Combining every season — this one is larger, give it a moment.
Team reportBundesliga 2025/26
Hamburg
A territorially-dominated side, aggressive high-press out of possession, with a moderate attack. Finished #13 of 18 on 38 points (9-11-14, 40:54). Tactical profile below is built from 34 matches of ball-by-ball event data, contextualised against the league.
In one read, what kind of team is Hamburg and how good was the season?
Read. Hamburg finished #13 of 18 on 38 points (9W-11D-14L, 1.12 pts/game), scoring 40 and conceding 54 for a goal difference of -14.
On the ball they are a territorially-dominated team (40% field tilt, #17 in the league); off it they defend with a aggressive high-press (#15 of 18 for press intensity).
Across the 34 matches of event data, they create 1.21 xG/game (#15) and concede 1.63 (#15), a net of -0.42 xG/game — the underlying performance matches the actual goal difference.
Season Context — the league table
The question
Where did Hamburg actually finish, and against whom?
The authoritative final table (full season), centred on Hamburg.
#
team
P
W
D
L
GF
GA
GD
Pts
10
Mainz
34
10
10
14
44
53
-9
40
11
Union Berlin
34
10
9
15
44
58
-14
39
12
Borussia Monchengladbach
34
9
11
14
42
53
-11
38
13
Hamburg
34
9
11
14
40
54
-14
38
14
Koln
34
7
11
16
49
63
-14
32
15
Werder Bremen
34
8
8
18
37
60
-23
32
16
Wolfsburg
34
7
8
19
45
69
-24
29
Match Momentum — when they create and concede danger
The question
Across a whole game, which periods does Hamburg 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
Hamburg tend to fade after a strong start — first-half net xG -5.06, second-half -9.10. Their strongest period is 31-45' (net +1.06 xG); their most dangerous period to face is 61-75' (net -5.12 xG against them).
xG Race — the season, match by match
Expected goals piling up over 34 matches: Hamburg 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 (-14.2 xG). Hover a point for that match.
HamburgOpponents against Hamburg
Tactical DNA
The football question
In five genes, what is Hamburg'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.
28
Build-Up
possession control from the back
3
Progression
moving the ball upfield with threat
20
Creation
manufacturing chances
11
Press
winning the ball high & hard
17
Solidity
resisting the opponent's threat
Genetic identity
Hamburg are defined most by their Build-Up DNA (28/100 · limited at possession control from the back), and least by their Progression DNA (3/100). 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
62%
Possession
19%
Field tilt
10%
Disrupt
Central progression ~proxy
29%
Dribble tendency
86%
Progressive passes
10%
Finish
Final-third patience
0%
Shot quality
14%
Open-play xG
14%
Press
Forward press ~proxy
14%
High regains
5%
Pressing efficiency ~proxy
0%
Defend
Def-mid press ~proxy
0%
High line ~proxy
0%
PAdj defensive actions
29%
Hamburg is defined most by Disrupt (41% in the league) and weakest on Press (6%). 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 Hamburg actually control, relative to the rest of the league?
Field tilt = share of final-third touches between the two sides across Hamburg'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; Hamburg highlighted. Hover any dot for the team.
Vision AI Model Insight
Hamburg post 40% field tilt, ranking #17 of 18 in the league — below the league mid-point, spending more of the game defending their own territory than dictating it, and their 1.12 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 Hamburg has the ball, how much of that time is spent building, progressing and creating?
