Combining every season — this one is larger, give it a moment.
Team reportBundesliga 2026
Koln
A balanced-territory side, aggressive high-press out of possession, with a high-volume attack. Currently #12 of 18 on 8 points (2-2-3, 8:11). Tactical profile below is built from 7 matches of ball-by-ball event data, contextualised against the league.
In one read, what kind of team is Koln and how good was the season?
Read. Koln currently sit #12 of 18 on 8 points (2W-2D-3L, 1.14 pts/game), scoring 8 and conceding 11 for a goal difference of -3.
On the ball they are a balanced-territory team (48% field tilt, #10 in the league); off it they defend with a aggressive high-press (#6 of 18 for press intensity).
Across the 7 matches of event data, they create 1.55 xG/game (#10) and concede 1.43 (#5), a net of +0.12 xG/game — the underlying performance matches 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
9
Augsburg
7
2
3
2
17
13
+4
9
10
Hoffenheim
7
3
0
4
14
14
+0
9
11
Hamburg
7
3
0
4
6
16
-10
9
12
Koln
7
2
2
3
8
11
-3
8
13
Paderborn
7
2
2
3
6
9
-3
8
14
Eintracht Frankfurt
7
2
2
3
10
17
-7
8
15
Stuttgart
7
2
0
5
10
13
-3
6
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 -0.37, second-half +1.19. Their strongest period is 76-90+' (net +1.50 xG); their most dangerous period to face is 16-30' (net -0.73 xG against them).
xG Race — the season, match by match
Expected goals piling up over 7 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 (+0.8 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.
50
Build-Up
possession control from the back
22
Progression
moving the ball upfield with threat
39
Creation
manufacturing chances
56
Press
winning the ball high & hard
72
Solidity
resisting the opponent's threat
Genetic identity
Koln are defined most by their Solidity DNA (72/100 · strong at resisting the opponent's threat), and least by their Progression DNA (22/100). Percentiles vs the 18 teams in the league.
The Playstyle Wheel
Fifteen style metrics across five domains — Possess, Disrupt, Finish, Press, Defend — each a percentile against the rest of the league. Metrics marked ~proxy use the closest available event-data signal where the standard definition needs data this feed doesn't carry (explicit pressure events, offside/keeper-sweep incidents).
Possess
Deep buildup
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 48% field tilt, ranking #10 of 18 in the league — below the league mid-point, spending more of the game defending their own territory than dictating it, and their 1.14 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
49.6%
Chance creation
22.7%
Attacking transition
2.4%
Vision AI Model Insight
Progression is where Koln spends most of its time on the ball (50% of its time on the ball).
Passing Network
The question
Who actually plays with whom — the real on-pitch connections, not just average positions?
Every pass linked to its likely receiver (the next same-team touch), aggregated into player-pair combinations at each player's average position. Line width = how often the pair combines; colour = threat generated per pass (blue low → red high). Hover a line for the pair, or click a player to isolate their connections.
width = passes between the paircolour = xT per pass: low → high
Top 10 combinations
Lochoshvili ↔ Simpson-Pusey
147p · -1.2xT
Schwäbe ↔ Lochoshvili
120p · -0.5xT
Sosa ↔ Lochoshvili
93p · 0.1xT
Schwäbe ↔ Simpson-Pusey
88p · -0.5xT
Mensah ↔ Lochoshvili
62p · -0.7xT
Sebulonsen ↔ Simpson-Pusey
56p · -0.6xT
Krauß ↔ Simpson-Pusey
51p · -0.7xT
Skhiri ↔ Mensah
43p · -0.2xT
Lochoshvili ↔ Jóhannesson
38p · -0.2xT
Krauß ↔ Jóhannesson
35p · 0.6xT
Vision AI Model Insight
Busiest connection: Luka Lochoshvili ↔ Jahmai Simpson-Pusey (147 passes). Most dangerous per pass: Saïd El Mala ↔ Mikey Moore (0.04 xT/pass over 20 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 63 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
3340
ACTIONS
Carries
the ball moved with a player
962
ACTIONS
Vision AI Model Insight
Their single busiest zone is the centre of the defensive third (12.4% of all actions). 63% of actions run through the inside lanes (centre + both half-spaces) versus 37% down the two wings — an inside-dominant, half-space build. By third: 29% defensive, 51% middle, 20% 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 (47%). The bars under the pitch put the league’s share beside theirs.
