Combining every season — this one is larger, give it a moment.
Team reportBundesliga 2026
Hamburg
A territorially-dominated side, aggressive high-press out of possession, with a low-output attack. Currently #11 of 18 on 9 points (3-0-4, 6:16). 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 Hamburg and how good was the season?
Read. Hamburg currently sit #11 of 18 on 9 points (3W-0D-4L, 1.29 pts/game), scoring 6 and conceding 16 for a goal difference of -10.
On the ball they are a territorially-dominated team (35% field tilt, #18 in the league); off it they defend with a aggressive high-press (#15 of 18 for press intensity).
Across the 7 matches of event data, they create 0.84 xG/game (#18) and concede 1.78 (#13), a net of -0.94 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
8
Elversberg
7
3
1
3
12
15
-3
10
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
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 come alive after the break — first-half net xG -4.18, second-half -2.40. Their strongest period is 61-75' (net -0.48 xG); their most dangerous period to face is 31-45' (net -2.31 xG against them).
xG Race — the season, match by match
Expected goals piling up over 7 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 (-6.6 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.
6
Build-Up
possession control from the back
22
Progression
moving the ball upfield with threat
4
Creation
manufacturing chances
8
Press
winning the ball high & hard
28
Solidity
resisting the opponent's threat
Genetic identity
Hamburg are defined most by their Solidity DNA (28/100 · limited at resisting the opponent's threat), and least by their Creation DNA (4/100). 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 35% field tilt, ranking #18 of 18 in the league — below the league mid-point, spending more of the game defending their own territory than dictating it, and their 1.29 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
30.5%
Progression
47.5%
Chance creation
18.7%
Attacking transition
3.3%
Vision AI Model Insight
Progression is where Hamburg spends most of its time on the ball (48% of its time on the ball).
Passing Network
The question
Who actually plays with whom — the real on-pitch connections, not just average positions?
Every pass linked to its likely receiver (the next same-team touch), aggregated into player-pair combinations at each player's average position. Line width = how often the pair combines; colour = threat generated per pass (blue low → red high). Hover a line for the pair, or click a player to isolate their connections.
width = passes between the paircolour = xT per pass: low → high
Top 10 combinations
Fernandes ↔ Omari
58p · -0.6xT
Fernandes ↔ Bornauw
37p · 0.3xT
Lokonga ↔ Remberg
36p · 0.0xT
Fernandes ↔ Lokonga
34p · 0.1xT
Fernandes ↔ Remberg
31p · 0.5xT
Capaldo ↔ Ouahdi
29p · -0.0xT
Capaldo ↔ Remberg
25p · -0.1xT
Bornauw ↔ Remberg
24p · -0.1xT
Omari ↔ Wolfe
23p · -0.3xT
Wolfe ↔ Grønbæk
22p · 1.4xT
Vision AI Model Insight
Busiest connection: Daniel Heuer Fernandes ↔ Warmed Omari (58 passes). Most dangerous per pass: David Møller Wolfe ↔ Martin Adeline (0.08 xT/pass over 18 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 63 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
2634
ACTIONS
Carries
the ball moved with a player
502
ACTIONS
Vision AI Model Insight
Their single busiest zone is the centre of the defensive third (13.6% 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: 35% defensive, 48% middle, 18% 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 right (43%). The bars under the pitch put the league’s share beside theirs.
Hamburg
36%21%43%
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. Hamburg made 201 this season (28.7 a match); 27% came into the right 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
50
7.1
25%
28%
Left half-space
35
5.0
17%
17%
Centre
20
2.9
10%
14%
Right half-space
42
6.0
21%
15%
Right wing
54
7.7
27%
26%
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.
927build-up passes attempted
74%completed
32%reach the middle third
3%go long into the final third
Pass types
268CB · 39%
63Long Wide · 9%
12Long Central · 2%
268Wide → FB · 39%
79Central · 11%
Sequence success — build-up
108build-up sequences this season
6%succeed — reach the next third or a shot
45%turned over with nothing to show
CB268 · 39%
Long Wide63 · 9%
Long Central12 · 2%
Wide → FB268 · 39%
Central79 · 11%
◆ Vision AI Insight
Hamburg build predominantly down the left, with Fernandes as the fulcrum (210 build-up passes). Only 35% 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 (42×).