The share of Hamburg'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
32.4%
Progression
46.8%
Chance creation
18.1%
Attacking transition
2.7%
Vision AI Model Insight
Progression is where Hamburg spends most of its time on the ball (47% 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
Fernandes ↔ Vuskovic
547p · 19.7xT
Fernandes ↔ Torunarigha
432p · 12.9xT
Fernandes ↔ Remberg
353p · 20.6xT
Remberg ↔ Vuskovic
299p · 6.7xT
Fernandes ↔ Omari
219p · 8.1xT
Torunarigha ↔ Muheim
218p · 10.4xT
Torunarigha ↔ Vuskovic
200p · 4.4xT
Fernandes ↔ Elfadli
199p · 8.8xT
Elfadli ↔ Vuskovic
194p · 3.4xT
Capaldo ↔ Vuskovic
190p · 8.9xT
Vision AI Model Insight
Busiest connection: Daniel Heuer Fernandes ↔ Luka Vuskovic (547 passes). Most dangerous per pass: Daniel Heuer Fernandes ↔ Miro Muheim (0.08 xT/pass over 156 passes) — the combination that hurts opponents most when it fires, not just the one used most.
Touch Density — where Hamburg had the ball
Every touch across 306 matches. Brighter means more touches.
Which Zones Do They Attack Through?
The question
Across the whole pitch, where does Hamburg 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
14467
ACTIONS
Carries
the ball moved with a player
3420
ACTIONS
Vision AI Model Insight
Their single busiest zone is the centre of the defensive third (15.1% 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: 36% defensive, 48% middle, 17% 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. Hamburg leaned on their left (41%). The bars under the pitch put the league’s share beside theirs.
Hamburg
41%20%39%
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. Hamburg made 1071 this season (31.5 a match); 30% 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
320
9.4
30%
27%
Left half-space
161
4.7
15%
16%
Centre
129
3.8
12%
14%
Right half-space
163
4.8
15%
15%
Right wing
298
8.8
28%
28%
Build-up2 sections
Build-Up — the pass map
The football question
How does Hamburg 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.
5183build-up passes attempted
79%completed
27%reach the middle third
2%go long into the final third
Pass types
1645CB · 40%
326Long Wide · 8%
61Long Central · 1%
1520Wide → FB · 37%
551Central · 13%
Sequence success — build-up
1045build-up sequences this season
8%succeed — reach the next third or a shot
44%turned over with nothing to show
CB1645 · 40%
Long Wide326 · 8%
Long Central61 · 1%
Wide → FB1520 · 37%
Central551 · 13%
◆ Vision AI Insight
Hamburg build predominantly down the left, with Fernandes as the fulcrum (1258 build-up passes). Only 28% 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 (262×).
Pass Explorer
Every pass of Hamburg’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. 1339 progressive passes across 34 matches (39.4/game).
Origin lane
Right wing
25%
Right half-space
13%
Central
19%
Left half-space
14%
Left wing
29%
Progression is left-dominant: 38% right side · 19% central · 43% left.
Who generates it
Miro Muheim
208 · 16%
Daniel Heuer Fernandes
188 · 14%
Fábio Vieira
112 · 8%
Nicolás Capaldo
89 · 7%
Luka Vuskovic
88 · 7%
Top two — Miro Muheim & Daniel Heuer Fernandes — account for 30% of all progression.
PROGRESSION
39.4 prog passes/game
mostly left
→
PENETRATION
31% reach final third
21% reach the box
→
THREAT
the other 69% is recycled
progress ≠ danger
What came of it.31% of progressive passes reached the final third and 21% the box; the rest was recycled in front of the block. 30% of the progression ran through two players, mostly down the left side.
Which lanes actually create the threat
Share of passes played from each lane against its share of the threat (xT) they add. Efficient = more threat than volume, sterile = lots of the ball, little damage.
Per lane — volume vs threat
Right wingbalanced
Right half-spacebalanced
Centralbalanced
Left half-spacebalanced
Left wingefficient
Progression — the pass map
The football question
Once Hamburg 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.
