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Team reportBundesliga 2025/26

Koln

A balanced-territory side, aggressive high-press out of possession, with a moderate attack. Finished #15 of 21 on 40 points (9-13-19, 57:74). Tactical profile below is built from 41 matches of ball-by-ball event data, contextualised against the league.

balanced-territoryaggressive high-pressmoderate attack
D
D
W
D
L
D
L
L
W
L
D
L
W
L
D
40
points
#15 of 21 · 41 games
57:74
goals
-17 GD
9-13-19
record
W-D-L
0.98
points/game
#15 in the league
1.42
xG for/game
rank #10
1.50
xG against/game
rank #10
#14
press rank
of 21 · most aggressive
47%
field tilt
#12
Koln
League average
Expected goals for / game
1.421.43
Expected goals against / game
1.501.52
Field tilt
47%49%
Pass retention
79%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 Koln and how good was the season?
Read. Koln finished #15 of 21 on 40 points (9W-13D-19L, 0.98 pts/game), scoring 57 and conceding 74 for a goal difference of -17. On the ball they are a balanced-territory team (47% field tilt, #12 in the league); off it they defend with a aggressive high-press (#14 of 21 for press intensity). Across the 41 matches of event data, they create 1.42 xG/game (#10) and concede 1.50 (#10), a net of -0.07 xG/game — the underlying performance diverges from the actual goal difference.

Season Context — the league table

The question
Where did Koln actually finish, and against whom?

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

#teamPWDLGFGAGDPts
12Union Berlin411112185076-2645
13Werder Bremen411111195274-2244
14Borussia Monchengladbach411011205375-2241
15Koln41913195774-1740
16Wolfsburg3478194569-2429
17Heidenheim3468204172-3126
18St. Pauli3468202960-3126

Match Momentum — when they create and concede danger

The question
Across a whole game, which periods does Koln 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+'+4.89-3.10
Vision AI Model Insight
Koln tend to come alive after the break — first-half net xG -2.97, second-half -0.01. Their strongest period is 76-90+' (net +4.89 xG); their most dangerous period to face is 46-60' (net -3.10 xG against them).

xG Race — the season, match by match

Expected goals piling up over 41 matches: Koln 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 (-3.0 xG). Hover a point for that match.

022446658.461.4Mainz (A) 1-0 · xG 1.02 to 1.68Freiburg (H) 4-1 · xG 1.53 to 0.97Wolfsburg (A) 3-3 · xG 1.55 to 1.61RBL (A) 1-3 · xG 1.25 to 3.47Stuttgart (H) 1-2 · xG 1.02 to 2.33Hoffenheim (A) 1-0 · xG 0.83 to 1.18Augsburg (H) 1-1 · xG 0.54 to 1.18Borussia Dortmund (A) 0-1 · xG 0.76 to 1.88Hamburg (H) 4-1 · xG 2.47 to 1.01Borussia Monchengladbach (A) 1-3 · xG 2.32 to 2.38Eintracht Frankfurt (H) 3-4 · xG 1.25 to 1.68Werder Bremen (A) 1-1 · xG 0.81 to 1.28St. Pauli (H) 1-1 · xG 1.92 to 0.22Leverkusen (A) 0-2 · xG 0.21 to 1.41Union Berlin (H) 0-1 · xG 0.78 to 1.47Heidenheim (A) 2-2 · xG 2.56 to 1.58Bayern (H) 1-3 · xG 0.62 to 1.25Mainz (H) 2-1 · xG 1.97 to 1.04Freiburg (A) 1-2 · xG 1.48 to 2.07Wolfsburg (H) 1-0 · xG 1.04 to 1.10RBL (H) 1-2 · xG 1.16 to 2.36Stuttgart (A) 1-3 · xG 1.39 to 1.46Hoffenheim (H) 2-2 · xG 1.10 to 1.87Augsburg (A) 0-2 · xG 0.55 to 1.25Borussia Dortmund (H) 1-2 · xG 1.57 to 1.38Hamburg (A) 1-1 · xG 1.15 to 0.30Borussia Monchengladbach (H) 3-3 · xG 1.36 to 1.10Eintracht Frankfurt (A) 2-2 · xG 2.81 to 2.02Werder Bremen (H) 3-1 · xG 3.45 to 1.03St. Pauli (A) 1-1 · xG 1.44 to 1.64Leverkusen (H) 1-2 · xG 2.56 to 1.35Union Berlin (A) 2-2 · xG 1.10 to 1.06Heidenheim (H) 1-3 · xG 1.18 to 1.16Bayern (A) 1-5 · xG 0.82 to 2.62Hoffenheim (H) 3-2 · xG 1.67 to 1.83Stuttgart (A) 1-4 · xG 2.24 to 2.67Werder Bremen (H) 1-1 · xG 1.91 to 1.06Hamburg (A) 1-2 · xG 1.83 to 1.29Borussia Monchengladbach (H) 2-1 · xG 1.44 to 1.02Eintracht Frankfurt (A) 0-1 · xG 0.79 to 1.55Schalke (H) 0-0 · xG 0.97 to 0.60M1M6M11M16M21M26M31M36M41
KolnOpponents against Koln

Tactical DNA

The football question
In five genes, what is Koln'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.

48
Build-Up
possession control from the back
40
Progression
moving the ball upfield with threat
54
Creation
manufacturing chances
38
Press
winning the ball high & hard
52
Solidity
resisting the opponent's threat
Genetic identity
Koln are defined most by their Creation DNA (54/100 · average at manufacturing chances), and least by their Press DNA (38/100). 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).

Possess38Disrupt40Finish40Press27Defend37
Possess
Deep buildup
24%
Possession
48%
Field tilt
43%
Disrupt
Central progression ~proxy
48%
Dribble tendency
33%
Progressive passes
38%
Finish
Final-third patience
14%
Shot quality
48%
Open-play xG
57%
Press
Forward press ~proxy
33%
High regains
29%
Pressing efficiency ~proxy
19%
Defend
Def-mid press ~proxy
43%
High line ~proxy
29%
PAdj defensive actions
38%
Koln is defined most by Disrupt (40% in the league) and weakest on Press (27%). 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 Koln actually control, relative to the rest of the league?