Pass Explorer
Every pass of Hamburg’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. 293 progressive passes across 7 matches (41.9/game).
Origin lane
Right wing
31%
Right half-space
11%
Central
20%
Left half-space
14%
Left wing
25%
Progression is right-dominant: 42% right side · 20% central · 39% left.
Who generates it
Daniel Heuer Fernandes
50 · 17%
David Møller Wolfe
37 · 13%
Nicolás Capaldo
27 · 9%
Albert Grønbæk
24 · 8%
Albert Sambi Lokonga
19 · 6%
Top two — Daniel Heuer Fernandes & David Møller Wolfe — account for 30% of all progression.
PROGRESSION
41.9 prog passes/game
mostly right
→
PENETRATION
30% reach final third
17% reach the box
→
THREAT
the other 70% is recycled
progress ≠ danger
What came of it.30% of progressive passes reached the final third and 17% the box; the rest was recycled in front of the block. 30% of the progression ran through two players, mostly down the right 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-spacesterile
Centralbalanced
Left half-spacebalanced
Left wingbalanced
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.
1240progression passes attempted
81%completed
14%break into the final third
17%go backward into the defensive third
Pass types
303CB · 30%
31Long Wide · 3%
4Long Central · 0%
532Wide → FB · 53%
130Central · 13%
Sequence success — progression
165progression sequences this season
28%succeed — reach the next third or a shot
45%turned over with nothing to show
CB303 · 30%
Long Wide31 · 3%
Long Central4 · 0%
Wide → FB532 · 53%
Central130 · 13%
◆ Vision AI Insight
Hamburg progress predominantly down the right, with Remberg the busiest midfield outlet (124 progression passes). Only 14% of passes that start in midfield actually break the final-third line — the rest (69%) is recycled back into midfield or (17%) played backward, the hallmark of a side that carries progression itself through midfield rather than just passing through it. Their most-repeated pattern is RHS mid → RW mid (59×) — 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)
15%
Progressive
1%
Neutral / sideways
51%
Safe / backward
33%
16% of passes are progressive-or-better; the remaining 84% 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?
117 progressive carries this season across the whole squad (24% of 491 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 47m. Most active carrier: Omari (12). Filter to one player below.
First (34)Second (33)Third (31)Fourth (19)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.
75shots
5.9xG
0.08xG per shot
3big chances (0.3+ xG)
6goals
#17key 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 transition23% of shots
17
0
1.55
0.09
Cross
1
0
0.07
0.07
Pass into the box / short pass
10
0
0.73
0.07
Own creation (carry, dribble, rebound)
6
0
0.75
0.13
Settled attack52% of shots
39
4
2.96
0.08
Cross
5
2
0.65
0.13
Cut-back
4
1
0.48
0.12
Long ball
1
0
0.03
0.03
Pass into the box / short pass
20
1
1.23
0.06
Own creation (carry, dribble, rebound)
9
0
0.58
0.06
Set piece25% of shots
19
2
1.39
0.07
Corner
13
2
1.26
0.10
Free kick
6
0
0.13
0.02
Every attempt to create — and what came of it
Every pass into the box, every cut-back and every pass before a shot. Red led to a shot, gold to a goal; grey ones were tried but no shot came: solid found a team-mate, dashed lost the ball. Filter by outcome, type or player; tap an arrow for the detail.
Who sets the shots up
Player
Set-ups
xG created
Goals
Albert Sambi Lokonga
7
0.68
1
Albert Grønbæk
4
0.46
1
Fábio Vieira
3
0.30
0
Miro Muheim
5
0.29
1
Nicolai Remberg
2
0.23
0
Who takes them
Player
Shots
xG
Goals
Yussuf Poulsen
9
1.22
2
Fábio Vieira
14
0.93
0
Patson Daka
8
0.76
0
Terem Moffi
6
0.74
1
Nicolás Capaldo
6
0.69
1
Vision AI Model Insight
The biggest source of Hamburg's chances, by xG, is settled attack (3.0 of 5.9 xG, 39 of 75 shots), mostly pass into the box / short pass. 17 of the 75 shots (23%) came within 15 seconds of winning the ball. 19 of the 75 shots came from set pieces; 6 goals from 5.9 xG (+0.1).
