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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.

possession-dominantaggressive high-presshigh-volume attack
D
W
W
L
W
W
L
D
L
W
D
W
W
W
L
13
points
#4 of 18 · 7 games
16:12
goals
+4 GD
4-1-2
record
W-D-L
1.86
points/game
#4 in the league
1.75
xG for/game
rank #6
1.46
xG against/game
rank #6
#9
press rank
of 18 · most aggressive
57%
field tilt
#4
Leverkusen
League average
Expected goals for / game
1.751.63
Expected goals against / game
1.461.63
Field tilt
57%50%
Pass retention
84%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 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.

#teamPWDLGFGAGDPts
1Bayern7520256+1917
2Borussia Dortmund7520124+817
3Freiburg7412169+713
4Leverkusen74121612+413
5RBL74032011+912
6Werder Bremen73311514+112
7Mainz73131613+310

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)
even0-15'16-30'31-45'46-60'61-75'76-90+'+2.35-0.61
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.

0491312.310.2Elversberg (A) 2-3 · xG 1.73 to 1.19Union Berlin (H) 4-0 · xG 2.72 to 0.38Augsburg (A) 2-2 · xG 2.60 to 1.05RBL (H) 2-0 · xG 1.71 to 0.87Mainz (A) 4-3 · xG 1.67 to 2.88Freiburg (H) 2-1 · xG 1.20 to 1.02Hoffenheim (A) 0-3 · xG 0.64 to 2.80M1M2M3M4M5M6M7
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).

Possess83Disrupt57Finish76Press57Defend49
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.

Keeping the ball6 sections

Field Tilt — Territorial Control

The question
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.

Borussia Dortmund: 57% tilt, 2.43 pts/gBayern: 77% tilt, 2.43 pts/gFreiburg: 55% tilt, 1.86 pts/gLeverkusen: 57% tilt, 1.86 pts/gWerder Bremen: 42% tilt, 1.71 pts/gRBL: 54% tilt, 1.71 pts/gMainz: 60% tilt, 1.43 pts/gElversberg: 37% tilt, 1.43 pts/gHamburg: 35% tilt, 1.29 pts/gAugsburg: 44% tilt, 1.29 pts/gHoffenheim: 62% tilt, 1.29 pts/gPaderborn: 46% tilt, 1.14 pts/gEintracht Frankfurt: 48% tilt, 1.14 pts/gKoln: 48% tilt, 1.14 pts/gUnion Berlin: 41% tilt, 0.86 pts/gStuttgart: 56% tilt, 0.86 pts/gSchalke: 38% tilt, 0.86 pts/gBorussia Monchengladbach: 44% tilt, 0.43 pts/gLeverkusen35%49%63%77%0.41.11.82.4FIELD TILT %POINTS / GAME
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.

Build-up: 24.0% of the time on the ball24%Progression: 48.2% of the time on the ball48%Chance creation: 24.5% of the time on the ball25%Attacking transition: 3.3% of the time on the ball48%PROGRESSION
PhaseShare of the time
Build-up24.0%
Progression48.2%
Chance creation24.5%
Attacking transition3.3%
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.