Koln
47%23%29%
League
39%25%36%
Final-Third Entries
Every completed pass or carry that took the ball from outside the final third into it, by the channel it arrived in. Koln made 278 this season (39.7 a match); 40% 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
110
15.7
40%
28%
Left half-space
50
7.1
18%
17%
Centre
24
3.4
9%
14%
Right half-space
44
6.3
16%
15%
Right wing
50
7.1
18%
26%
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.
982build-up passes attempted
77%completed
26%reach the middle third
2%go long into the final third
Pass types
303CB · 40%
78Long Wide · 10%
14Long Central · 2%
269Wide → FB · 36%
92Central · 12%
Sequence success — build-up
112build-up sequences this season
8%succeed — reach the next third or a shot
46%turned over with nothing to show
CB303 · 40%
Long Wide78 · 10%
Long Central14 · 2%
Wide → FB269 · 36%
Central92 · 12%
◆ Vision AI Insight
Koln build predominantly down the left, with Schwäbe as the fulcrum (222 build-up passes). Only 29% of their build-up passes actually advance a third — the rest is lateral circulation, the hallmark of a possession-controlling side that circulates patiently in midfield before progressing. Their single most-repeated pattern is C def → LHS def (56×).
Pass Explorer
Every pass of Koln’s last 7 matches as an arrow, theirs first and the opponents’ with other team, each side attacking left to right (solid = completed, dashed grey = failed). Eight families per team — click any map to open it wide, then filter by channel or third, from one to another (or click a cell of the flow grids), by outcome, half, minute and passer. Channels: right wing, right half-space, centre, left half-space, left wing.
From channel
To channel
From third
To third
Outcome
Minutes
Channel to channel · click a cell
Third to third · click a cell
Progression4 sections
How They Progress — the mechanism
Not just how many progressive passes, but where they start, who plays them, and whether they actually penetrate. 278 progressive passes across 7 matches (39.7/game).
Origin lane
Right wing
21%
Right half-space
13%
Central
20%
Left half-space
9%
Left wing
37%
Progression is left-dominant: 33% right side · 20% central · 47% left.
Who generates it
Marvin Schwäbe
53 · 19%
Borna Sosa
36 · 13%
Gideon Mensah
31 · 11%
Ísak Jóhannesson
25 · 9%
Alessio Castro-Montes
22 · 8%
Top two — Marvin Schwäbe & Borna Sosa — account for 32% of all progression.
PROGRESSION
39.7 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. 32% 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-spacesterile
Left wingefficient
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.
1663progression passes attempted
87%completed
13%break into the final third
11%go backward into the defensive third
Pass types
497CB · 34%
53Long Wide · 4%
4Long Central · 0%
704Wide → FB · 49%
189Central · 13%
Sequence success — progression
156progression sequences this season
30%succeed — reach the next third or a shot
42%turned over with nothing to show
CB497 · 34%
Long Wide53 · 4%
Long Central4 · 0%
Wide → FB704 · 49%
Central189 · 13%
◆ Vision AI Insight
Koln progress predominantly down the left, with Lochoshvili the busiest midfield outlet (249 progression passes). Only 13% of passes that start in midfield actually break the final-third line — the rest (76%) is recycled back into midfield or (11%) 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 (118×) — press that lane and midfield progression stalls.
Decision Quality — not all completed passes are equal
A 90% pass completion can hide a sideways habit. Grading every pass by the threat it adds separates value creation from safe circulation.