6843progression passes attempted
83%completed
14%break into the final third
15%go backward into the defensive third
Pass types
2290CB · 40%
238Long Wide · 4%
46Long Central · 1%
2419Wide → FB · 42%
702Central · 12%
Sequence success — progression
1619progression sequences this season
22%succeed — reach the next third or a shot
45%turned over with nothing to show
CB2290 · 40%
Long Wide238 · 4%
Long Central46 · 1%
Wide → FB2419 · 42%
Central702 · 12%
◆ Vision AI Insight
Hamburg progress predominantly down the left, with Remberg the busiest midfield outlet (820 progression passes). Only 14% of passes that start in midfield actually break the final-third line — the rest (71%) is recycled back into midfield or (15%) 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 (314×) — 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)
13%
Progressive
1%
Neutral / sideways
50%
Safe / backward
36%
14% of passes are progressive-or-better; the remaining 86% keep possession without advancing it. A control-first side — safe in build-up, reliant on a few players for the incision.
Where does Hamburg actually progress the ball with their feet, not a pass?
816 progressive carries this season across the whole squad (24% of 3420 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 64m. Most active carrier: Vuskovic (96). Filter to one player below.
First (279)Second (227)Third (172)Fourth (138)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 Hamburg'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.
418shots
41.3xG
0.10xG per shot
18big chances (0.3+ xG)
40goals
#11key passes / game in the league
#17box entries / game in the league
Kind of attack, then how the shot was set up
Shots
Goals
xG
xG / shot
Attacking transition26% of shots
110
15
11.98
0.11
Cross
12
0
1.63
0.14
Cut-back
9
1
1.31
0.15
Long ball
15
5
1.88
0.13
Pass into the box / short pass
56
7
5.69
0.10
Own creation (carry, dribble, rebound)
18
2
1.47
0.08
Settled attack58% of shots
241
17
20.10
0.08
Cross
58
5
4.86
0.08
Cut-back
18
2
1.98
0.11
Long ball
12
1
0.72
0.06
Pass into the box / short pass
91
4
7.33
0.08
Own creation (carry, dribble, rebound)
62
5
5.22
0.08
Set piece16% of shots
67
8
9.18
0.14
Corner
47
2
4.09
0.09
Free kick
14
2
0.33
0.02
Penalty
6
4
4.76
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
Jean-Luc Dompé
31
3.18
0
William Mikelbrencis
19
3.17
4
Fábio Vieira
32
3.11
5
Miro Muheim
29
2.01
3
Nicolás Capaldo
22
1.72
3
Who takes them
Player
Shots
xG
Goals
Ransford Königsdörffer
65
7.35
5
Rayan Philippe
39
5.47
5
Fábio Vieira
59
4.49
7
Luka Vuskovic
32
4.25
6
Robert Glatzel
16
2.66
3
Vision AI Model Insight
The biggest source of Hamburg's chances, by xG, is settled attack (20.1 of 41.3 xG, 241 of 418 shots), mostly pass into the box / short pass. 110 of the 418 shots (26%) came within 15 seconds of winning the ball. 67 of the 418 shots came from set pieces; 40 goals from 41.3 xG (-1.3).
Chance Creation — the pass map
The football question
How does Hamburg 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
42CB · 14%
21Long Wide · 7%
33Long Central · 11%
89Wide → FB · 31%
105Central · 36%
Sequence success — final-third
903final-third sequences this season
26%succeed — produce a shot
37%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
CB42 · 14%
Long Wide21 · 7%
Long Central33 · 11%
Wide → FB89 · 31%
Central105 · 36%
Who creates — volume × threat
40Muheim
37Vieira
33Dompé
23Lokonga
21Capaldo
17Königsdörffer
16Mikelbrencis
290key passes #11 of 18
10.5box entries/game #17 of 18
25.1final-3rd entries/game #17 of 18
1.21xG/game #15 of 18
40goals #16 of 18
-1.3finishing vs xG #16 of 18
◆ Vision AI Insight
Hamburg lean right — 40% of their key passes come from that flank and its half-space (115 of 290), most often from Vieira (19). They funnel chances into the c of the final third, most often via LW att → C att (35 key passes, most often from Dompé). Muheim is the primary source overall. They generate 1.21 xG a game and finish it wasteful — they under-perform their xG, led by Fábio Vieira (7G), Luka Vuskovic (6G), Rayan Philippe (5G).
