14
5.5
Edmond Tapsoba
D
7
664
0
0.2
2
20
123
9.2
Miguel Gutiérrez
D
7
653
1
0.5
12
57
85
11.7
Aleix García
M
7
652
0
0.3
17
62
86
17.2
Ibrahim Maza
M
7
624
2
2.1
16
33
78
7.2
Facundo Medina
D
7
560
1
0.4
1
43
98
9.7
Patrik Schick
F
7
545
6
3.5
7
1
67
0.9
Jarell Quansah
D
7
541
1
0.4
6
27
79
5.9
Afonso Moreira
M
7
512
0
0.7
3
13
76
3.5
Equi Fernández
M
7
438
1
0.8
6
17
93
5.3
Moussa Diaby
M
6
397
1
0.8
6
17
35
3.3
Malik Tillman
M
6
251
0
0.2
3
7
48
2.6
Robert Andrich
M
4
176
1
0.1
1
4
30
1.4
Christian Kofane
F
4
148
2
1.0
1
0
18
0.3
Nathan Tella
M
3
119
0
0.3
1
1
18
0.1
Loïc Badé
D
3
113
0
0.1
0
3
25
1.2
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.
Mark Flekken
Goalkeeper · Goalkeeper
minutes664
goals · xG0 · 0.0
prog passes /902.3
key passes /900.00
def actions /901.9
pass accuracy79%
Edmond Tapsoba
Stopper / marker · Defender
minutes664
goals · xG0 · 0.2
prog passes /902.7
key passes /900.27
def actions /9016.7
pass accuracy89%
Miguel Gutiérrez
Ball-playing defender · Defender
minutes653
goals · xG1 · 0.5
prog passes /907.9
key passes /901.65
def actions /9011.7
pass accuracy81%
Aleix García
Advanced creator · Attacking mid
minutes652
goals · xG0 · 0.3
prog passes /908.6
key passes /902.35
def actions /9011.9
pass accuracy88%
Ibrahim Maza
Advanced creator · Attacking mid
minutes624
goals · xG2 · 2.1
prog passes /904.8
key passes /902.31
def actions /9011.2
pass accuracy74%
Facundo Medina
Ball-playing defender · Defender
minutes560
goals · xG1 · 0.4
prog passes /906.9
key passes /900.16
def actions /9015.8
pass accuracy90%
Patrik Schick
Poacher / finisher · Forward
minutes545
goals · xG6 · 3.5
prog passes /900.2
key passes /901.16
def actions /9011.1
pass accuracy79%
Jarell Quansah
Stopper / marker · Defender
minutes541
goals · xG1 · 0.4
prog passes /904.5
key passes /901.00
def actions /9013.1
pass accuracy84%
Afonso Moreira
Ball-winner / destroyer · Midfielder
minutes512
goals · xG0 · 0.7
prog passes /902.3
key passes /900.53
def actions /9013.4
pass accuracy75%
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 (5.09, 50th percentile vs the league).