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
5CB · 10%
2Long Wide · 4%
4Long Central · 8%
16Wide → FB · 33%
22Central · 45%
Sequence success — final-third
86final-third sequences this season
22%succeed — produce a shot
36%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
CB5 · 10%
Long Wide2 · 4%
Long Central4 · 8%
Wide → FB16 · 33%
Central22 · 45%
Who creates — volume × threat
9Muheim
7Grønbæk
5Lokonga
4Wolfe
4Daka
3Capaldo
3Adeline
49key passes #17 of 18
9.3box entries/game #17 of 18
23.3final-3rd entries/game #16 of 18
0.84xG/game #18 of 18
6goals #15 of 18
+0.1finishing vs xG #11 of 18
◆ Vision AI Insight
Hamburg lean right — 47% of their key passes come from that flank and its half-space (23 of 49), most often from Muheim (5). They funnel chances into the c of the final third, most often via RHS att → C att (10 key passes, most often from Lokonga). Muheim is the primary source overall. They generate 0.84 xG a game and finish it clinical — they beat their xG, led by Miro Muheim (2G), Yussuf Poulsen (2G), Nicolás Capaldo (1G).
Crosses & Cutbacks — squad-wide
The question
How does Hamburg deliver into the box from wide areas, and what comes of it?
70 crosses: 21 found a team-mate (30%), 6 led to a shot (9%). 17 cut-backs: 8 found a team-mate (47%), 5 led to a shot (29%). 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?
70 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. 21% come from one territory, the byline right wing; 9 come from the half-spaces and 0 from the centre.
Territory
Crosses
Share
Led to a shot
byline · right wing
15
21%
2
byline · left wing
15
21%
0
advanced · right wing
12
17%
0
advanced · left wing
8
11%
1
deep · left wing
6
9%
0
deep · right wing
5
7%
0
advanced · right half-space
4
6%
2
byline · left half-space
2
3%
1
deep · right half-space
1
1%
0
deep · left half-space
1
1%
0
byline · right half-space
1
1%
0
Finishing3 sections
Shot Map
75 shots for 5.9 xG (0.84/game, #18 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.
Low-output attack. 10.7 shots/game at 0.079 xG/shot. Box entries 9/game (#17). Threat created 8.8 xT/game (#17).
Goal Sequences
The move behind each of Hamburg’s 6 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 — 6 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
4 · 67%
Set pieces
2 · 33%
Penalties
0 · 0%
By finish
Right foot
1 · 17%
Left foot
3 · 50%
Header
2 · 33%
Vision AI Model Insight
6 goals scored. 67% come in open play, but 33% from set pieces — a genuine dead-ball weapon that bypasses the open-play build-up.
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
164
Failed forward pass
115
Failed take-on
46
Lost in the defensive third
237
Their weakest zone is the left half-space in the attacking third: 43% of 67 pass attempts there are lost (29 times). Separately, 26% 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.
15
Pressing Index /100 · passive
Intensity24
PPDA, high-press share, high-zone actions
Height11
how high up the pitch they defend
Effectiveness6
high recoveries & high-regain share
Disruption19
opponent completion & progression allowed
Every defensive action & where it happens
Where they win the ball back — by lane
RW
18%
RHS
22%
C
27%
LHS
17%
LW
17%
By third (high / mid / low regain)
Def 51%
Mid 35%
Att 14%
Read
Ranked #15 of 18 for press intensity, this is a high press. Note the tension: 51% of their raw defensive actions still occur in their own third — a possession effect, not a passive one. Dominating the ball (21% high-press share) means that when they do defend, it is often deep. Of recoveries, 44% 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
177
#5/18
High-press share
21%
#13/18
def actions won high up
Defensive-action height (0–100)
36.4
#16/18
avg pitch height of actions
High recoveries / game
17.3
#17/18
ball won in mid+att third
High-recovery share
44%
#17/18
share of recoveries won high
Opponent completion allowed
84%
#18/18
lower = more disruptive
Opponent progressive allowed
9.8%
#11/18
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 224 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 8.4 opponent passes in the first half-hour and after 5.7 after the hour mark (sooner).