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

Bayern: 73% tilt, 2.59 pts/gBorussia Dortmund: 59% tilt, 2.20 pts/gRBL: 55% tilt, 1.88 pts/gLeverkusen: 57% tilt, 1.76 pts/gHoffenheim: 57% tilt, 1.71 pts/gStuttgart: 58% tilt, 1.66 pts/gFreiburg: 48% tilt, 1.46 pts/gElversberg: 37% tilt, 1.43 pts/gEintracht Frankfurt: 51% tilt, 1.27 pts/gAugsburg: 46% tilt, 1.27 pts/gMainz: 51% tilt, 1.22 pts/gHamburg: 39% tilt, 1.15 pts/gPaderborn: 46% tilt, 1.14 pts/gUnion Berlin: 45% tilt, 1.10 pts/gWerder Bremen: 48% tilt, 1.07 pts/gBorussia Monchengladbach: 42% tilt, 1.00 pts/gKoln: 47% tilt, 0.98 pts/gSchalke: 38% tilt, 0.86 pts/gWolfsburg: 41% tilt, 0.85 pts/gSt. Pauli: 49% tilt, 0.76 pts/gHeidenheim: 39% tilt, 0.76 pts/gKoln37%49%61%73%0.81.42.02.6FIELD TILT %POINTS / GAME
Vision AI Model Insight
Koln post 47% field tilt, ranking #12 of 21 in the league — below the league mid-point, spending more of the game defending their own territory than dictating it, and their 0.98 points/game sits below the league-average return — territory is genuinely converting into results.

Where the time goes — how long each phase lasts

The question
When Koln has the ball, how much of that time is spent building, progressing and creating?

The share of Koln'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: 25.4% of the time on the ball25%Progression: 48.9% of the time on the ball49%Chance creation: 23.8% of the time on the ball24%Attacking transition: 2.0% of the time on the ball49%PROGRESSION
PhaseShare of the time
Build-up25.4%
Progression48.9%
Chance creation23.8%
Attacking transition2.0%
Vision AI Model Insight
Progression is where Koln spends most of its time on the ball (49% of its time on the ball).

Passing Network

The question
Who actually plays with whom — the real on-pitch connections, not just average positions?

Every pass linked to its likely receiver (the next same-team touch), aggregated into player-pair combinations at each player's average position. Line width = how often the pair combines; colour = threat generated per pass (blue low → red high). Hover a line for the pair, or click a player to isolate their connections.

Marvin Schwäbe ↔ Cenk Özkacar: 320 passes, 12.8 combined xTMarvin Schwäbe ↔ Eric Martel: 248 passes, 14.7 combined xTCenk Özkacar ↔ Kristoffer Lund: 239 passes, 10.3 combined xTCenk Özkacar ↔ Eric Martel: 236 passes, 8.0 combined xTJakub Kaminski ↔ Kristoffer Lund: 200 passes, 13.8 combined xTEric Martel ↔ Ísak Bergmann Jóhannesson: 182 passes, 7.7 combined xTEric Martel ↔ Kristoffer Lund: 170 passes, 5.0 combined xTMarvin Schwäbe ↔ Sebastian Sebulonsen: 167 passes, 15.9 combined xTSebastian Sebulonsen ↔ Eric Martel: 153 passes, 6.0 combined xTTom Krauß ↔ Eric Martel: 145 passes, 3.9 combined xTJan Thielmann ↔ Sebastian Sebulonsen: 126 passes, 7.5 combined xTJakub Kaminski ↔ Ísak Bergmann Jóhannesson: 125 passes, 5.9 combined xTCenk Özkacar ↔ Jakub Kaminski: 124 passes, 10.0 combined xTTom Krauß ↔ Jakub Kaminski: 123 passes, 7.5 combined xTJakub Kaminski ↔ Eric Martel: 122 passes, 5.6 combined xTCenk Özkacar ↔ Ísak Bergmann Jóhannesson: 112 passes, 4.2 combined xTSebastian Sebulonsen ↔ Ísak Bergmann Jóhannesson: 111 passes, 6.0 combined xTKristoffer Lund ↔ Saïd El Mala: 110 passes, 7.9 combined xTTom Krauß ↔ Kristoffer Lund: 98 passes, 3.6 combined xTMarvin Schwäbe ↔ Kristoffer Lund: 98 passes, 8.1 combined xTMarius Bülter ↔ Jakub Kaminski: 96 passes, 7.3 combined xTJakub Kaminski ↔ Saïd El Mala: 92 passes, 6.0 combined xTÍsak Bergmann Jóhannesson ↔ Kristoffer Lund: 91 passes, 2.5 combined xTTom Krauß ↔ Cenk Özkacar: 83 passes, 2.6 combined xTMarvin Schwäbe — isolate this player's connectionsSchwäbeSaïd El Mala — isolate this player's connectionsMalaÍsak Bergmann Jóhannesson — isolate this player's connectionsJóhannessoMarius Bülter — isolate this player's connectionsBülterSebastian Sebulonsen — isolate this player's connectionsSebulonsenJakub Kaminski — isolate this player's connectionsKaminskiEric Martel — isolate this player's connectionsMartelRagnar Ache — isolate this player's connectionsAcheTom Krauß — isolate this player's connectionsKraußKristoffer Lund — isolate this player's connectionsLundEllyes Skhiri — isolate this player's connectionsSkhiriLinton Maina — isolate this player's connectionsMainaJan Thielmann — isolate this player's connectionsThielmannCenk Özkacar — isolate this player's connectionsÖzkacarLuca Waldschmidt — isolate this player's connectionsWaldschmidAlessio Castro-Montes — isolate this player's connectionsCastro-Mon
width = passes between the paircolour = xT per pass: low → high

Top 10 combinations

Schwäbe ↔ Özkacar
320p · 12.8xT
Schwäbe ↔ Martel
248p · 14.7xT
Özkacar ↔ Lund
239p · 10.3xT
Özkacar ↔ Martel
236p · 8.0xT
Kaminski ↔ Lund
200p · 13.8xT
Martel ↔ Jóhannesson
182p · 7.7xT
Martel ↔ Lund
170p · 5.0xT
Schwäbe ↔ Sebulonsen
167p · 15.9xT
Sebulonsen ↔ Martel
153p · 6.0xT
Krauß ↔ Martel
145p · 3.9xT
Vision AI Model Insight
Busiest connection: Marvin Schwäbe ↔ Cenk Özkacar (320 passes). Most dangerous per pass: Marvin Schwäbe ↔ Sebastian Sebulonsen (0.09 xT/pass over 167 passes) — the combination that hurts opponents most when it fires, not just the one used most.