Crosses & Cutbacks — squad-wide
The question
How does Hamburg deliver into the box from wide areas, and what comes of it?
449 crosses: 152 found a team-mate (34%), 68 led to a shot (15%). 70 cut-backs: 40 found a team-mate (57%), 28 led to a shot (40%). 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 Hamburg actually cross from?
449 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. 22% come from one territory, the advanced left wing; 15 come from the half-spaces and 0 from the centre.
Territory
Crosses
Share
Led to a shot
advanced · left wing
101
22%
20
byline · left wing
97
22%
18
byline · right wing
91
20%
8
advanced · right wing
82
18%
13
deep · left wing
32
7%
2
deep · right wing
31
7%
5
advanced · right half-space
5
1%
0
byline · right half-space
3
1%
1
byline · left half-space
3
1%
1
deep · right half-space
2
0%
0
deep · left half-space
2
0%
0
Finishing3 sections
Shot Map
418 shots for 41.3 xG (1.21/game, #15 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.
Moderate attack. 12.3 shots/game at 0.099 xG/shot. Box entries 10/game (#17). Threat created 9.4 xT/game (#18).
Goal Sequences
The move behind each of Hamburg’s 40 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 — 40 goals traced.
How Do They Score?
The question
Where do the goals actually come from — open play, dead balls, which foot?
Every goal Hamburg scored across the loaded matches, broken down by how it was created and finished.
By situation
Open play
32 · 80%
Set pieces
4 · 10%
Penalties
4 · 10%
By finish
Right foot
22 · 55%
Left foot
15 · 38%
Header
3 · 8%
Vision AI Model Insight
40 goals scored. 80% come in open play; set pieces add 10%. 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
821
Failed forward pass
555
Failed take-on
306
Lost in the defensive third
1080
Their weakest zone is the right wing in the attacking third: 36% of 722 pass attempts there are lost (260 times). Separately, 27% 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 Hamburg 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.
11
Pressing Index /100 · passive
Intensity7
PPDA, high-press share, high-zone actions
Height0
how high up the pitch they defend
Effectiveness3
high recoveries & high-regain share
Disruption39
opponent completion & progression allowed
Every defensive action & where it happens
Where they win the ball back — by lane
RW
17%
RHS
22%
C
22%
LHS
20%
LW
19%
By third (high / mid / low regain)
Def 59%
Mid 31%
Att 11%
Read
Ranked #15 of 18 for press intensity, this is a high press. Note the tension: 59% of their raw defensive actions still occur in their own third — a possession effect, not a passive one. Dominating the ball (16% high-press share) means that when they do defend, it is often deep. Of recoveries, 47% are won high up the pitch.
Every pressing metric, ranked in the league
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#15/18
lower = more intense; rank is what matters
Defensive actions / game
141
#9/18
High-press share
16%
#17/18
def actions won high up
Defensive-action height (0–100)
32.9
#18/18
avg pitch height of actions
High recoveries / game
18.1
#18/18
ball won in mid+att third
High-recovery share
47%
#17/18
share of recoveries won high
Opponent completion allowed
81%
#12/18
lower = more disruptive
Opponent progressive allowed
9.6%
#10/18
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 Hamburg win it back with a defensive action?
Counted over the 1006 opposition possessions that Hamburg 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 Hamburg step in. Hamburg win it after 5.8 opponent passes in the first half-hour and after 4.5 after the hour mark (sooner).
PASSES THEY GET
5.3passes
median 3 · before Hamburg win it
TIME THEY KEEP IT
15sec
median 7s · 29.6 wins per game
How many passes the opponent got in before the ball was won
40%
27%
17%
16%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
5.6
15s
15–30′
6.1
18s
30–45+′
5.5
15s
45–60′
5.3
16s
60–75′
4.8
14s
75–90+′
4.4
13s
average passesaverage seconds
Phases of play
Every moment of the match read twice — what Hamburg 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.