Excellent (high xT gain)
12%
Progressive
1%
Neutral / sideways
50%
Safe / backward
37%
13% of passes are progressive-or-better; the remaining 87% 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?
206 progressive carries this season across the whole squad (21% of 962 tracked carries total), grouped into up to 4 spatial patterns by where each carry starts and ends together — not just its origin zone. Median 16m, longest 62m. Most active carrier: Simpson-Pusey (26). Filter to one player below.
First (57)Second (55)Third (50)Fourth (44)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.
110shots
10.9xG
0.10xG per shot
4big chances (0.3+ xG)
7goals
#10key passes / game in the league
#12box entries / game in the league
Kind of attack, then how the shot was set up
Shots
Goals
xG
xG / shot
Attacking transition25% of shots
28
2
3.81
0.14
Cross
1
0
0.07
0.07
Cut-back
4
0
0.65
0.16
Long ball
2
0
0.35
0.18
Pass into the box / short pass
13
2
1.77
0.14
Own creation (carry, dribble, rebound)
8
0
0.97
0.12
Settled attack48% of shots
53
4
4.33
0.08
Cross
10
2
1.28
0.13
Cut-back
2
0
0.33
0.17
Long ball
5
0
0.31
0.06
Pass into the box / short pass
24
0
1.36
0.06
Own creation (carry, dribble, rebound)
12
2
1.05
0.09
Set piece26% of shots
29
1
2.72
0.09
Corner
25
1
1.84
0.07
Free kick
3
0
0.08
0.03
Penalty
1
0
0.79
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
Ísak Jóhannesson
9
1.04
0
Mikey Moore
3
0.88
0
Linton Maina
7
0.62
2
Saïd El Mala
4
0.52
0
Borna Sosa
6
0.48
0
Who takes them
Player
Shots
xG
Goals
Thijs Dallinga
9
1.94
2
Saïd El Mala
26
1.93
2
Marius Bülter
9
1.86
0
Ragnar Ache
8
0.97
1
Mikey Moore
12
0.87
0
Vision AI Model Insight
The biggest source of Koln's chances, by xG, is settled attack (4.3 of 10.9 xG, 53 of 110 shots), mostly pass into the box / short pass. 28 of the 110 shots (25%) came within 15 seconds of winning the ball. 29 of the 110 shots came from set pieces; 7 goals from 10.9 xG (-3.9).
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
3CB · 4%
6Long Wide · 8%
6Long Central · 8%
31Wide → FB · 43%
26Central · 36%
Sequence success — final-third
148final-third sequences this season
27%succeed — produce a shot
34%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
CB3 · 4%
Long Wide6 · 8%
Long Central6 · 8%
Wide → FB31 · 43%
Central26 · 36%
Who creates — volume × threat
12Maina
9Sosa
8Jóhannesson
7Krauß
6Skhiri
5Bülter
4Castro-Montes
72key passes #10 of 18
12.9box entries/game #12 of 18
29.6final-3rd entries/game #10 of 18
1.55xG/game #10 of 18
7goals #14 of 18
-3.9finishing vs xG #18 of 18
◆ Vision AI Insight
Koln lean left — 44% of their key passes come from that flank and its half-space (32 of 72), most often from Sosa (9). They funnel chances into the c of the final third, most often via LW att → C att (8 key passes, most often from Maina). Maina is the primary source overall. They generate 1.55 xG a game and finish it wasteful — they under-perform their xG, led by Thijs Dallinga (2G), Saïd El Mala (2G), Ísak Jóhannesson (1G).
Crosses & Cutbacks — squad-wide
The question
How does Koln deliver into the box from wide areas, and what comes of it?
101 crosses: 30 found a team-mate (30%), 14 led to a shot (14%). 16 cut-backs: 8 found a team-mate (50%), 5 led to a shot (31%). 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?
101 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; 5 come from the half-spaces and 0 from the centre.