Touch Density — where Koln 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 Koln 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
18628
ACTIONS
3%4%12%5%5%7%8%12%11%11%5%3%2%4%7%PASSES
Carries
the ball moved with a player
4828
ACTIONS
3%5%12%6%4%7%9%13%12%11%4%2%1%3%7%CARRIES
Vision AI Model Insight
Their single busiest zone is the centre of the defensive third (12.2% of all actions). 62% of actions run through the inside lanes (centre + both half-spaces) versus 38% down the two wings — an inside-dominant, half-space build. By third: 30% defensive, 49% middle, 21% 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. Koln leaned on their left (46%). The bars under the pitch put the league’s share beside theirs.

46%Left side23%Middle31%Right sideATTACKING →
Koln
46%23%31%
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. Koln made 1496 this season (36.5 a match); 34% 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: 513 entries (34%)51334%LEFT WINGLeft half-space: 299 entries (20%)29920%LEFT HALF-SPACECentre: 168 entries (11%)16811%CENTRERight half-space: 185 entries (12%)18512%RIGHT HALF-SPACERight wing: 331 entries (22%)33122%RIGHT WINGATTACKING ↑
ChannelEntriesPer matchShareLeague share
Left wing51312.534%27%
Left half-space2997.320%16%
Centre1684.111%14%
Right half-space1854.512%15%
Right wing3318.122%28%
Build-up2 sections

Build-Up — the pass map

The football question
How does Koln 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.

5564build-up passes attempted
77%completed
28%reach the middle third
2%go long into the final third

Pass types

1452CB · 34%
458Long Wide · 11%
98Long Central · 2%
1696Wide → FB · 40%
584Central · 14%

Sequence success — build-up

1126build-up sequences this season
8%succeed — reach the next third or a shot
45%turned over with nothing to show
CB1452 · 34%
left: 803 passes, most from Özkacar803right: 649 passes, most from Simpson-Pusey649
Long Wide458 · 11%
left: 265 passes, most from Schwäbe265right: 193 passes, most from Schwäbe193
Long Central98 · 2%
left: 52 passes, most from Schwäbe52right: 46 passes, most from Schwäbe46
Wide → FB1696 · 40%
left: 923 passes, most from Schwäbe923right: 773 passes, most from Schwäbe773
Central584 · 14%
left: 304 passes, most from Schwäbe304right: 280 passes, most from Schwäbe280
◆ Vision AI Insight
Koln build predominantly down the left, with Schwäbe as the fulcrum (1369 build-up passes). Only 31% 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 → LHS def (298×).

Pass Explorer

Every pass of Koln’s last 10 matches as an arrow, theirs first and the opponents’ with other team, each side attacking left to right (solid = completed, dashed grey = failed). Eight families per team — click any map to open it wide, then filter by channel or third, from one to another (or click a cell of the flow grids), by outcome, half, minute and passer. Channels: right wing, right half-space, centre, left half-space, left wing.

Progression4 sections

How They Progress — the mechanism

Not just how many progressive passes, but where they start, who plays them, and whether they actually penetrate. 1809 progressive passes across 41 matches (44.1/game).

Origin lane

Right wing
24%
Right half-space
12%
Central
20%
Left half-space
12%
Left wing
32%
Progression is left-dominant: 35% right side · 20% central · 44% left.

Who generates it

Marvin Schwäbe
336 · 19%
Kristoffer Lund
146 · 8%
Jakub Kaminski
139 · 8%
Sebastian Sebulonsen
129 · 7%
Cenk Özkacar
104 · 6%
Top two — Marvin Schwäbe & Kristoffer Lund — account for 27% of all progression.
PROGRESSION
44.1 prog passes/game
mostly left
→
PENETRATION
30% reach final third
21% reach the box
→
THREAT
the other 70% is recycled
progress ≠ danger
What came of it. 30% of progressive passes reached the final third and 21% the box; the rest was recycled in front of the block. 27% 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 wingbalanced

Progression — the pass map

The football question
Once Koln 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.

9059progression passes attempted
85%completed
14%break into the final third
12%go backward into the defensive third

Pass types

2538CB · 33%
277Long Wide · 4%
59Long Central · 1%
3614Wide → FB · 47%
1197Central · 16%

Sequence success — progression

1731progression sequences this season
25%succeed — reach the next third or a shot
45%turned over with nothing to show
CB2538 · 33%
left: 1488 passes, most from Özkacar1488right: 1050 passes, most from Simpson-Pusey1050
Long Wide277 · 4%
left: 160 passes, most from Lund160right: 117 passes, most from Sebulonsen117
Long Central59 · 1%
left: 34 passes, most from Lund34right: 25 passes, most from Krauß25
Wide → FB3614 · 47%
left: 2182 passes, most from Lund2182right: 1432 passes, most from Sebulonsen1432
Central1197 · 16%
right: 626 passes, most from Martel626left: 571 passes, most from Martel571
◆ Vision AI Insight
Koln progress predominantly down the left, with Martel the busiest midfield outlet (765 progression passes). Only 14% of passes that start in midfield actually break the final-third line — the rest (74%) is recycled back into midfield or (12%) played backward, the hallmark of a side that carries progression itself through midfield rather than just passing through it. Their most-repeated pattern is LHS mid → LW mid (524×) — 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)
14%
Progressive
1%
Neutral / sideways
49%
Safe / backward
36%
15% of passes are progressive-or-better; the remaining 85% keep possession without advancing it. A control-first side — safe in build-up, reliant on a few players for the incision.

Carries Map — progressive ball-carries, squad-wide

The question
Where does Koln actually progress the ball with their feet, not a pass?

1067 progressive carries this season across the whole squad (23% of 4618 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 62m. Most active carrier: Özkacar (82). Filter to one player below.

First (342)Second (291)Third (229)Fourth (205)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 Koln'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.

567shots
58.4xG
0.10xG per shot
30big chances (0.3+ xG)
54goals
#12key passes / game in the league
#11box entries / game in the league
Attacking transition: 154 shots (27%)27%154Settled attack: 295 shots (52%)52%295Set piece: 118 shots (21%)21%118567SHOTS
Kind of attack, then how the shot was set upShotsGoalsxGxG / shot
Attacking transition 27% of shots1542020.700.13
Cross1122.160.20
Cut-back1111.240.11
Long ball811.260.16
Pass into the box / short pass7089.670.14
Own creation (carry, dribble, rebound)5486.360.12
Settled attack 52% of shots2952425.230.09
Cross63107.320.12
Cut-back1111.860.17
Long ball1211.090.09
Pass into the box / short pass13079.310.07
Own creation (carry, dribble, rebound)7955.650.07
Set piece 21% of shots1181012.490.11
Corner10168.910.09
Free kick1310.400.03
Penalty433.180.79

Every attempt to create — and what came of it

Every pass into the box, every cut-back and every pass before a shot. Red led to a shot, gold to a goal; grey ones were tried but no shot came: solid found a team-mate, dashed lost the ball. Filter by outcome, type or player; tap an arrow for the detail.