6,001phase segments analysed
7.0smedian time before the opposition engage
2.0their passes per defensive action when pressed
#18of 18 for share of defending spent pressing high
High press 4.6%Mid block 10.2%Low block 59.6%Unopposed 25.7%
What opponents do against Hamburg
The mirror image, and the part no other section can show: whether teams come
out to press Hamburg 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 8.5%Mid block 15.4%Low block 47.0%Unopposed 29.1%
Which phases actually produce
Phase
Segments
Avg length
Avg actions
Ends in shot
Turned over
Build-up
2,151
5.0s
3.0
0.2%
52.3%
Progression
2,465
6.3s
3.7
0.5%
49.1%
Chance creation
1,256
4.8s
2.8
29.5%
51.4%
Attacking transition
129
6.9s
4.0
3.9%
41.1%
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
909
0.0%
progression
649
0.2%
build_up → progression
341
0.0%
chance_creation
278
36.0%
progression → chance_creation
275
33.8%
build_up → progression → chance_creation
167
37.1%
Attitude in each third
Every other style number here says how much Hamburg 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 Hamburg 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) 5,183 passes
nothing unusual in this zone
Width of use-0.6119.75
Wide-channel share-0.490.27
Side bias+0.110.59
Pass length+0.0923.02
Long-ball share+0.110.24
Forward gain per pass+0.0411.61
Backward-pass share+0.190.21
Deep-pass share+0.120.30
Completion-0.080.79
Seconds between passes+0.293.50
Side used
left 26% · central 49% · right 25%
Progression (middle third) 6,843 passes
nothing unusual in this zone
Width of use+0.1125.80
Wide-channel share+0.020.41
Side bias+0.361.47
Pass length+0.6418.12
Long-ball share+0.440.12
Forward gain per pass-0.083.49
Backward-pass share+0.390.35
Deep-pass share+0.560.19
Completion-0.360.82
Seconds between passes+0.823.56
Side used
left 38% · central 29% · right 34%
Final third 2,441 passes
longer passing
Width of use+0.4732.78
Wide-channel share+0.370.62
Side bias+0.090.20
Pass length+0.9216.90
Long-ball share+0.800.14
Forward gain per pass+0.081.15
Backward-pass share-0.010.39
Deep-pass share+0.580.11
Completion-0.500.66
Seconds between passes+0.573.82
Side used
left 43% · central 14% · right 43%
Reference measured across roughly 3,500 match-teams
in this competition, per third, so the comparison is with Hamburg'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 Hamburg 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 share16%
#13 of 18 · reaches final third in ≤3 passes
Patient build-up share23%
#12 of 18 · spells of 6+ passes
Tempo (possession length)17.5s
#10 of 18 · shorter = faster tempo
Passes per possession4.1
#12 of 18
Counter-attack shots /game1.32
#6 of 18 · shot within 12s of a regain in own half
Press→shot /game2.44
#17 of 18 · shot within 8s of a high regain
Speed to final third after regain13.8s
#16 of 18 · lower = faster break
Sequence success — attacking transition
104attacking transition sequences this season
32%succeed — produce a shot
36%turned over with nothing to show
Sequence success — defensive transition
263defensive transition sequences this season
58%succeed — reach the next third or a shot
11%turned over with nothing to show
Vision AI Model Insight
Hamburg are balanced between tempo and patience: 16% of possessions reach the final third in 3 passes or fewer (#13 of 18), against 23% built patiently over 6+ passes (#12 of 18). Winning the ball back leads to a shot inside 12 seconds 1.32 times a game (#6), and a high regain converts to a shot within 8 seconds 2.44 times a game (#17 of 18) — not yet a major source of chances.
