Combining every season — this one is larger, give it a moment.
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.
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.
#
team
P
W
D
L
GF
GA
GD
Pts
12
Union Berlin
41
11
12
18
50
76
-26
45
13
Werder Bremen
41
11
11
19
52
74
-22
44
14
Borussia Monchengladbach
41
10
11
20
53
75
-22
41
15
Koln
41
9
13
19
57
74
-17
40
16
Wolfsburg
34
7
8
19
45
69
-24
29
17
Heidenheim
34
6
8
20
41
72
-31
26
18
St. Pauli
34
6
8
20
29
60
-31
26
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)
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.
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).
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.
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.
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.
Phase
Share of the time
Build-up
25.4%
Progression
48.9%
Chance creation
23.8%
Attacking transition
2.0%
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.
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
Carries
the ball moved with a player
4828
ACTIONS
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.
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.
Channel
Entries
Per match
Share
League share
Left wing
513
12.5
34%
27%
Left half-space
299
7.3
20%
16%
Centre
168
4.1
11%
14%
Right half-space
185
4.5
12%
15%
Right wing
331
8.1
22%
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%
Long Wide458 · 11%
Long Central98 · 2%
Wide → FB1696 · 40%
Central584 · 14%
◆ 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.
From channel
To channel
From third
To third
Outcome
Minutes
Channel to channel · click a cell
Third to third · click a cell
Progression4 sections
How They Progress — the mechanism
Not just how many progressive passes, but where they start, who plays them, and whether they actually penetrate. 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%
Long Wide277 · 4%
Long Central59 · 1%
Wide → FB3614 · 47%
Central1197 · 16%
◆ 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.
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
Kind of attack, then how the shot was set up
Shots
Goals
xG
xG / shot
Attacking transition27% of shots
154
20
20.70
0.13
Cross
11
2
2.16
0.20
Cut-back
11
1
1.24
0.11
Long ball
8
1
1.26
0.16
Pass into the box / short pass
70
8
9.67
0.14
Own creation (carry, dribble, rebound)
54
8
6.36
0.12
Settled attack52% of shots
295
24
25.23
0.09
Cross
63
10
7.32
0.12
Cut-back
11
1
1.86
0.17
Long ball
12
1
1.09
0.09
Pass into the box / short pass
130
7
9.31
0.07
Own creation (carry, dribble, rebound)
79
5
5.65
0.07
Set piece21% of shots
118
10
12.49
0.11
Corner
101
6
8.91
0.09
Free kick
13
1
0.40
0.03
Penalty
4
3
3.18
0.79
Every attempt to create — and what came of it
Every pass into the box, every cut-back and every pass before a shot. Red led to a shot, gold to a goal; grey ones were tried but no shot came: solid found a team-mate, dashed lost the ball. Filter by outcome, type or player; tap an arrow for the detail.
Who sets the shots up
Player
Set-ups
xG created
Goals
Marius Bülter
32
5.61
4
Jakub Kaminski
36
4.19
3
Saïd El Mala
22
2.90
3
Ragnar Ache
25
2.58
4
Linton Maina
24
2.51
2
Who takes them
Player
Shots
xG
Goals
Saïd El Mala
101
12.29
15
Ragnar Ache
60
9.78
8
Jakub Kaminski
63
6.18
7
Marius Bülter
51
5.30
5
Luca Waldschmidt
28
4.53
5
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%
Long Wide17 · 5%
Long Central39 · 11%
Wide → FB127 · 35%
Central148 · 40%
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.
Territory
Crosses
Share
Led to a shot
byline · left wing
178
28%
24
byline · right wing
158
25%
17
advanced · left wing
117
18%
14
advanced · right wing
91
14%
10
deep · left wing
36
6%
1
deep · right wing
25
4%
2
byline · right half-space
10
2%
3
advanced · right half-space
7
1%
0
advanced · left half-space
5
1%
1
byline · left half-space
5
1%
0
deep · left half-space
3
0%
1
deep · right half-space
2
0%
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
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.