Aleix García ↔ Facundo Medina: 165 passes, 0.3 combined xTAleix García ↔ Edmond Tapsoba: 129 passes, 0.1 combined xTAleix García ↔ Miguel Gutiérrez: 127 passes, 0.7 combined xTFacundo Medina ↔ Miguel Gutiérrez: 116 passes, -0.4 combined xTEdmond Tapsoba ↔ Jarell Quansah: 116 passes, -0.8 combined xTFacundo Medina ↔ Edmond Tapsoba: 110 passes, -0.1 combined xTAleix García ↔ Equi Fernández: 105 passes, 1.0 combined xTMark Flekken ↔ Facundo Medina: 84 passes, -0.2 combined xTAleix García ↔ Jarell Quansah: 80 passes, -0.1 combined xTAleix García ↔ Ibrahim Maza: 63 passes, 1.1 combined xTMiguel Gutiérrez ↔ Afonso Moreira: 57 passes, 1.7 combined xTMark Flekken ↔ Edmond Tapsoba: 56 passes, 0.8 combined xTFacundo Medina ↔ Equi Fernández: 52 passes, 0.1 combined xTMiguel Gutiérrez ↔ Equi Fernández: 52 passes, 0.4 combined xTEdmond Tapsoba ↔ Equi Fernández: 49 passes, -0.3 combined xTMiguel Gutiérrez ↔ Ibrahim Maza: 44 passes, 2.0 combined xTMoussa Diaby ↔ Jarell Quansah: 39 passes, 0.3 combined xTAleix García ↔ Afonso Moreira: 38 passes, 0.8 combined xTEqui Fernández ↔ Ibrahim Maza: 38 passes, 1.3 combined xTMark Flekken ↔ Aleix García: 37 passes, -0.2 combined xTAleix García ↔ Moussa Diaby: 36 passes, 1.4 combined xTRobert Andrich ↔ Aleix García: 34 passes, 0.1 combined xTAleix García ↔ Malik Tillman: 34 passes, -0.1 combined xTJarell Quansah ↔ Ibrahim Maza: 33 passes, 1.4 combined xTMark Flekken — isolate this player's connectionsFlekkenPatrik Schick — isolate this player's connectionsSchickAleix García — isolate this player's connectionsGarcíaFacundo Medina — isolate this player's connectionsMedinaEdmond Tapsoba — isolate this player's connectionsTapsobaMiguel Gutiérrez — isolate this player's connectionsGutiérrezEqui Fernández — isolate this player's connectionsFernándezJarell Quansah — isolate this player's connectionsQuansahIbrahim Maza — isolate this player's connectionsMazaAfonso Moreira — isolate this player's connectionsMoreiraArthur — isolate this player's connectionsArthurMalik Tillman — isolate this player's connectionsTillmanMoussa Diaby — isolate this player's connectionsDiabyRobert Andrich — isolate this player's connectionsAndrichLucas Vázquez — isolate this player's connectionsVázquezLoïc Badé — isolate this player's connectionsBadé
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
84p · -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
4%4%10%4%4%7%10%13%12%8%6%4%3%4%7%PASSES
Carries
the ball moved with a player
1044
ACTIONS
4%5%9%5%4%9%10%13%9%10%7%2%2%3%7%CARRIES
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.

41%Left side26%Middle33%Right sideATTACKING →
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.

Left wing: 100 entries (26%)10026%LEFT WINGLeft half-space: 63 entries (17%)6317%LEFT HALF-SPACECentre: 66 entries (17%)6617%CENTRERight half-space: 50 entries (13%)5013%RIGHT HALF-SPACERight wing: 100 entries (26%)10026%RIGHT WINGATTACKING ↑
ChannelEntriesPer matchShareLeague share
Left wing10014.326%28%
Left half-space639.017%17%
Centre669.417%14%
Right half-space507.113%15%
Right wing10014.326%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%
left: 185 passes, most from Medina185right: 167 passes, most from Tapsoba167
Long Wide52 · 6%
right: 31 passes, most from Flekken31left: 21 passes, most from Flekken21
Long Central12 · 1%
right: 8 passes, most from Flekken8left: 4 passes, most from Flekken4
Wide → FB346 · 40%
left: 187 passes, most from Flekken187right: 159 passes, most from Flekken159
Central112 · 13%
right: 61 passes, most from García61left: 51 passes, most from Flekken51
◆ 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.

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%
left: 314 passes, most from Medina314right: 238 passes, most from Tapsoba238
Long Wide68 · 4%
right: 40 passes, most from García40left: 28 passes, most from Gutiérrez28
Long Central12 · 1%
right: 8 passes, most from Gutiérrez8left: 4 passes, most from Quansah4
Wide → FB908 · 49%
left: 488 passes, most from Gutiérrez488right: 420 passes, most from García420
Central321 · 17%
right: 161 passes, most from García161left: 160 passes, most from García160
◆ 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.

Carries Map — progressive ball-carries, squad-wide

The question
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 (83)Second (70)Third (52)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
Attacking transition: 25 shots (20%)20%25Settled attack: 75 shots (59%)59%75Set piece: 27 shots (21%)21%27127SHOTS
Kind of attack, then how the shot was set upShotsGoalsxGxG / shot
Attacking transition 20% of shots2542.770.11
Cross320.470.16
Cut-back500.870.17
Long ball100.120.12
Pass into the box / short pass610.540.09
Own creation (carry, dribble, rebound)1010.770.08
Settled attack 59% of shots75107.460.10
Cross801.040.13
Cut-back1321.420.11
Long ball410.240.06
Pass into the box / short pass3143.080.10
Own creation (carry, dribble, rebound)1931.680.09
Set piece 21% of shots2722.040.08
Corner2622.010.08
Free kick100.020.02

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

PlayerSet-upsxG createdGoals
Ibrahim Maza172.021
Miguel Gutiérrez121.663
Moussa Diaby60.790
Patrik Schick70.712
Jarell Quansah40.541