PASSES THEY GET
7.0passes
median 4 · before Hamburg win it
TIME THEY KEEP IT
22sec
median 10s · 32.0 wins per game
How many passes the opponent got in before the ball was won
33%
26%
14%
26%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
9.7
32s
15–30′
6.9
20s
30–45+′
7.7
24s
45–60′
6.6
22s
60–75′
7.1
24s
75–90+′
4.6
12s
average passesaverage seconds
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) 927 passes
slower
Width of use-0.4420.10
Wide-channel share-0.300.29
Side bias+0.030.27
Pass length+0.5224.58
Long-ball share+0.480.28
Forward gain per pass+0.7414.65
Backward-pass share-0.600.17
Deep-pass share+0.680.35
Completion-0.670.74
Seconds between passes+0.883.86
Side used
left 25% · central 50% · right 25%
Progression (middle third) 1,240 passes
nothing unusual in this zone
Width of use+0.8227.30
Wide-channel share+0.400.43
Side bias-0.82-3.17
Pass length+0.1117.38
Long-ball share+0.040.10
Forward gain per pass-0.333.11
Backward-pass share+0.200.34
Deep-pass share+0.020.16
Completion-0.610.80
Seconds between passes+0.533.43
Side used
left 35% · central 24% · right 41%
Final third 467 passes
trying to break lines, longer passing
Width of use-0.1131.34
Wide-channel share-0.210.57
Side bias-0.64-5.00
Pass length+0.9016.86
Long-ball share+0.190.11
Forward gain per pass+0.661.92
Backward-pass share-0.120.38
Deep-pass share+1.130.13
Completion-0.660.64
Seconds between passes-0.153.29
Side used
left 35% · central 16% · right 48%
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%
#14 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
#13 of 18
Counter-attack shots /game1.32
#6 of 18 · shot within 12s of a regain in own half
Press→shot /game2.44
#18 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
7attacking transition sequences this season
29%succeed — produce a shot
0%turned over with nothing to show
Sequence success — defensive transition
39defensive transition sequences this season
51%succeed — reach the next third or a shot
13%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 (#14 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 (#18 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? 402 opponent progressive passes and 131 shots faced (12.5 xG against) across the season.
Opponent threat by lane (attacking into Hamburg)
Right wing
28%
Right half-space
14%
Central
17%
Left half-space
11%
Left wing
29%
Where the danger came from. Opponents generated most of their threat through the left wing (29% 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 (96 total)
By situation
Open play
15 · 100%
Set pieces
0 · 0%
Penalties
0 · 0%
Vision AI Model Insight
15 goals conceded. 100% come in open play; set pieces account for only 0%, so dead-ball defending is not the main issue. 7% 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 (21% 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
4 g · 4.5 xG
Set pieces
2 g · 1.4 xG
Penalties
0 g
33% of goals come from set pieces (2 of 6), worth 1.4 xG from 19 shots. A genuine set-piece weapon — defend the box seriously.
Where do Hamburg's corners go?
26 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 (13)
Delivered into the box 100%Short 0%
Left side (13)
Delivered into the box 100%Short 0%
Where opponents attack Hamburg from set pieces
21 shots from opponent corners against Hamburg, 0 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 (9)
Shot 9Goals 0
Left flag (12)
Shot 12Goals 0
Tactical Strengths & Weaknesses
Strengths (top-third vs league)
none stand out
Weaknesses (bottom-third vs league)
xG created /game — 0.84 (0th pct)
field tilt % — 34.65 (0th pct)
box entries /game — 9.29 (0th pct)
threat created /game — 8.80 (6th pct)
pass retention % — 75.66 (11th 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.
Union Berlin
88%
Elversberg
79%
Schalke
77%
Paderborn
71%
Borussia Monchengladba
55%
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 (2m)
1.50
1.50
1.50
vs mid-block opponents (2m)
1.50
0.50
3.00
vs low-block opponents (3m)
0.00
4.00
0.00
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
68%
21%
35
8.3
8.5
vs mid-block opponents
74%
18%
38
7.6
10.5
vs low-block opponents
80%
11%
49
9.9
9.0
Vision AI Model Insight
Facing a high press, retention drops to 68% vs 80% against a low block (12pt lower under pressure), and directness (long-ball share) sits at 21% when pressed vs 11% 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 0.84 xG a game and they concede 1.78 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 Hamburg’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).