Who sets the shots up

PlayerSet-upsxG createdGoals
Marius Bülter325.614
Jakub Kaminski364.193
Saïd El Mala222.903
Ragnar Ache252.584
Linton Maina242.512

Who takes them

PlayerShotsxGGoals
Saïd El Mala10112.2915
Ragnar Ache609.788
Jakub Kaminski636.187
Marius Bülter515.305
Luca Waldschmidt284.535
Vision AI Model Insight
The biggest source of Koln's chances, by xG, is settled attack (25.2 of 58.4 xG, 295 of 567 shots), mostly pass into the box / short pass. 154 of the 567 shots (27%) came within 15 seconds of winning the ball. 118 of the 567 shots came from set pieces; 54 goals from 58.4 xG (-4.4).

Chance Creation — the pass map

The football question
How does Koln 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

36CB · 10%
17Long Wide · 5%
39Long Central · 11%
127Wide → FB · 35%
148Central · 40%

Sequence success — final-third

1259final-third sequences this season
23%succeed — produce a shot
42%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
CB36 · 10%
left: 21 passes, most from Castro-Montes21right: 15 passes, most from Castro-Montes15
Long Wide17 · 5%
left: 11 passes, most from Sosa11right: 6 passes, most from Kaminski6
Long Central39 · 11%
right: 25 passes, most from Kaminski25left: 14 passes, most from Kaminski14
Wide → FB127 · 35%
left: 79 passes, most from Kaminski79right: 48 passes, most from Krauß48
Central148 · 40%
left: 79 passes, most from Kaminski79right: 69 passes, most from Kaminski69

Who creates — volume × threat

48Kaminski
32Jóhannesson
30Bülter
29Maina
27Ache
25Castro-Montes
22Krauß
367key passes #12 of 21
13.0box entries/game #11 of 21
28.8final-3rd entries/game #11 of 21
1.42xG/game #10 of 21
54goals #11 of 21
-4.4finishing vs xG #16 of 21
◆ Vision AI Insight
Koln lean left — 42% of their key passes come from that flank and its half-space (155 of 367), most often from Kaminski (24). They funnel chances into the c of the final third, most often via LW att → C att (53 key passes, most often from Bülter). Kaminski is the primary source overall. They generate 1.42 xG a game and finish it wasteful — they under-perform their xG, led by Saïd El Mala (15G), Ragnar Ache (8G), Jakub Kaminski (7G).

Crosses & Cutbacks — squad-wide

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

637 crosses: 203 found a team-mate (32%), 74 led to a shot (12%). 109 cut-backs: 42 found a team-mate (39%), 23 led to a shot (21%). 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 Koln actually cross from?

637 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. 28% come from one territory, the byline left wing; 32 come from the half-spaces and 0 from the centre.

deep right wing: 25 crosses (4%)4%25deep right half-space: 2 crosses (0%)0%2deep centre: 0 crosses (0%)deep left half-space: 3 crosses (0%)0%3deep left wing: 36 crosses (6%)6%36advanced right wing: 91 crosses (14%)14%91advanced right half-space: 7 crosses (1%)1%7advanced centre: 0 crosses (0%)advanced left half-space: 5 crosses (1%)1%5advanced left wing: 117 crosses (18%)18%117byline right wing: 158 crosses (25%)25%158byline right half-space: 10 crosses (2%)2%10byline centre: 0 crosses (0%)byline left half-space: 5 crosses (1%)1%5byline left wing: 178 crosses (28%)28%178
TerritoryCrossesShareLed to a shot
byline · left wing17828%24
byline · right wing15825%17
advanced · left wing11718%14
advanced · right wing9114%10
deep · left wing366%1
deep · right wing254%2
byline · right half-space102%3
advanced · right half-space71%0
advanced · left half-space51%1
byline · left half-space51%0
deep · left half-space30%1
deep · right half-space20%1
Finishing3 sections

Shot Map

567 shots for 58.4 xG (1.42/game, #10 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.

Moderate attack. 13.8 shots/game at 0.103 xG/shot. Box entries 13/game (#11). Threat created 10.4 xT/game (#12).

Goal Sequences

The move behind each of Koln’s 57 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 — 57 goals traced.

How Do They Score?

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

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

By situation

Open play
44 · 81%
Set pieces
7 · 13%
Penalties
3 · 6%

By finish

Right foot
29 · 54%
Left foot
16 · 30%
Header
9 · 17%
Vision AI Model Insight
54 goals scored. 81% come in open play; set pieces add 13%. 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

204 of 634 pass attempts lost here (32%).32%204/634Sebulonsen164 of 824 pass attempts lost here (20%).20%164/824Schwäbe488 of 2304 pass attempts lost here (21%).21%488/2304Schwäbe137 of 909 pass attempts lost here (15%).15%137/909Schwäbe283 of 893 pass attempts lost here (32%).32%283/893Lund274 of 1219 pass attempts lost here (22%).22%274/1219Sebulonsen188 of 1502 pass attempts lost here (13%).13%188/1502Sebulonsen240 of 2247 pass attempts lost here (11%).11%240/2247Jóhannesso274 of 2080 pass attempts lost here (13%).13%274/2080Martel398 of 2011 pass attempts lost here (20%).20%398/2011Lund353 of 1001 pass attempts lost here (35%).35%353/1001Sebulonsen181 of 548 pass attempts lost here (33%).33%181/548Sebulonsen124 of 447 pass attempts lost here (28%).28%124/447Ache191 of 653 pass attempts lost here (29%).29%191/653Mala411 of 1356 pass attempts lost here (30%).30%411/1356Lundattacking →

How they lose it

Incomplete pass — attacking third
1260
Failed forward pass
862
Failed take-on
351
Lost in the defensive third
1276
Their weakest zone is the right wing in the attacking third: 35% of 1001 pass attempts there are lost (353 times). Separately, 32% of all losses happen in the attacking third — a fair share happen deeper, which invites transitions.
Out of possession

Out of possession

How they press, where they win the ball back, and how they concede.