Territory
Crosses
Share
Led to a shot
byline · left wing
28
28%
6
advanced · left wing
23
23%
3
byline · right wing
19
19%
0
advanced · right wing
15
15%
4
deep · left wing
6
6%
1
deep · right wing
5
5%
0
advanced · right half-space
2
2%
0
deep · right half-space
1
1%
0
byline · right half-space
1
1%
0
byline · left half-space
1
1%
0
Finishing3 sections
Shot Map
110 shots for 10.9 xG (1.55/game, #10 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.
High-volume attack. 15.7 shots/game at 0.099 xG/shot. Box entries 13/game (#12). Threat created 10.2 xT/game (#12).
Goal Sequences
The move behind each of Koln’s 8 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 — 8 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
6 · 86%
Set pieces
1 · 14%
Penalties
0 · 0%
By finish
Right foot
4 · 57%
Left foot
2 · 29%
Header
1 · 14%
Vision AI Model Insight
7 goals scored. 86% come in open play; set pieces add 14%. 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
221
Failed forward pass
140
Failed take-on
71
Lost in the defensive third
226
Their weakest zone is the left wing in the defensive third: 45% of 143 pass attempts there are lost (64 times). Separately, 33% 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.
23
Pressing Index /100 · passive
Intensity30
PPDA, high-press share, high-zone actions
Height17
how high up the pitch they defend
Effectiveness17
high recoveries & high-regain share
Disruption31
opponent completion & progression allowed
Every defensive action & where it happens
Where they win the ball back — by lane
RW
19%
RHS
18%
C
20%
LHS
19%
LW
23%
By third (high / mid / low regain)
Def 50%
Mid 38%
Att 12%
Read
Ranked #6 of 18 for press intensity, this is a high press. Note the tension: 50% 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, 50% are won high up the pitch.
Every pressing metric, ranked in the league
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#6/18
lower = more intense; rank is what matters
Defensive actions / game
165
#13/18
High-press share
20%
#16/18
def actions won high up
Defensive-action height (0–100)
36.8
#15/18
avg pitch height of actions
High recoveries / game
18.7
#16/18
ball won in mid+att third
High-recovery share
50%
#14/18
share of recoveries won high
Opponent completion allowed
79%
#8/18
lower = more disruptive
Opponent progressive allowed
10.9%
#17/18
of opp passes, lower = better
Transition timing
This source carries per-event timestamps, so genuine transition metrics — counter-attack shots, press→shot conversions, time-to-recover — are measured; see the dedicated Transitions section below.
How long they keep it — passes and seconds until the ball is won
The question
Once the opponent has the ball, how many passes and how many seconds pass before Koln win it back with a defensive action?
Counted over the 209 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 4.9 opponent passes in the first half-hour and after 3.9 after the hour mark (sooner).
PASSES THEY GET
4.8passes
median 3 · before Koln win it
TIME THEY KEEP IT
13sec
median 7s · 29.9 wins per game
How many passes the opponent got in before the ball was won
45%
25%
18%
12%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
5.0
14s
15–30′
4.8
14s
30–45+′
6.8
18s
45–60′
4.8
13s
60–75′
3.7
12s
75–90+′
4.0
11s
average passesaverage seconds
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) 982 passes
nothing unusual in this zone
Width of use-0.7119.53
Wide-channel share-0.670.26
Side bias+0.331.40
Pass length+0.2223.51
Long-ball share+0.330.26
Forward gain per pass+0.1412.06
Backward-pass share+0.000.20
Deep-pass share+0.080.29
Completion-0.350.77
Seconds between passes+0.413.57
Side used
left 27% · central 51% · right 23%
Progression (middle third) 1,663 passes
left-sided, playing short
Width of use-0.4124.70
Wide-channel share-0.410.37
Side bias+1.536.10
Pass length-0.1317.04
Long-ball share-0.560.08
Forward gain per pass-0.712.53
Backward-pass share+0.510.36
Deep-pass share-0.980.11
Completion+0.350.87
Seconds between passes+0.203.29
Side used