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
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#14/21
lower = more intense; rank is what matters
Defensive actions / game
143
#14/21
High-press share
20%
#12/21
def actions won high up
Defensive-action height (0–100)
36.2
#15/21
avg pitch height of actions
High recoveries / game
18.9
#20/21
ball won in mid+att third
High-recovery share
51%
#17/21
share of recoveries won high
Opponent completion allowed
81%
#13/21
lower = more disruptive
Opponent progressive allowed
9.4%
#9/21
of opp passes, lower = better
Transition timing
This source carries per-event timestamps, so genuine transition metrics — counter-attack shots, press→shot conversions, time-to-recover — are measured; see the dedicated Transitions section below.
How long they keep it — passes and seconds until the ball is won
The question
Once the opponent has the ball, how many passes and how many seconds pass before 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
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
Phase
Segments
Avg length
Avg actions
Ends in shot
Turned over
Build-up
2,095
4.3s
2.7
0.0%
51.0%
Progression
2,505
6.9s
3.9
0.4%
46.5%
Chance creation
1,465
5.7s
3.2
27.7%
52.9%
Attacking transition
99
7.0s
4.2
8.1%
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.
Route
Times
Ends in shot
build_up
837
0.0%
progression
607
0.0%
chance_creation
368
36.4%
build_up → progression
339
0.0%
progression → chance_creation
300
30.7%
build_up → progression → chance_creation
170
27.1%
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
Every opponent key pass against them (388 total)
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.
The tactical idea behind each phase of this team's play, written from the same numbers shown above — what they do in build-up, how they progress, where they create, and how they defend it.
How much of the goal threat is manufactured from dead balls — a route to goal that bypasses their open-play mechanism entirely.
Open play
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)
Delivered into the box 98%Short 2%
Left side (112)
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)
Shot 51Goals 5
Left flag (62)
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 type
goals for/g
goals against/g
points/g
vs high-pressing opponents (13m)
1.31
1.62
1.38
vs mid-block opponents (13m)
1.38
1.77
0.92
vs low-block opponents (15m)
1.47
2.00
0.67
Behaviour — how Koln actually play against each
opponent type
retention
directness (long balls)
prog. passes/g
xT/g
box entries/g
vs high-pressing opponents
74%
20%
38
9.0
10.7
vs mid-block opponents
77%
17%
47
10.6
13.5
vs low-block opponents
83%
12%
47
11.5
14.7
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.]
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.
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)
Actions
OBV
per 100
Saïd El Mala
351
+3.53
+1.00
Luca Waldschmidt
130
+1.34
+1.03
Thijs Dallinga
39
+1.30
+3.34
Jakub Kaminski
232
+1.26
+0.54
Marvin Schwäbe
769
+1.06
+0.14
Ellyes Skhiri
283
+1.06
+0.37
Tom Krauß
526
+0.85
+0.16
Cenk Özkacar
450
+0.82
+0.18
Biggest single moves
OBV
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.
Player
Corners taken
Into the box
Set-piece shots
Goals
xG
Free-kick shots
Penalties
Jakub Kaminski
56
50 (89%)
11
1
0.78
2
—
Alessio Castro-Montes
44
38 (86%)
1
—
0.04
1
—
Ísak Bergmann Jóhannesson
34
29 (85%)
4
—
0.16
3
—
Ragnar Ache
—
—
13
2
2.32
—
—
Eric Martel
—
—
10
1
1.07
—
—
Saïd El Mala
2
0 (0%)
7
1
0.67
1
1/1
Kristoffer Lund
6
5 (83%)
6
—
0.26
—
—
Jahmai Simpson-Pusey
—
—
8
—
0.65
—
—
Luca Waldschmidt
9
9 (100%)
2
—
0.08
1
2/2
Linton Maina
17
15 (88%)
1
—
0.03
—
—
Jan Thielmann
—
—
6
—
0.29
—
—
Cenk Özkacar
—
—
6
—
0.50
—
—
Marius Bülter
—
—
6
—
0.36
—
—
Florian Kainz
14
12 (86%)
1
1
0.03
1
—
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.