Defending the box2 sections
Vulnerability Map — how opponents hurt them
The report flipped: where do opponents create their threat against Hamburg? 1680 opponent progressive passes and 494 shots faced (55.4 xG against) across the season.
Opponent threat by lane (attacking into Hamburg)
Right wing
31%
Right half-space
14%
Central
17%
Left half-space
13%
Left wing
25%
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 Hamburg actually come from — open play, dead balls, and who supplies them?
Every goal Hamburg conceded across the loaded matches — the opponent's shots in Hamburg'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 (339 total)
By situation
Open play
41 · 76%
Set pieces
7 · 13%
Penalties
6 · 11%
Vision AI Model Insight
54 goals conceded. 76% come in open play; set pieces account for only 13%, so dead-ball defending is not the main issue. 13% are headers — aerial defending is not where the damage comes from. Their supply line runs mainly through the centre 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
32 g · 32.1 xG
Set pieces
4 g · 4.4 xG
Penalties
4 g
10% of goals come from set pieces (4 of 40), worth 4.4 xG from 61 shots. Set pieces are a secondary threat; the danger is in open play.
Where do Hamburg's corners go?
124 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 (66)
Delivered into the box 97%Short 3%
Left side (58)
Delivered into the box 97%Short 3%
Where opponents attack Hamburg from set pieces
89 shots from opponent corners against Hamburg, 6 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 (46)
Shot 46Goals 4
Left flag (43)
Shot 43Goals 2
Tactical Strengths & Weaknesses
Strengths (top-third vs league)
none stand out
Weaknesses (bottom-third vs league)
threat created /game — 9.39 (0th pct)
field tilt % — 39.53 (6th pct)
progression /game — 39.38 (6th pct)
box entries /game — 10.47 (6th pct)
xG created /game — 1.21 (17th 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.
Koln
95%
Wolfsburg
91%
Heidenheim
83%
Borussia Monchengladba
80%
Augsburg
78%
Performance vs Opponent Styles
The question
Not just how Hamburg 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 Hamburg's own passing behaviour to the result.
Results
opponent type
goals for/g
goals against/g
points/g
vs high-pressing opponents (11m)
1.45
1.55
1.45
vs mid-block opponents (11m)
1.27
1.82
1.09
vs low-block opponents (12m)
0.83
1.42
0.83
Behaviour — how Hamburg actually play against each
opponent type
retention
directness (long balls)
prog. passes/g
xT/g
box entries/g
vs high-pressing opponents
74%
20%
34
7.6
8.3
vs mid-block opponents
79%
16%
39
9.6
10.5
vs low-block opponents
82%
14%
45
10.8
12.5
Vision AI Model Insight
Facing a high press, retention drops to 74% vs 82% against a low block (8pt lower under pressure), and directness (long-ball share) sits at 20% when pressed vs 14% 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. Hamburg are territorially-dominated with a aggressive high-press.
Their attack creates 1.21 xG a game and they concede 1.63 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 Hamburg’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.
Hamburg
across 34 matches this season
PPDAat Bayern · 0-5 · 2025-09-1320.8avg 7.8
Progressive carriesat Union Berlin · 0-0 · 2025-09-2882avg 41
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 Hamburg’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).
