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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.

territorially-dominatedaggressive high-pressmoderate attack
L
D
L
L
L
W
W
D
L
L
L
W
W
W
L
38
points
#13 of 18 · 34 games
40:54
goals
-14 GD
9-11-14
record
W-D-L
1.12
points/game
#13 in the league
1.21
xG for/game
rank #15
1.63
xG against/game
rank #15
#15
press rank
of 18 · most aggressive
40%
field tilt
#17
Hamburg
League average
Expected goals for / game
1.211.44
Expected goals against / game
1.631.44
Field tilt
40%50%
Pass retention
79%80%
Overview

The team

Who they are and how the season went.

Who Are They? — Identity

The question
In one read, what kind of team is 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.

#teamPWDLGFGAGDPts
10Mainz341010144453-940
11Union Berlin34109154458-1439
12Borussia Monchengladbach34911144253-1138
13Hamburg34911144054-1438
14Koln34711164963-1432
15Werder Bremen3488183760-2332
16Wolfsburg3478194569-2429

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)
even0-15'16-30'31-45'46-60'61-75'76-90+'+1.06-5.12
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.

020406041.355.4Borussia Monchengladbach (A) 0-0 · xG 0.52 to 1.22St. Pauli (H) 0-2 · xG 0.26 to 1.41Bayern (A) 0-5 · xG 0.69 to 2.32Heidenheim (H) 2-1 · xG 2.81 to 1.87Union Berlin (A) 0-0 · xG 1.41 to 1.56Mainz (H) 4-0 · xG 1.60 to 0.71RBL (A) 1-2 · xG 1.49 to 0.95Wolfsburg (H) 0-1 · xG 3.06 to 0.38Koln (A) 1-4 · xG 1.01 to 2.47Borussia Dortmund (H) 1-1 · xG 0.63 to 1.04Augsburg (A) 0-1 · xG 1.41 to 1.31Stuttgart (H) 2-1 · xG 0.96 to 1.08Werder Bremen (H) 3-2 · xG 1.21 to 0.92Hoffenheim (A) 1-4 · xG 1.60 to 1.34Eintracht Frankfurt (H) 1-1 · xG 0.72 to 1.38Freiburg (A) 1-2 · xG 0.59 to 2.65Borussia Monchengladbach (H) 0-0 · xG 1.36 to 0.47St. Pauli (A) 0-0 · xG 0.33 to 0.41Bayern (H) 2-2 · xG 2.12 to 2.01Heidenheim (A) 2-0 · xG 1.37 to 1.60Union Berlin (H) 3-2 · xG 1.69 to 1.79Mainz (A) 1-1 · xG 0.78 to 1.71RBL (H) 1-2 · xG 0.47 to 2.87Leverkusen (H) 0-1 · xG 1.46 to 1.57Wolfsburg (A) 2-1 · xG 1.95 to 2.07Koln (H) 1-1 · xG 0.30 to 1.15Borussia Dortmund (A) 2-3 · xG 0.90 to 5.33Augsburg (H) 1-1 · xG 1.80 to 2.41Stuttgart (A) 0-4 · xG 0.64 to 2.82Werder Bremen (A) 1-3 · xG 0.67 to 1.35Hoffenheim (H) 1-2 · xG 1.49 to 1.50Eintracht Frankfurt (A) 2-1 · xG 0.57 to 0.55Freiburg (H) 3-2 · xG 1.56 to 0.35Leverkusen (A) 1-1 · xG 1.84 to 2.85M1M5M9M13M17M21M25M29M33
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).

Possess30Disrupt41Finish10Press6Defend10
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.

Keeping the ball6 sections

Field Tilt — Territorial Control

The question
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.

Bayern: 72% tilt, 2.62 pts/gBorussia Dortmund: 59% tilt, 2.15 pts/gRBL: 55% tilt, 1.91 pts/gStuttgart: 59% tilt, 1.82 pts/gHoffenheim: 56% tilt, 1.79 pts/gLeverkusen: 57% tilt, 1.74 pts/gFreiburg: 46% tilt, 1.38 pts/gEintracht Frankfurt: 51% tilt, 1.29 pts/gAugsburg: 46% tilt, 1.26 pts/gMainz: 49% tilt, 1.18 pts/gUnion Berlin: 46% tilt, 1.15 pts/gBorussia Monchengladbach: 42% tilt, 1.12 pts/gHamburg: 40% tilt, 1.12 pts/gKoln: 47% tilt, 0.94 pts/gWerder Bremen: 49% tilt, 0.94 pts/gWolfsburg: 41% tilt, 0.85 pts/gSt. Pauli: 49% tilt, 0.76 pts/gHeidenheim: 39% tilt, 0.76 pts/gHamburg39%50%61%72%0.81.42.02.6FIELD TILT %POINTS / GAME
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.

Build-up: 32.4% of the time on the ball32%Progression: 46.8% of the time on the ball47%Chance creation: 18.1% of the time on the ball18%Attacking transition: 2.7% of the time on the ball47%PROGRESSION
PhaseShare of the time
Build-up32.4%
Progression46.8%
Chance creation18.1%
Attacking transition2.7%
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.