Who takes them

PlayerShotsxGGoals
Patrik Schick253.526
Ibrahim Maza222.122
Christian Kofane81.002
Equi Fernández50.841
Moussa Diaby110.761
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%
left: 2 passes, most from Vázquez2right: 2 passes, most from Tapsoba2
Long Wide5 · 6%
left: 3 passes, most from Maza3right: 2 passes, most from García2
Long Central13 · 15%
left: 7 passes, most from García7right: 6 passes, most from García6
Wide → FB21 · 24%
left: 13 passes, most from Gutiérrez13right: 8 passes, most from Quansah8
Central43 · 50%
left: 22 passes, most from Maza22right: 21 passes, most from Schick21

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.

deep right wing: 5 crosses (4%)4%5deep right half-space: 0 crosses (0%)deep centre: 0 crosses (0%)deep left half-space: 0 crosses (0%)deep left wing: 7 crosses (5%)5%7advanced right wing: 30 crosses (23%)23%30advanced right half-space: 0 crosses (0%)advanced centre: 0 crosses (0%)advanced left half-space: 2 crosses (2%)2%2advanced left wing: 35 crosses (27%)27%35byline right wing: 21 crosses (16%)16%21byline right half-space: 2 crosses (2%)2%2byline centre: 0 crosses (0%)byline left half-space: 1 crosses (1%)1%1byline left wing: 25 crosses (20%)20%25
TerritoryCrossesShareLed to a shot
advanced · left wing3527%3
advanced · right wing3023%1
byline · left wing2520%5
byline · right wing2116%1
deep · left wing75%1
deep · right wing54%0
advanced · left half-space22%0
byline · right half-space22%0
byline · left half-space11%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

35 of 147 pass attempts lost here (24%).24%35/147Quansah26 of 174 pass attempts lost here (15%).15%26/174Tapsoba59 of 396 pass attempts lost here (15%).15%59/396Flekken18 of 162 pass attempts lost here (11%).11%18/162Medina42 of 175 pass attempts lost here (24%).24%42/175Gutiérrez35 of 289 pass attempts lost here (12%).12%35/289Quansah44 of 415 pass attempts lost here (11%).11%44/415Maza39 of 532 pass attempts lost here (7%).7%39/532García42 of 480 pass attempts lost here (9%).9%42/480Medina31 of 336 pass attempts lost here (9%).9%31/336Gutiérrez69 of 241 pass attempts lost here (29%).29%69/241García48 of 154 pass attempts lost here (31%).31%48/154Maza36 of 141 pass attempts lost here (26%).26%36/141Schick47 of 153 pass attempts lost here (31%).31%47/153Maza75 of 273 pass attempts lost here (27%).27%75/273Gutiérrezattacking →

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.

Pressing2 sections

Pressing — the full picture

The football question
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

metricvaluerankmeaning
PPDA (opp passes per def action)—#9/18lower = more intense; rank is what matters
Defensive actions / game145#17/18
High-press share23%#10/18def actions won high up
Defensive-action height (0–100)38.7#9/18avg pitch height of actions
High recoveries / game24.3#7/18ball won in mid+att third
High-recovery share54%#8/18share of recoveries won high
Opponent completion allowed80%#10/18lower = more disruptive
Opponent progressive allowed10.4%#15/18of 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

← opponent shot at own goal

Every opponent key pass against them (65 total)

5%6%2%2%20%14%17%15%20%Vladimír CoufalAlexander PrassMats RotsLeon AvdullahuWouter BurgerMats RotsFisnik AsllaniVladimír CoufalTim SkarkeWoo-yeong JeongAndrej KramaricBambasé ContéJosip JuranovicRobin FellhauerMichael GregoritschMichael GregoritschHennes BehrensRobin FellhauerMarius WolfFabian RiederNoahkai BanksDavid RaumAntonio NusaChristopher NkunkuNicolas SeiwaldTidiam GomisFrancis OnyekaFrancis OnyekaLukas PetkovLasse GüntherLukas PetkovLukasz PorebaSheraldo BeckerAnthony CaciPhillipp MweneSheraldo BeckerNadiem AmiriSheraldo BeckerNadiem AmiriSheraldo BeckerAnthony CaciAnthony CaciAnthony CaciNadiem AmiriSheraldo BeckerKacper PotulskiSheraldo BeckerPhillipp MweneDanny da CostaNadiem AmiriAntonio NusaDavid RaumChristopher NkunkuDavid RaumDavid RaumPhilipp TreuNiklas BesteJordy MakengoDerry ScherhantDerry ScherhantNiklas BesteIgor MatanovicIgor MatanovicMatthias GinterMatthias Ginter← own goal

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.