+9.2on-ball value, 7 matches
Where the value came from
Passes2634 actions+3.77
Carries491 actions+0.33
Dribbles83 actions+0.42
Shots75 actions+2.62
Ball wins412 actions+2.05
Most on-ball value (OBV)
Actions
OBV
per 100
Daniel Heuer Fernandes
371
+1.00
+0.27
Albert Grønbæk
207
+0.94
+0.46
David Møller Wolfe
234
+0.92
+0.39
Yussuf Poulsen
87
+0.91
+1.04
Fábio Vieira
163
+0.80
+0.49
Nicolás Capaldo
307
+0.74
+0.24
Terem Moffi
49
+0.64
+1.31
Nicolai Remberg
248
+0.56
+0.22
Biggest single moves
OBV
1 Yussuf Poulsen · shot
+0.391
2 Terem Moffi · shot
+0.343
3 Yussuf Poulsen · take-on
+0.281
4 Sebastiaan Bornauw · pass
+0.275
5 Fábio Vieira · shot
+0.227
6 Sebastiaan Bornauw · pass
+0.197
Set Pieces by Player
Who delivers Hamburg’s dead balls and who finishes them this season. 26 corners taken, most by Albert Grønbæk (10); 19 shots from corners and free kicks, 2 goals, led by Nicolás Capaldo (1 goals, 0.49 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
Fábio Vieira
6
6 (100%)
5
—
0.44
4
—
Albert Grønbæk
10
9 (90%)
1
—
0.02
1
—
Sebastiaan Bornauw
—
—
3
—
0.16
—
—
Nicolás Capaldo
—
—
3
1
0.49
—
—
Miro Muheim
5
4 (80%)
1
1
0.03
—
—
David Møller Wolfe
3
2 (67%)
1
—
0.06
—
—
Bilal Nadir
—
—
1
—
0.03
1
—
Zakaria El Ouahdi
—
—
1
—
0.04
—
—
Patson Daka
—
—
1
—
0.04
—
—
Yussuf Poulsen
—
—
1
—
0.06
—
—
Louis Lemke
—
—
1
—
0.04
—
—
Albert Sambi Lokonga
1
1 (100%)
—
—
0.00
—
—
Kofi Amoako
1
0 (0%)
—
—
0.00
—
—
Top Performers
Ranked by Vision AI Score — the same tactical behavioural evaluation each player's own report page shows (min 189 minutes). Coloured chips are that player's strongest capability blocks.
Chance creation leaders — key passes /90 and box entries.
player
pos
min
key P /90
box entries
xT
Albert Grønbæk
M
385
1.64
12
4.4
Miro Muheim
M
218
3.72
5
2.0
David Møller Wolfe
M
534
0.67
8
5.8
Albert Sambi Lokonga
M
359
1.25
6
4.6
Fábio Vieira
M
344
0.78
4
1.7
Martin Adeline
M
360
0.75
4
2.9
Patson Daka
M
211
1.71
3
0.8
Nicolás Capaldo
D
559
0.48
4
4.4
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)
Otto Stange
W
219
0
1 (0%)
Defensive Volume Leaders
Who does the defensive work — tackles, interceptions, recoveries.
player
pos
min
def actions
tackles
intercept
recoveries
Nicolás Capaldo
D
559
165
18
14
36
Sebastiaan Bornauw
D
468
101
4
10
10
Warmed Omari
D
395
79
4
9
14
Martin Adeline
M
360
79
13
4
25
David Møller Wolfe
M
534
78
3
4
19
Jordan Torunarigha
D
311
71
3
6
9
Nicolai Remberg
M
411
66
5
5
19
Albert Grønbæk
M
385
63
8
2
12
Full Squad Data
Every player with 100+ minutes (21 players).