Pressing3 sections

Pressing — the full picture

The football question
How hard does Koln 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.

31
Pressing Index
/100 · passive
Intensity40
PPDA, high-press share, high-zone actions
Height29
how high up the pitch they defend
Effectiveness12
high recoveries & high-regain share
Disruption48
opponent completion & progression allowed

Every defensive action & where it happens

Where they win the ball back — by lane

RW
19%
RHS
18%
C
19%
LHS
22%
LW
21%
By third (high / mid / low regain)
Def 52%
Mid 35%
Att 13%
Read
Ranked #14 of 21 for press intensity, this is a high press. Note the tension: 52% of their raw defensive actions still occur in their own third — a possession effect, not a passive one. Dominating the ball (20% high-press share) means that when they do defend, it is often deep. Of recoveries, 51% are won high up the pitch.

Every pressing metric, ranked in the league

metricvaluerankmeaning
PPDA (opp passes per def action)—#14/21lower = more intense; rank is what matters
Defensive actions / game143#14/21
High-press share20%#12/21def actions won high up
Defensive-action height (0–100)36.2#15/21avg pitch height of actions
High recoveries / game18.9#20/21ball won in mid+att third
High-recovery share51%#17/21share of recoveries won high
Opponent completion allowed81%#13/21lower = more disruptive
Opponent progressive allowed9.4%#9/21of opp passes, lower = better
Transition timing
This source carries per-event timestamps, so genuine transition metrics — counter-attack shots, press→shot conversions, time-to-recover — are measured; see the dedicated Transitions section below.

How long they keep it — passes and seconds until the ball is won

The question
Once the opponent has the ball, how many passes and how many seconds pass before Koln win it back with a defensive action?

Counted over the 1174 opposition possessions that Koln 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 Koln step in. Koln win it after 5.5 opponent passes in the first half-hour and after 4.0 after the hour mark (sooner).

PASSES THEY GET
4.9passes
median 3 · before Koln win it
TIME THEY KEEP IT
14sec
median 6s · 28.6 wins per game
How many passes the opponent got in before the ball was won
44%
26%
15%
15%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
5.6
16s
15–30′
5.4
16s
30–45+′
5.9
16s
45–60′
4.9
14s
60–75′
4.4
13s
75–90+′
3.9
11s
average passesaverage seconds

Phases of play

Every moment of the match read twice — what Koln 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.

6,164phase segments analysed
7.0smedian time before the opposition engage
2.0their passes per defensive action when pressed
#14of 18 for share of defending spent pressing high

With the ball

Share of possession time spent in each phase.

Build-up 25.4%Progression 48.9%Chance creation 23.8%Attacking transition 2.0%

Without the ball

How Koln defend when the opponent has it.

High press 6.0%Mid block 10.0%Low block 54.7%Unopposed 29.4%

What opponents do against Koln

The mirror image, and the part no other section can show: whether teams come out to press Koln 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 6.7%Mid block 10.2%Low block 53.7%Unopposed 29.4%

Which phases actually produce

PhaseSegmentsAvg lengthAvg actionsEnds in shotTurned over
Build-up2,0954.3s2.70.0%51.0%
Progression2,5056.9s3.90.4%46.5%
Chance creation1,4655.7s3.227.7%52.9%
Attacking transition997.0s4.28.1%33.3%

Most common routes through the phases

How a possession actually travelled, in order. The chain is the tactical signature — a move that climbs all three thirds is a different thing from one that never leaves the first.

RouteTimesEnds in shot
build_up8370.0%
progression6070.0%
chance_creation36836.4%
build_up → progression3390.0%
progression → chance_creation30030.7%
build_up → progression → chance_creation17027.1%

Attitude in each third

Every other style number here says how much Koln 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 Koln 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) 5,564 passes

nothing unusual in this zone

Width of use-0.4620.05
Wide-channel share-0.310.29
Side bias+0.522.11
Pass length+0.5224.59
Long-ball share+0.510.28
Forward gain per pass+0.3613.03
Backward-pass share+0.190.21
Deep-pass share+0.380.32
Completion-0.320.77
Seconds between passes+0.353.54
Side used

left 29% · central 48% · right 23%

Progression (middle third) 9,059 passes

left-sided

Width of use-0.3324.87
Wide-channel share-0.300.38
Side bias+1.295.18
Pass length+0.4017.79
Long-ball share+0.040.10
Forward gain per pass-0.093.48
Backward-pass share+0.250.35
Deep-pass share+0.060.16
Completion-0.150.83
Seconds between passes+0.663.49
Side used

left 41% · central 32% · right 27%

Final third 4,005 passes

nothing unusual in this zone

Width of use+0.2832.30
Wide-channel share+0.320.61
Side bias+0.744.72
Pass length+0.4816.22
Long-ball share+0.620.13
Forward gain per pass+0.191.30
Backward-pass share-0.300.37
Deep-pass share+0.210.10
Completion-0.260.68
Seconds between passes+0.223.56
Side used

left 48% · central 16% · right 35%

Reference measured across roughly 3,500 match-teams in this competition, per third, so the comparison is with Koln'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 Koln 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%
#6 of 21 · reaches final third in ≤3 passes
Patient build-up share26%
#11 of 21 · spells of 6+ passes
Tempo (possession length)18.0s
#14 of 21 · shorter = faster tempo
Passes per possession4.4
#9 of 21
Counter-attack shots /game1.39
#4 of 21 · shot within 12s of a regain in own half
Press→shot /game3.44
#8 of 21 · shot within 8s of a high regain
Speed to final third after regain12.8s
#17 of 21 · lower = faster break

Sequence success — attacking transition

123attacking transition sequences this season
28%succeed — produce a shot
37%turned over with nothing to show

Sequence success — defensive transition

335defensive transition sequences this season
60%succeed — reach the next third or a shot
13%turned over with nothing to show
Vision AI Model Insight
Koln are a direct, fast-break side: 18% of possessions reach the final third in 3 passes or fewer (#6 of 21), against 26% built patiently over 6+ passes (#11 of 21). Winning the ball back leads to a shot inside 12 seconds 1.39 times a game (#4), and a high regain converts to a shot within 8 seconds 3.44 times a game (#8 of 21) — a modest but real source of chances.
Defending the box2 sections

Vulnerability Map — how opponents hurt them

The report flipped: where do opponents create their threat against Koln? 1935 opponent progressive passes and 566 shots faced (61.4 xG against) across the season.