left 42% · central 33% · right 25%
Final third 695 passes
left-sided
Width of use+0.4032.59
Wide-channel share+0.390.62
Side bias+0.915.93
Pass length+0.4616.18
Long-ball share+0.620.13
Forward gain per pass+0.281.42
Backward-pass share-0.290.37
Deep-pass share+0.300.10
Completion-0.250.68
Seconds between passes+0.423.71
Side used
left 50% · 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%
#5 of 18 · reaches final third in ≤3 passes
Patient build-up share26%
#11 of 18 · spells of 6+ passes
Tempo (possession length)18.0s
#11 of 18 · shorter = faster tempo
Passes per possession4.4
#9 of 18
Counter-attack shots /game1.39
#4 of 18 · shot within 12s of a regain in own half
Press→shot /game3.44
#8 of 18 · shot within 8s of a high regain
Speed to final third after regain12.8s
#14 of 18 · lower = faster break
Sequence success — attacking transition
15attacking transition sequences this season
33%succeed — produce a shot
40%turned over with nothing to show
Sequence success — defensive transition
25defensive transition sequences this season
48%succeed — reach the next third or a shot
12%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 (#5 of 18), against 26% built patiently over 6+ passes (#11 of 18). 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 18) — 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? 364 opponent progressive passes and 103 shots faced (10.0 xG against) across the season.
Opponent threat by lane (attacking into Koln)
Right wing
26%
Right half-space
11%
Central
20%
Left half-space
13%
Left wing
31%
Where the danger came from. Opponents generated most of their threat through the left 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 (73 total)
By situation
Open play
8 · 73%
Set pieces
3 · 27%
Penalties
0 · 0%
Vision AI Model Insight
11 goals conceded. 73% come in open play, and 27% from set pieces — a genuine defensive frailty at dead balls to fix on the training ground. 27% are headers — aerial defending is not where the damage comes from. Their supply line runs mainly through the right half-space of the attacking third (22% 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
6 g · 8.1 xG
Set pieces
1 g · 1.9 xG
Penalties
0 g
14% of goals come from set pieces (1 of 7), worth 1.9 xG from 28 shots. Set pieces are a secondary threat; the danger is in open play.
Where do Koln's corners go?
42 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 (18)
Delivered into the box 100%Short 0%
Left side (24)
Delivered into the box 92%Short 8%
Where opponents attack Koln from set pieces
20 shots from opponent corners against Koln, 3 conceded, split by the flag the corner came from — arrows from the flag, dots where the shot was taken (gold = goal), shaded by the % of that flag's shots in each zone. Tap or hover a shot for who took it, the minute and the match; filter to one match and the shading recomputes for just that game.
Right flag (5)
Shot 5Goals 2
Left flag (15)
Shot 15Goals 1
Tactical Strengths & Weaknesses
Strengths (top-third vs league)
xG conceded /game — 1.43 (72th pct)
press intensity (PPDA) — 4.82 (67th pct)
Weaknesses (bottom-third vs league)
progression /game — 39.71 (11th pct)
box entries /game — 12.86 (22th pct)
threat created /game — 10.21 (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.
Freiburg
76%
Eintracht Frankfurt
42%
Elversberg
35%
Hamburg
27%
Borussia Monchengladba
26%
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 (2m)
1.50
1.50
1.50
vs mid-block opponents (2m)
1.50
2.50
1.50
vs low-block opponents (3m)
0.67
1.00
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
75%
19%
37
9.4
11.0
vs mid-block opponents
79%
15%
40
9.0
12.5
vs low-block opponents
83%
12%
42
11.5
14.3
Vision AI Model Insight
Facing a high press, retention drops to 75% vs 83% against a low block (8pt lower under pressure), and directness (long-ball share) sits at 19% 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 1.55 xG a game and they concede 1.43 a game.
[Event-data based — no tracking, so off-ball structure and marking scheme need video.]