+17.6on-ball value, 12 matches
Where the value came from
Passes4546 actions+7.98
Carries922 actions-0.21
Dribbles185 actions+0.01
Shots128 actions+5.89
Ball wins713 actions+3.95
Most on-ball value (OBV)
Actions
OBV
per 100
Luka Vuskovic
550
+2.49
+0.45
Fábio Vieira
572
+2.17
+0.38
William Mikelbrencis
407
+1.72
+0.42
Ransford Königsdörffer
283
+1.45
+0.51
Jean-Luc Dompé
79
+1.43
+1.81
Nicolás Capaldo
262
+1.32
+0.50
Albert Grønbæk
241
+1.08
+0.45
Robert Glatzel
121
+0.93
+0.77
Biggest single moves
OBV
1 Robert Glatzel · shot
+0.864
2 Luka Vuskovic · shot
+0.795
3 Jean-Luc Dompé · shot
+0.783
4 William Mikelbrencis · pass
+0.624
5 Luka Vuskovic · shot
+0.417
6 Sander Tangvik · pass
+0.391
Set Pieces by Player
Who delivers Hamburg’s dead balls and who finishes them this season. 124 corners taken, most by Miro Muheim (52); 61 shots from corners and free kicks, 4 goals, led by Luka Vuskovic (2 goals, 1.42 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
Miro Muheim
52
47 (90%)
5
—
0.13
4
—
Fábio Vieira
33
25 (76%)
8
1
0.20
3
1/1
Luka Vuskovic
—
—
14
2
1.42
3
1/1
Jean-Luc Dompé
23
21 (91%)
4
—
0.11
3
1/1
Rayan Philippe
—
—
6
—
0.35
—
0/1
Ransford Königsdörffer
2
2 (100%)
3
—
0.61
—
0/1
Albert Grønbæk
5
4 (80%)
2
1
0.06
1
—
Jordan Torunarigha
—
—
3
—
0.15
—
—
Albert Sambi Lokonga
1
0 (0%)
2
—
0.10
—
—
Alexander Røssing-Lelesiit
6
3 (50%)
—
—
0.00
—
—
Bakery Jatta
—
—
2
—
0.08
—
—
Nicolai Remberg
—
—
2
—
0.39
—
—
William Mikelbrencis
—
—
2
—
0.05
—
—
Philip Otele
1
0 (0%)
1
—
0.03
—
—
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
Miro Muheim
M
2638
1.36
54
42.6
Fábio Vieira
M
2345
1.42
54
28.5
Jean-Luc Dompé
F
1227
2.42
44
15.3
Albert Sambi Lokonga
M
1815
1.14
20
16.6
Nicolás Capaldo
D
2101
0.90
20
18.5
William Mikelbrencis
M
1475
0.98
19
13.5
Ransford Königsdörffer
F
2384
0.64
16
8.0
Rayan Philippe
F
1376
0.92
17
5.7
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)
Jean-Luc Dompé
W
1227
40 (48%)
17 (41%)
Defensive Volume Leaders
Who does the defensive work — tackles, interceptions, recoveries.
player
pos
min
def actions
tackles
intercept
recoveries
Luka Vuskovic
D
2641
657
22
40
126
Nicolai Remberg
M
3111
483
60
42
138
Jordan Torunarigha
D
2085
433
37
30
61
Miro Muheim
M
2638
390
62
27
126
Nicolás Capaldo
D
2101
379
68
35
93
Warmed Omari
D
1715
269
22
27
51
Ransford Königsdörffer
F
2384
236
6
6
53
Fábio Vieira
M
2345
236
20
13
126
Full Squad Data
Every player with 100+ minutes (28 players).