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
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)

14%3%45%7%14%7%3%
Delivered into the box 93%Short 7%

Left side (18)

17%6%28%6%33%11%
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)

12%25%38%12%12%
Shot 8Goals 1

Left flag (6)

17%33%33%17%
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 typegoals for/ggoals against/gpoints/g
vs high-pressing opponents (2m)1.001.501.50
vs mid-block opponents (2m)3.000.503.00
vs low-block opponents (3m)2.672.671.33

Behaviour — how Leverkusen actually play against each

opponent typeretentiondirectness (long balls)prog. passes/gxT/gbox entries/g
vs high-pressing opponents83%13%4210.911.5
vs mid-block opponents85%12%4612.018.0
vs low-block opponents84%9%5414.325.7
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.]
Squad

Players

Who drives the numbers.

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 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)ActionsOBVper 100
Ibrahim Maza466+2.85+0.61
Patrik Schick161+2.58+1.60
Miguel Gutiérrez615+2.00+0.32
Facundo Medina621+1.06+0.17
Moussa Diaby237+1.03+0.44
Edmond Tapsoba659+0.84+0.13
Equi Fernández412+0.83+0.20
Afonso Moreira269+0.75+0.28
123456
Biggest single movesOBV
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.

PlayerCorners takenInto the boxSet-piece shotsGoalsxGFree-kick shotsPenalties
Aleix García3932 (82%)2—0.061—
Ibrahim Maza31 (33%)5—0.25——
Patrik Schick——510.65——
Christian Kofane——3—0.25——
Moussa Diaby——3—0.15——
Facundo Medina——210.22——
Jarell Quansah——2—0.15——
Edmond Tapsoba——2—0.16——
Miguel Gutiérrez40 (0%)——0.00——
Malik Tillman——1—0.07——
Afonso Moreira——1—0.04——
Victor Boniface——1—0.05——
Jonas Hofmann10 (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.

playerposminVision AI Scorecapability blocks
Ibrahim MazaAM6246.8Ball-Carrying 85%Box Threat 62%Chance Creation 43%
Aleix GarcíaCM6526.7Progression 90%Chance Creation 61%Ball-Carrying 52%Successful Dribble 15%
Patrik SchickST5456.3Box Threat 80%Finishing 58%Ball-Carrying 36%Aerial Threat 0%
Edmond TapsobaCB6646.0Ball-Carrying 96%Progression 84%Ball-Winning 37%Aerial & Box Defending 14%
Mark FlekkenGK6644.9Shot-Stopping 49%
Jarell QuansahFB5414.3Ball-Carrying 79%Ball-Winning 23%Wide Delivery 8%
Miguel GutiérrezFB6534.1Wide Delivery 48%Ball-Winning 38%Ball-Carrying 38%
Afonso MoreiraAM5123.8Box Threat 50%Ball-Carrying 38%Chance Creation 25%
Facundo MedinaCB5603.8Progression 89%Ball-Carrying 78%Ball-Winning 41%Aerial & Box Defending 6%

Top Creators

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

playerposminkey P /90box entriesxT
Aleix GarcíaM6522.352417.2
Ibrahim MazaM6242.31247.2
Miguel GutiérrezD6531.652111.7
Moussa DiabyM3971.36153.3
Jarell QuansahD5411.0085.9
Patrik SchickF5451.1660.9
Afonso MoreiraM5120.53103.5
Equi FernándezM4381.2355.3

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.

playerposmincrosses (completed)cutbacks (completed)
Miguel GutiérrezFB65325 (40%)3 (67%)
Moussa DiabyW3978 (38%)17 (53%)
Jarell QuansahFB5413 (67%)0

Defensive Volume Leaders

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

playerposmindef actionstacklesinterceptrecoveries
Edmond TapsobaD6641239537
Facundo MedinaD560986729
Equi FernándezM4389320432
Aleix GarcíaM6528681137
Miguel GutiérrezD6538510436
Jarell QuansahD541799122
Ibrahim MazaM624789425
Afonso MoreiraM512766318

Full Squad Data

Every player with 100+ minutes (16 players).

playerposappsmingoalsxGkey PprogdefxT
Mark FlekkenG766400.0017145.5
Edmond TapsobaD766400.22201239.2
Miguel GutiérrezD765310.512578511.7
Aleix GarcíaM765200.317628617.2
Ibrahim MazaM762422.11633787.2
Facundo MedinaD756010.4143989.7
Patrik SchickF754563.571670.9
Jarell QuansahD754110.4627795.9
Afonso MoreiraM751200.7313763.5
Equi FernándezM743810.8617935.3
Moussa DiabyM639710.8617353.3
Malik TillmanM625100.237482.6
Robert AndrichM417610.114301.4
Christian KofaneF414821.010180.3
Nathan TellaM311900.311180.1
Loïc BadéD311300.103251.2

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).
Vision AI · building report