Opponent threat by lane (attacking into Koln)

Right wing
31%
Right half-space
13%
Central
16%
Left half-space
12%
Left wing
28%
Where the danger came from. Opponents generated most of their threat through the right wing (31% of the danger conceded).

How Do They Concede?

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

Every goal Koln conceded across the loaded matches — the opponent's shots in Koln'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 (388 total)

2%2%2%3%1%18%16%17%18%19%Phillipp MweneAnthony CaciDominik KohrPhillipp MwenePaul NebelPhillipp MweneNelson WeiperDominik KohrDanny da CostaDanny da CostaDominik KohrStefan BellPhillipp MweneVincenzo GrifoChristian GünterEren DinkçiJohan ManzambiYuito SuzukiChristian GünterAnthony JungChristian GünterChristian GünterJohan ManzambiMaximilian ArnoldMaximilian ArnoldMohammed AmouraLovro MajerMaximilian ArnoldKilian FischerJoakim MæhleMaximilian ArnoldAndreas Skov OlsenMaximilian ArnoldTim LemperleBernardoFisnik AsllaniLeon AvdullahuMuhammed DamarWouter BurgerTim LemperleBazoumana TouréMuhammed DamarLeon AvdullahuVladimír CoufalVladimír CoufalDavid RaumDavid RaumJohan BakayokoYan DiomandeNicolas SeiwaldYan DiomandeJohan BakayokoDavid RaumRômuloEzechiel BanzuziConrad HarderMalik TillmanErnest PokuJonas HofmannAleix GarcíaChristian KofaneIbrahim MazaArthurJarell QuansahMalik TillmanArthurAleix GarcíaNathan TellaMartin TerrierAngelo StillerTiago TomásChris FührichAngelo StillerBilal El KhannoussRamon HendriksBilal El KhannoussAngelo StillerJosha VagnomanAngelo StillerAngelo StillerNikolas NarteyChristopher TrimmelDerrick KöhnChristopher TrimmelLeopold QuerfeldDerrick KöhnMert KömürHan-Noah MassengoMert KömürRobin FellhauerMert KömürCédric ZesigerJan SchöppnerNiklas DorschArijon IbrahimovicMarvin PieringerJulian NiehuesNiklas DorschFelix NmechaKarim AdeyemiPascal GroßJulian RyersonSerhou GuirassyDaniel SvenssonKarim AdeyemiSerhou GuirassyRamy BensebainiCarney ChukwuemekaJulian RyersonDaniel SvenssonJulian RyersonWaldemar AntonJulian RyersonJulian RyersonMaximilian BeierJulian RyersonJulian BrandtRamy BensebainiSerhou GuirassyMaximilian BeierSerhou GuirassyAtakan KarazorJamie LewelingAtakan KarazorMaximilian MittelstädtAtakan KarazorChris FührichJamie LewelingRamon HendriksAngelo StillerRayan PhilippeRayan PhilippeJean-Luc DompéFábio VieiraJean-Luc DompéJean-Luc DompéJean-Luc DompéAlbian HajdariOzan KabakVladimír CoufalWouter BurgerWouter BurgerAndrej KramaricAlbian HajdariVladimír CoufalVladimír CoufalFisnik AsllaniFisnik AsllaniBazoumana TouréVladimír CoufalJoe ScallyHaris TabakovicYannik EngelhardtFranck HonoratLukas UllrichHaris TabakovicMert KömürFabian RiederNoahkai BanksNoahkai BanksCédric ZesigerRobin FellhauerAnton KadeAnsgar KnauffAnsgar KnauffMahmoud DahoudFarès ChaïbiMario GötzeFarès ChaïbiRitsu DoanAnsgar KnauffNathaniel BrownEllyes SkhiriSerhou GuirassyMaximilian BeierSerhou GuirassyDaniel SvenssonJulian BrandtYan CoutoDaniel SvenssonDaniel SvenssonDaniel SvenssonRomano SchmidRomano SchmidJens StageRomano SchmidCameron PuertasMarco FriedlCameron PuertasRomano SchmidMarco GrüllVictor BonifaceVictor BonifaceMarco GrüllMarco GrüllMiro MuheimWilliam MikelbrencisJean-Luc DompéJean-Luc DompéJackson IrvineAdam DzwigalaArkadiusz PyrkaDanel SinaniFranck HonoratJoe ScallyJens CastropKevin StögerYannik EngelhardtHugo BolinLuis DíazLeon GoretzkaAleksandar PavlovicHiroki ItoMichael OliseMichael OliseSerge GnabrySerge GnabryLuis DíazHiroki ItoTom BischofLuis DíazKaishu SanoDanny da CostaPaul NebelPaul NebelPaul NebelIgor MatanovicDerry ScherhantYuito SuzukiLukas KüblerNiklas BesteNiklas BesteYuito SuzukiYuito SuzukiNiklas BesteDzenan PejcinovicYannick GerhardtChristian EriksenChristian EriksenSaël KumbediYannick GerhardtAdam DaghimAdam DaghimJesper LindstrømDavid RaumAntonio NusaDavid RaumChristoph BaumgartnerDavid RaumRômuloYan DiomandeXaver SchlagerDavid RaumXaver SchlagerAntonio NusaDavid RaumAntonio NusaEzechiel BanzuziArnaud KalimuendoArnaud KalimuendoHugo LarssonOscar HøjlundAurèle AmendaRobin KochHugo LarssonRitsu DoanFarès ChaïbiArnaud KalimuendoFarès ChaïbiFarès ChaïbiArnaud KalimuendoFarès ChaïbiFarès ChaïbiArnaud KalimuendoKarim CoulibalyOlivier DemanDanel SinaniLars RitzkaJackson IrvineMathias RasmussenDanel SinaniMathias Pereira LageHauke WahlDanel SinaniJackson IrvineNathan TellaOliver BurkeAndrás SchäferStanley NsokiChristopher TrimmelChristopher TrimmelChristopher TrimmelRani KhediraArijon IbrahimovicHennes BehrensArijon IbrahimovicJan SchöppnerEren DinkçiArijon IbrahimovicHennes BehrensNiklas DorschJamal MusialaJoshua KimmichJoshua KimmichLennart KarlTom BischofJoshua KimmichLuis DíazHarry KaneLennart KarlDayot UpamecanoJoshua KimmichLeon GoretzkaJosip StanisicLuis DíazAdam HlozekVladimír CoufalBambasé ContéVladimír CoufalPatrick WimmerVladimír CoufalNathan De CatVladimír CoufalBambasé ContéBambasé ContéDeniz UndavBilal El KhannoussChris FührichChris FührichJosha VagnomanMaximilian MittelstädtJosha VagnomanDeniz UndavJosha VagnomanMaximilian MittelstädtChris FührichMaximilian MittelstädtErmedin DemirovicMaximilian MittelstädtFinn JeltschJosha VagnomanFinn JeltschArthurMarco GrüllYouri RegeerChukiMarco GrüllYouri RegeerNiclas FüllkrugNiclas FüllkrugChukiAlbert GrønbækAlbert GrønbækAlbert GrønbækYussuf PoulsenDavid Møller WolfePatson DakaFábio VieiraRobin HackFlorian NeuhausHugo BolinDaiki HashiokaJoe ScallyZento UnoWael MohyaShuto MachinoWael MohyaPhilipp SanderPhilipp SanderRitsu DoanKeita KosugiLilian BrassierKeita KosugiRitsu DoanRitsu DoanNnamdi CollinsFarès ChaïbiNoël AsékoAnsgar KnauffFarès ChaïbiRobin GosensHwang Hee-ChanRobin GosensSoufiane El-FaouziJunior AdamuAdil AouchicheAdil AouchicheJanik Bachmann← own goal