Every pass, carry, take-on, shot and ball win is worth the change it made to the chance of scoring minus the chance of conceding in the next 10 seconds. This is Koln’s last 7 matches added up: where the value came from, who produced it, and the six biggest single moves. OBV only measures what a player does with the ball: it shows who added the most on-ball value, not who was the most important player (defending off the ball, positioning and pressing are not in it).
+10.5on-ball value, 7 matches
Where the value came from
Passes3340 actions+2.18
Carries962 actions+0.50
Dribbles120 actions+0.03
Shots110 actions+6.15
Ball wins389 actions+1.67
Most on-ball value (OBV)
Actions
OBV
per 100
Saïd El Mala
206
+1.55
+0.75
Marvin Schwäbe
401
+1.38
+0.34
Thijs Dallinga
39
+1.30
+3.34
Ellyes Skhiri
283
+1.06
+0.37
Marius Bülter
123
+1.00
+0.82
Tom Krauß
396
+0.80
+0.20
Mikey Moore
242
+0.77
+0.32
Luka Lochoshvili
636
+0.63
+0.10
Biggest single moves
OBV
1 Thijs Dallinga · shot
+0.748
2 Ellyes Skhiri · shot
+0.665
3 Mikey Moore · pass
+0.404
4 Marvin Schwäbe · pass
+0.321
5 Saïd El Mala · shot
+0.309
6 Ragnar Ache · take-on
+0.264
Set Pieces by Player
Who delivers Koln’s dead balls and who finishes them this season. 42 corners taken, most by Linton Maina (14); 28 shots from corners and free kicks, 1 goals, led by Thijs Dallinga (1 goals, 0.12 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
Ísak Jóhannesson
9
9 (100%)
2
—
0.05
1
—
Linton Maina
14
12 (86%)
—
—
0.00
—
—
Alessio Castro-Montes
12
12 (100%)
—
—
0.00
—
—
Gideon Mensah
—
—
4
—
0.11
—
—
Luka Lochoshvili
—
—
4
—
0.28
—
—
Mikey Moore
1
1 (100%)
3
—
0.07
—
—
Borna Sosa
6
6 (100%)
1
—
0.02
1
—
Ragnar Ache
—
—
3
—
0.26
—
—
Thijs Dallinga
—
—
2
1
0.12
—
0/1
Ellyes Skhiri
—
—
3
—
0.42
—
—
Saïd El Mala
—
—
2
—
0.06
1
—
Jahmai Simpson-Pusey
—
—
2
—
0.27
—
—
Tom Krauß
—
—
1
—
0.22
—
—
Jan Thielmann
—
—
1
—
0.02
—
—
Top Performers
Ranked by Vision AI Score — the same tactical behavioural evaluation each player's own report page shows (min 189 minutes). Coloured chips are that player's strongest capability blocks.
Chance creation leaders — key passes /90 and box entries.
player
pos
min
key P /90
box entries
xT
Linton Maina
M
496
2.18
16
5.3
Ísak Jóhannesson
M
500
1.44
11
6.7
Borna Sosa
D
311
2.60
7
7.1
Ellyes Skhiri
M
311
1.74
9
4.7
Alessio Castro-Montes
D
343
1.05
8
4.3
Tom Krauß
M
571
1.10
5
3.9
Marius Bülter
F
414
1.09
5
0.8
Mikey Moore
M
392
0.69
7
2.8
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)
Borna Sosa
FB
311
16 (25%)
3 (33%)
Alessio Castro-Montes
FB
343
4 (25%)
5 (20%)
Gideon Mensah
FB
346
7 (14%)
1 (0%)
Saïd El Mala
W
416
1 (0%)
2 (50%)
Sebastian Sebulonsen
FB
301
2 (50%)
0
Defensive Volume Leaders
Who does the defensive work — tackles, interceptions, recoveries.