Chance creation leaders — key passes /90 and box entries.
player
pos
min
key P /90
box entries
xT
Jakub Kaminski
M
3215
1.34
73
28.5
Ísak Jóhannesson
M
2418
1.49
60
32.5
Linton Maina
M
1621
1.61
43
15.8
Alessio Castro-Montes
D
1090
2.06
42
19.1
Marius Bülter
F
2177
1.24
35
11.5
Sebastian Sebulonsen
D
2950
0.55
43
26.3
Kristoffer Lund
D
2396
0.68
38
28.9
Tom Krauß
M
2446
0.81
25
21.5
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.
player
pos
min
crosses (completed)
cutbacks (completed)
Sebastian Sebulonsen
FB
2950
28 (14%)
28 (43%)
Kristoffer Lund
FB
2396
23 (30%)
22 (32%)
Saïd El Mala
W
2510
20 (25%)
16 (50%)
Defensive Volume Leaders
Who does the defensive work — tackles, interceptions, recoveries.
player
pos
min
def actions
tackles
intercept
recoveries
Tom Krauß
M
2446
484
94
43
159
Sebastian Sebulonsen
D
2950
409
52
15
122
Ragnar Ache
F
2014
386
13
4
38
Kristoffer Lund
D
2396
377
38
26
104
Cenk Özkacar
D
1924
369
33
24
57
Jakub Kaminski
M
3215
330
45
23
120
Jahmai Simpson-Pusey
D
1628
283
14
26
54
Marius Bülter
F
2177
277
10
7
54
Full Squad Data
Every player with 100+ minutes (31 players).
player
pos
apps
min
goals
xG
key P
prog
def
xT
Marvin Schwäbe
G
41
3950
0
0.0
2
336
113
74.8
Jakub Kaminski
M
34
3215
7
6.2
48
139
330
28.5
Sebastian Sebulonsen
D
35
2950
0
1.9
18
129
409
26.3
Saïd El Mala
F
40
2510
15
12.3
20
54
205
9.4
Tom Krauß
M
33
2446
0
1.1
22
81
484
21.5
Ísak Jóhannesson
M
38
2418
2
1.6
40
125
249
32.5
Kristoffer Lund
D
33
2396
0
0.9
18
146
377
28.9
Marius Bülter
F
39
2177
5
5.3
30
55
277
11.5
Ragnar Ache
F
33
2014
8
9.8
27
15
386
4.7
Cenk Özkacar
D
25
1924
0
0.8
3
104
369
26.0
Jan Thielmann
M
30
1756
3
2.2
14
72
214
14.2
Jahmai Simpson-Pusey
D
18
1628
0
0.6
4
39
283
14.5
Linton Maina
M
31
1621
3
1.8
29
68
165
15.8
Joël Schmied
D
20
1252
0
0.4
2
35
176
8.7
Rav van den Berg
D
14
1211
0
0.0
0
26
197
10.0
Alessio Castro-Montes
D
19
1090
1
1.1
25
98
169
19.1
Luca Waldschmidt
M
28
852
5
4.5
13
24
72
7.3
Denis Huseinbasic
M
15
783
0
0.3
3
22
80
6.1
Timo Hübers
D
8
728
0
0.0
0
17
161
5.7
Luka Lochoshvili
D
7
635
0
0.5
1
9
135
5.3
Dominique Heintz
D
9
424
0
0.0
1
15
65
4.8
Mikey Moore
M
6
392
0
0.9
3
13
41
2.8
Florian Kainz
M
15
363
1
0.6
9
24
35
6.5
Gideon Mensah
D
4
346
0
0.1
2
31
63
5.8
Ellyes Skhiri
M
5
311
0
1.0
6
10
58
4.7
Borna Sosa
D
4
311
0
0.1
9
36
60
7.1
Youssoupha Niang
M
10
236
0
0.4
3
5
45
0.8
Thijs Dallinga
F
3
157
2
1.9
1
0
23
0.2
Paul Okon-Engstler
M
5
154
0
0.1
2
5
25
1.1
Felipe Chávez
M
7
152
0
0.4
0
4
13
1.2
Fynn Schenten
F
7
133
0
0.0
1
1
7
0.1
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).