By situation

Open play
50 · 68%
Set pieces
19 · 26%
Penalties
5 · 7%
Vision AI Model Insight
74 goals conceded. 68% come in open play, and 26% from set pieces — a genuine defensive frailty at dead balls to fix on the training ground. 20% are headers — aerial defending is not where the damage comes from. Their supply line runs mainly through the left wing of the attacking third (19% of key passes faced) — that is the zone to shut off.
Context

Shape and tactics

Set pieces, strengths and weaknesses, and who they resemble.

Premium
Tactical Phase Read
The tactical idea behind each phase of this team's play, written from the same numbers shown above — what they do in build-up, how they progress, where they create, and how they defend it.
Subscribe →

Set-Piece Threat

How much of the goal threat is manufactured from dead balls — a route to goal that bypasses their open-play mechanism entirely.

Open play
44 g · 45.9 xG
Set pieces
7 g · 9.3 xG
Penalties
3 g
13% of goals come from set pieces (7 of 54), worth 9.3 xG from 114 shots. Set pieces are a secondary threat; the danger is in open play.

Where do Koln's corners go?

199 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 (87)

7%16%30%13%3%8%14%6%1%
Delivered into the box 98%Short 2%

Left side (112)

5%31%11%5%2%5%18%18%
Delivered into the box 96%Short 4%

Where opponents attack Koln from set pieces

113 shots from opponent corners against Koln, 16 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 (51)

10%14%25%16%18%18%
Shot 51Goals 5

Left flag (62)

11%11%31%31%6%10%
Shot 62Goals 11

Tactical Strengths & Weaknesses

Strengths (top-third vs league)
  • none stand out
Weaknesses (bottom-third vs league)
  • press intensity (PPDA) — 6.69 (33th pct)

Style Archetype & Similar Teams

Team archetype: Direct / counter high-press. Their closest stylistic matches in the league (cosine similarity on style metrics) — sides that play a similar way.

Borussia Monchengladba
94%
Hamburg
85%
Wolfsburg
71%
St. Pauli
61%
Heidenheim
58%

Performance vs Opponent Styles

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

Results

opponent typegoals for/ggoals against/gpoints/g
vs high-pressing opponents (13m)1.311.621.38
vs mid-block opponents (13m)1.381.770.92
vs low-block opponents (15m)1.472.000.67

Behaviour — how Koln actually play against each

opponent typeretentiondirectness (long balls)prog. passes/gxT/gbox entries/g
vs high-pressing opponents74%20%389.010.7
vs mid-block opponents77%17%4710.613.5
vs low-block opponents83%12%4711.514.7
Vision AI Model Insight
Facing a high press, retention drops to 74% vs 83% against a low block (9pt lower under pressure), and directness (long-ball share) sits at 20% when pressed vs 12% 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. Koln are balanced-territory with a aggressive high-press. Their attack creates 1.42 xG a game and they concede 1.50 a game. [Event-data based — no tracking, so off-ball structure and marking scheme need video.]
Squad

Players

Who drives the numbers.

Against Their Own Norm

For each of ten match numbers, the one match this season that departed furthest from Koln’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.

Koln
across 41 matches this season
Progressive carriesat Hamburg · 1-2 · 2026-09-19100avg 46
PPDAat Borussia Dortmund · 0-1 · 2025-10-2514.0avg 6.7
Expected goalsvs Werder Bremen · 3-1 · 2026-04-123.45avg 1.42
Progressive passesat Borussia Monchengladbach · 1-3 · 2025-11-0872avg 44
Final-third entriesat Borussia Monchengladbach · 1-3 · 2025-11-0864avg 36
Field tiltat Borussia Dortmund · 0-1 · 2025-10-2513%avg 47%
Pass accuracyvs Hoffenheim · 2-2 · 2026-02-2166%avg 78%
Defensive actionsat Eintracht Frankfurt · 0-1 · 2026-10-16201avg 143

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 Koln’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).

+17.9on-ball value, 12 matches
Where the value came from
Passes5659 actions+5.33
Carries1596 actions-0.17
Dribbles213 actions-0.40
Shots180 actions+10.38
Ball wins674 actions+2.79
Most on-ball value (OBV)ActionsOBVper 100
Saïd El Mala351+3.53+1.00
Luca Waldschmidt130+1.34+1.03
Thijs Dallinga39+1.30+3.34
Jakub Kaminski232+1.26+0.54
Marvin Schwäbe769+1.06+0.14
Ellyes Skhiri283+1.06+0.37
Tom Krauß526+0.85+0.16
Cenk Özkacar450+0.82+0.18
123456
Biggest single movesOBV
1  Luca Waldschmidt · shot+0.853
2  Thijs Dallinga · shot+0.748
3  Ellyes Skhiri · shot+0.665
4  Joël Schmied · pass+0.446
5  Mikey Moore · pass+0.404
6  Marvin Schwäbe · pass+0.321