player
pos
min
def actions
tackles
intercept
recoveries
Tom Krauß
M
571
141
22
15
37
Luka Lochoshvili
D
635
135
11
7
23
Jahmai Simpson-Pusey
D
657
128
5
13
23
Marius Bülter
F
414
88
3
0
12
Ísak Jóhannesson
M
500
70
11
3
17
Linton Maina
M
496
68
9
2
20
Gideon Mensah
D
346
63
5
5
25
Borna Sosa
D
311
60
11
2
11
Full Squad Data
Every player with 100+ minutes (18 players).
player
pos
apps
min
goals
xG
key P
prog
def
xT
Marvin Schwäbe
G
7
657
0
0.0
1
53
17
12.0
Jahmai Simpson-Pusey
D
7
657
0
0.3
2
13
128
6.0
Luka Lochoshvili
D
7
635
0
0.5
1
9
135
5.3
Tom Krauß
M
7
571
0
0.6
7
11
141
3.9
Ísak Jóhannesson
M
7
500
1
0.3
8
25
70
6.7
Linton Maina
M
7
496
1
0.3
12
18
68
5.3
Saïd El Mala
F
6
416
2
1.9
3
7
39
1.3
Marius Bülter
F
7
414
0
1.9
5
4
88
0.8
Mikey Moore
M
6
392
0
0.9
3
13
41
2.8
Gideon Mensah
D
4
346
0
0.1
2
31
63
5.8
Alessio Castro-Montes
D
5
343
0
0.0
4
22
50
4.3
Ellyes Skhiri
M
5
311
0
0.4
6
10
58
4.7
Borna Sosa
D
4
311
0
0.1
9
36
60
7.1
Sebastian Sebulonsen
D
4
301
0
0.2
1
11
48
2.3
Ragnar Ache
F
4
198
1
1.0
3
0
48
0.3
Jan Thielmann
F
4
162
0
0.1
1
5
17
0.6
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
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
minutes657
goals · xG0 · 0.0
prog passes /907.3
key passes /900.14
def actions /902.3
pass accuracy74%
Jahmai Simpson-Pusey
Stopper / marker · Defender
minutes657
goals · xG0 · 0.3
prog passes /901.8
key passes /900.27
def actions /9017.5
pass accuracy89%
Luka Lochoshvili
Stopper / marker · Defender
minutes635
goals · xG0 · 0.5
prog passes /901.3
key passes /900.14
def actions /9019.1
pass accuracy91%
Tom Krauß
Ball-winner / destroyer · Midfielder
minutes571
goals · xG0 · 0.6
prog passes /901.7
key passes /901.10
def actions /9022.2
pass accuracy80%
Ísak Jóhannesson
Ball-winner / destroyer · Midfielder
minutes500
goals · xG1 · 0.3
prog passes /904.5
key passes /901.44
def actions /9012.6
pass accuracy77%
Linton Maina
Advanced creator · Attacking mid
minutes496
goals · xG1 · 0.3
prog passes /903.3
key passes /902.18
def actions /9012.3
pass accuracy76%
Saïd El Mala
Poacher / finisher · Forward
minutes416
goals · xG2 · 1.9
prog passes /901.5
key passes /900.65
def actions /908.4
pass accuracy71%
Marius Bülter
Poacher / finisher · Forward
minutes414
goals · xG0 · 1.9
prog passes /900.9
key passes /901.09
def actions /9019.1
pass accuracy49%
Mikey Moore
Ball-winner / destroyer · Midfielder
minutes392
goals · xG0 · 0.9
prog passes /903.0
key passes /900.69
def actions /909.4
pass accuracy81%
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: progression /game (11th pct)
Sign a progressive passer / ball-carrier to break lines. The data flags this because progression is limited (39.71, 11th percentile vs the league).
Need — bottom-third: box entries /game (22th pct)
Sign a penetrative wide player or runner to create box entries. The data flags this because few entries into the box (12.86, 22th percentile vs the league).
Need — bottom-third: threat created /game (33th pct)
Sign a threat-generating creator between the lines. The data flags this because little threat is manufactured (10.21, 33th percentile vs the league).