player
pos
apps
min
goals
xG
key P
prog
def
xT
Daniel Heuer Fernandes
G
33
3202
0
0.2
0
188
105
46.9
Nicolai Remberg
M
32
3111
0
1.2
11
52
483
17.5
Luka Vuskovic
D
28
2641
6
4.2
9
88
657
25.9
Miro Muheim
M
29
2638
0
1.1
40
208
390
42.6
Ransford Königsdörffer
F
33
2384
5
7.3
17
40
236
8.0
Fábio Vieira
M
29
2345
7
4.5
37
112
236
28.5
Nicolás Capaldo
D
26
2101
1
2.1
21
89
379
18.5
Jordan Torunarigha
D
24
2085
0
0.3
2
82
433
20.4
Albert Sambi Lokonga
M
26
1815
5
1.6
23
52
182
16.6
Warmed Omari
D
20
1715
0
0.1
7
59
269
15.0
William Mikelbrencis
M
26
1475
0
0.9
16
71
204
13.5
Daniel Elfadli
D
21
1409
0
0.3
2
21
200
8.7
Rayan Philippe
F
28
1376
5
5.5
14
22
142
5.7
Jean-Luc Dompé
F
20
1227
3
2.1
33
83
39
15.3
Giorgi Gocholeishvili
M
24
1092
0
0.9
10
53
151
9.1
Bakery Jatta
M
15
945
1
0.9
4
33
115
5.9
Robert Glatzel
F
19
705
3
2.7
5
8
70
2.2
Damion Downs
F
12
585
0
0.5
6
2
54
1.1
Fabio Baldé
F
16
520
1
0.9
4
6
65
2.1
Albert Grønbæk
M
7
511
1
0.5
8
22
70
4.4
Philip Otele
M
9
501
1
1.2
9
18
55
3.6
Yussuf Poulsen
F
15
437
1
0.7
2
7
94
1.6
Alexander Røssing-Lelesiit
F
11
385
0
0.1
5
7
40
1.8
Otto Stange
F
9
344
0
0.9
1
3
25
0.5
Jonas Meffert
M
4
157
0
0.0
2
2
25
0.7
Aboubaka Soumahoro
D
4
150
0
0.0
1
0
15
0.5
Emir Sahiti
F
2
120
0
0.0
1
0
10
0.4
Guilherme Ramos
D
8
107
0
0.2
0
2
22
0.5
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.
Daniel Heuer Fernandes
Goalkeeper · Goalkeeper
minutes3202
goals · xG0 · 0.2
prog passes /905.3
key passes /900.00
def actions /903.0
pass accuracy75%
Nicolai Remberg
Ball-winner / destroyer · Midfielder
minutes3111
goals · xG0 · 1.2
prog passes /901.5
key passes /900.32
def actions /9014.0
pass accuracy89%
Luka Vuskovic
Stopper / marker · Defender
minutes2641
goals · xG6 · 4.2
prog passes /903.0
key passes /900.31
def actions /9022.4
pass accuracy83%
Miro Muheim
Deep-lying playmaker · Midfielder
minutes2638
goals · xG0 · 1.1
prog passes /907.1
key passes /901.36
def actions /9013.3
pass accuracy73%
Ransford Königsdörffer
Box-to-box midfielder · Midfielder
minutes2384
goals · xG5 · 7.3
prog passes /901.5
key passes /900.64
def actions /908.9
pass accuracy68%
Fábio Vieira
Ball-winner / destroyer · Midfielder
minutes2345
goals · xG7 · 4.5
prog passes /904.3
key passes /901.42
def actions /909.1
pass accuracy80%
Nicolás Capaldo
Stopper / marker · Defender
minutes2101
goals · xG1 · 2.1
prog passes /903.8
key passes /900.90
def actions /9016.2
pass accuracy77%
Jordan Torunarigha
Stopper / marker · Defender
minutes2085
goals · xG0 · 0.3
prog passes /903.5
key passes /900.09
def actions /9018.7
pass accuracy85%
Albert Sambi Lokonga
Ball-winner / destroyer · Midfielder
minutes1815
goals · xG5 · 1.6
prog passes /902.6
key passes /901.14
def actions /909.0
pass accuracy87%
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: threat created /game (0th pct)
Sign a threat-generating creator between the lines. The data flags this because little threat is manufactured (9.39, 0th percentile vs the league).
Need — bottom-third: field tilt % (6th pct)
Sign a possession-controlling midfielder to win the territorial battle. The data flags this because they get pinned back (39.53, 6th percentile vs the league).
Need — bottom-third: progression /game (6th pct)
Sign a progressive passer / ball-carrier to break lines. The data flags this because progression is limited (39.38, 6th percentile vs the league).