player
pos
apps
min
goals
xG
key P
prog
def
xT
Daniel Heuer Fernandes
G
7
653
0
0.0
1
50
14
10.8
Nicolás Capaldo
D
6
559
1
0.7
3
27
165
4.4
David Møller Wolfe
M
6
534
0
0.4
4
37
78
5.8
Sebastiaan Bornauw
D
5
468
0
0.2
0
11
101
3.4
Nicolai Remberg
M
5
411
0
0.2
1
11
66
3.0
Warmed Omari
D
5
395
0
0.0
2
10
79
3.4
Albert Grønbæk
M
6
385
0
0.1
7
24
63
4.4
Zakaria El Ouahdi
M
5
366
0
0.2
2
14
55
2.6
Martin Adeline
M
6
360
0
0.0
3
13
79
2.9
Albert Sambi Lokonga
M
5
359
0
0.1
5
19
55
4.6
Fábio Vieira
M
6
344
0
0.9
3
10
44
1.7
Jordan Torunarigha
D
4
311
0
0.0
0
10
71
2.7
Terem Moffi
F
7
269
1
0.7
1
2
29
0.7
Yussuf Poulsen
F
6
268
2
1.2
2
5
62
1.2
Louis Lemke
M
5
265
0
0.0
1
10
39
1.3
Otto Stange
F
6
219
0
0.1
0
3
46
0.7
Miro Muheim
M
3
218
2
0.1
9
9
32
2.0
Shafiq Nandja
D
3
213
0
0.0
0
5
45
1.6
Patson Daka
M
4
211
0
0.8
4
1
34
0.8
Bilal Nadir
M
4
211
0
0.1
1
9
45
2.0
Bakery Jatta
M
3
102
0
0.0
0
12
18
1.2
Player Role Architecture
The question
Who does what — and what tactical job does each key player actually perform?
The nine most-used players (450+ minutes), each with the role their event fingerprint points to — not just their listed position.
Daniel Heuer Fernandes
Goalkeeper · Goalkeeper
minutes653
goals · xG0 · 0.0
prog passes /906.9
key passes /900.14
def actions /901.9
pass accuracy70%
Nicolás Capaldo
Stopper / marker · Defender
minutes559
goals · xG1 · 0.7
prog passes /904.3
key passes /900.48
def actions /9026.6
pass accuracy69%
David Møller Wolfe
Deep-lying playmaker · Midfielder
minutes534
goals · xG0 · 0.4
prog passes /906.2
key passes /900.67
def actions /9013.1
pass accuracy64%
Sebastiaan Bornauw
Stopper / marker · Defender
minutes468
goals · xG0 · 0.2
prog passes /902.1
key passes /900.00
def actions /9019.4
pass accuracy80%
Nicolai Remberg
Ball-winner / destroyer · Midfielder
minutes411
goals · xG0 · 0.2
prog passes /902.4
key passes /900.22
def actions /9014.5
pass accuracy90%
Warmed Omari
Stopper / marker · Defender
minutes395
goals · xG0 · 0.0
prog passes /902.3
key passes /900.46
def actions /9018.0
pass accuracy84%
Albert Grønbæk
Advanced creator · Attacking mid
minutes385
goals · xG0 · 0.1
prog passes /905.6
key passes /901.64
def actions /9014.7
pass accuracy71%
Zakaria El Ouahdi
Ball-winner / destroyer · Midfielder
minutes366
goals · xG0 · 0.2
prog passes /903.4
key passes /900.49
def actions /9013.5
pass accuracy73%
Martin Adeline
Ball-winner / destroyer · Midfielder
minutes360
goals · xG0 · 0.0
prog passes /903.2
key passes /900.75
def actions /9019.8
pass accuracy77%
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: xG created /game (0th pct)
Sign a higher-volume goalscorer or chance creator. The data flags this because the attack under-produces xG (0.84, 0th percentile vs the league).
Need — bottom-third: field tilt % (0th pct)
Sign a possession-controlling midfielder to win the territorial battle. The data flags this because they get pinned back (34.65, 0th percentile vs the league).
Need — bottom-third: box entries /game (0th pct)
Sign a penetrative wide player or runner to create box entries. The data flags this because few entries into the box (9.29, 0th percentile vs the league).