Set Pieces by Player

Who delivers Koln’s dead balls and who finishes them this season. 199 corners taken, most by Jakub Kaminski (56); 114 shots from corners and free kicks, 7 goals, led by Ragnar Ache (2 goals, 2.32 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
Jakub Kaminski5650 (89%)1110.782—
Alessio Castro-Montes4438 (86%)1—0.041—
Ísak Bergmann Jóhannesson3429 (85%)4—0.163—
Ragnar Ache——1322.32——
Eric Martel——1011.07——
Saïd El Mala20 (0%)710.6711/1
Kristoffer Lund65 (83%)6—0.26——
Jahmai Simpson-Pusey——8—0.65——
Luca Waldschmidt99 (100%)2—0.0812/2
Linton Maina1715 (88%)1—0.03——
Jan Thielmann——6—0.29——
Cenk Özkacar——6—0.50——
Marius Bülter——6—0.36——
Florian Kainz1412 (86%)110.031—

Top Performers

Ranked by Vision AI Score — the same tactical behavioural evaluation each player's own report page shows (min 300 minutes). Coloured chips are that player's strongest capability blocks.

playerposminVision AI Scorecapability blocks
Marvin SchwäbeGK39506.3Shot-Stopping 63%
Tom KraußCM24465.2Ball-Carrying 60%Successful Dribble 53%Progression 52%Chance Creation 43%
Ísak JóhannessonCM24184.8Ball-Carrying 66%Progression 62%Successful Dribble 56%Chance Creation 55%
Luka LochoshviliCB6354.4Ball-Winning 47%Ball-Carrying 44%Progression 28%Aerial & Box Defending 21%
Jahmai Simpson-PuseyCB16282.8Ball-Carrying 66%Ball-Winning 35%Progression 31%Aerial & Box Defending 30%
Linton MainaAM16212.2Ball-Carrying 60%Chance Creation 47%Box Threat 7%

Top Creators

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

playerposminkey P /90box entriesxT
Jakub KaminskiM32151.347328.5
Ísak JóhannessonM24181.496032.5
Linton MainaM16211.614315.8
Alessio Castro-MontesD10902.064219.1
Marius BülterF21771.243511.5
Sebastian SebulonsenD29500.554326.3
Kristoffer LundD23960.683828.9
Tom KraußM24460.812521.5

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)
Sebastian SebulonsenFB295028 (14%)28 (43%)
Kristoffer LundFB239623 (30%)22 (32%)
Saïd El MalaW251020 (25%)16 (50%)

Defensive Volume Leaders

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

playerposmindef actionstacklesinterceptrecoveries
Tom KraußM24464849443159
Sebastian SebulonsenD29504095215122
Ragnar AcheF201438613438
Kristoffer LundD23963773826104
Cenk ÖzkacarD1924369332457
Jakub KaminskiM32153304523120
Jahmai Simpson-PuseyD1628283142654
Marius BülterF217727710754

Full Squad Data

Every player with 100+ minutes (31 players).

playerposappsmingoalsxGkey PprogdefxT
Marvin SchwäbeG41395000.0233611374.8
Jakub KaminskiM34321576.24813933028.5
Sebastian SebulonsenD35295001.91812940926.3
Saïd El MalaF4025101512.320542059.4
Tom KraußM33244601.1228148421.5
Ísak JóhannessonM38241821.64012524932.5
Kristoffer LundD33239600.91814637728.9
Marius BülterF39217755.3305527711.5
Ragnar AcheF33201489.827153864.7
Cenk ÖzkacarD25192400.8310436926.0
Jan ThielmannM30175632.2147221414.2
Jahmai Simpson-PuseyD18162800.643928314.5
Linton MainaM31162131.8296816515.8
Joël SchmiedD20125200.42351768.7
Rav van den BergD14121100.002619710.0
Alessio Castro-MontesD19109011.1259816919.1
Luca WaldschmidtM2885254.51324727.3
Denis HuseinbasicM1578300.3322806.1
Timo HübersD872800.00171615.7
Luka LochoshviliD763500.5191355.3
Dominique HeintzD942400.0115654.8
Mikey MooreM639200.9313412.8
Florian KainzM1536310.6924356.5
Gideon MensahD434600.1231635.8
Ellyes SkhiriM531101.0610584.7
Borna SosaD431100.1936607.1
Youssoupha NiangM1023600.435450.8
Thijs DallingaF315721.910230.2
Paul Okon-EngstlerM515400.125251.1
Felipe ChávezM715200.404131.2
Fynn SchentenF713300.01170.1

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.

Marvin Schwäbe
Goalkeeper · Goalkeeper
minutes3950
goals · xG0 · 0.0
prog passes /907.7
key passes /900.05
def actions /902.6
pass accuracy72%
Jakub Kaminski
Ball-winner / destroyer · Midfielder
minutes3215
goals · xG7 · 6.2
prog passes /903.9
key passes /901.34
def actions /909.2
pass accuracy74%
Sebastian Sebulonsen
Stopper / marker · Defender
minutes2950
goals · xG0 · 1.9
prog passes /903.9
key passes /900.55
def actions /9012.5
pass accuracy69%
Saïd El Mala
Poacher / finisher · Forward
minutes2510
goals · xG15 · 12.3
prog passes /901.9
key passes /900.72
def actions /907.4
pass accuracy71%
Tom Krauß
Ball-winner / destroyer · Midfielder
minutes2446
goals · xG0 · 1.1
prog passes /903.0
key passes /900.81
def actions /9017.8
pass accuracy84%
Ísak Jóhannesson
Ball-winner / destroyer · Midfielder
minutes2418
goals · xG2 · 1.6
prog passes /904.7
key passes /901.49
def actions /909.3
pass accuracy80%
Kristoffer Lund
Ball-playing defender · Defender
minutes2396
goals · xG0 · 0.9
prog passes /905.5
key passes /900.68
def actions /9014.2
pass accuracy74%
Marius Bülter
Ball-winner / destroyer · Midfielder
minutes2177
goals · xG5 · 5.3
prog passes /902.3
key passes /901.24
def actions /9011.5
pass accuracy64%
Ragnar Ache
Poacher / finisher · Forward
minutes2014
goals · xG8 · 9.8
prog passes /900.7
key passes /901.21
def actions /9017.2
pass accuracy61%

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 — bottom-third: press intensity (PPDA) (33th pct)
Sign a high-energy presser to lift defensive intensity. The data flags this because the press is passive (6.69, 33th percentile vs the league).
Need — below-average: progression /game (38th pct)
Sign a progressive passer / ball-carrier to break lines. The data flags this because progression is limited (44.12, 38th percentile vs the league).
Need — below-average: field tilt % (43th pct)
Sign a possession-controlling midfielder to win the territorial battle. The data flags this because they get pinned back (46.84, 43th percentile vs the league).
Vision AI · building report