Daniel Heuer Fernandes ↔ Luka Vuskovic: 547 passes, 19.7 combined xTDaniel Heuer Fernandes ↔ Jordan Torunarigha: 432 passes, 12.9 combined xTDaniel Heuer Fernandes ↔ Nicolai Remberg: 353 passes, 20.6 combined xTNicolai Remberg ↔ Luka Vuskovic: 299 passes, 6.7 combined xTDaniel Heuer Fernandes ↔ Warmed Omari: 219 passes, 8.1 combined xTJordan Torunarigha ↔ Miro Muheim: 218 passes, 10.4 combined xTJordan Torunarigha ↔ Luka Vuskovic: 200 passes, 4.4 combined xTDaniel Heuer Fernandes ↔ Daniel Elfadli: 199 passes, 8.8 combined xTDaniel Elfadli ↔ Luka Vuskovic: 194 passes, 3.4 combined xTNicolás Capaldo ↔ Luka Vuskovic: 190 passes, 8.9 combined xTJordan Torunarigha ↔ Nicolai Remberg: 189 passes, 5.2 combined xTMiro Muheim ↔ Nicolai Remberg: 188 passes, 6.2 combined xTMiro Muheim ↔ Daniel Elfadli: 182 passes, 8.8 combined xTFábio Vieira ↔ Nicolai Remberg: 182 passes, 8.0 combined xTJean-Luc Dompé ↔ Miro Muheim: 181 passes, 12.8 combined xTAlbert Sambi Lokonga ↔ Miro Muheim: 170 passes, 7.7 combined xTDaniel Heuer Fernandes ↔ Miro Muheim: 156 passes, 11.8 combined xTWarmed Omari ↔ Nicolai Remberg: 152 passes, 5.7 combined xTWarmed Omari ↔ Luka Vuskovic: 152 passes, 4.1 combined xTMiro Muheim ↔ Luka Vuskovic: 152 passes, 9.6 combined xTDaniel Elfadli ↔ Nicolai Remberg: 146 passes, 3.8 combined xTFábio Vieira ↔ Luka Vuskovic: 134 passes, 8.2 combined xTAlbert Sambi Lokonga ↔ Fábio Vieira: 132 passes, 5.7 combined xTAlbert Sambi Lokonga ↔ Nicolai Remberg: 122 passes, 5.0 combined xTDaniel Heuer Fernandes — isolate this player's connectionsFernandesRansford Königsdörffer — isolate this player's connectionsKönigsdörfNicolai Remberg — isolate this player's connectionsRembergMiro Muheim — isolate this player's connectionsMuheimFábio Vieira — isolate this player's connectionsVieiraLuka Vuskovic — isolate this player's connectionsVuskovicNicolás Capaldo — isolate this player's connectionsCapaldoRayan Philippe — isolate this player's connectionsPhilippeWilliam Mikelbrencis — isolate this player's connectionsMikelbrencAlbert Sambi Lokonga — isolate this player's connectionsLokongaJordan Torunarigha — isolate this player's connectionsTorunarighGiorgi Gocholeishvili — isolate this player's connectionsGocholeishWarmed Omari — isolate this player's connectionsOmariJean-Luc Dompé — isolate this player's connectionsDompéDaniel Elfadli — isolate this player's connectionsElfadliBakery Jatta — isolate this player's connectionsJatta
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
5%5%15%6%5%8%9%10%10%10%5%3%2%2%5%PASSES
Carries
the ball moved with a player
3420
ACTIONS
3%6%15%7%4%10%10%10%10%10%4%2%1%2%7%CARRIES
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.

41%Left side20%Middle39%Right sideATTACKING →
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.

Left wing: 320 entries (30%)32030%LEFT WINGLeft half-space: 161 entries (15%)16115%LEFT HALF-SPACECentre: 129 entries (12%)12912%CENTRERight half-space: 163 entries (15%)16315%RIGHT HALF-SPACERight wing: 298 entries (28%)29828%RIGHT WINGATTACKING ↑
ChannelEntriesPer matchShareLeague share
Left wing3209.430%27%
Left half-space1614.715%16%
Centre1293.812%14%
Right half-space1634.815%15%
Right wing2988.828%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%
left: 844 passes, most from Torunarigha844right: 801 passes, most from Vuskovic801
Long Wide326 · 8%
right: 191 passes, most from Fernandes191left: 135 passes, most from Fernandes135
Long Central61 · 1%
left: 40 passes, most from Fernandes40right: 21 passes, most from Fernandes21
Wide → FB1520 · 37%
left: 808 passes, most from Fernandes808right: 712 passes, most from Fernandes712
Central551 · 13%
left: 290 passes, most from Fernandes290right: 261 passes, most from Fernandes261
◆ 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.

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%
left: 1149 passes, most from Torunarigha1149right: 1141 passes, most from Vuskovic1141
Long Wide238 · 4%
right: 125 passes, most from Remberg125left: 113 passes, most from Muheim113
Long Central46 · 1%
left: 26 passes, most from Vieira26right: 20 passes, most from Muheim20
Wide → FB2419 · 42%
left: 1372 passes, most from Muheim1372right: 1047 passes, most from Remberg1047
Central702 · 12%
left: 368 passes, most from Remberg368right: 334 passes, most from Remberg334
◆ 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.

Carries Map — progressive ball-carries, squad-wide

The question
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
Attacking transition: 110 shots (26%)26%110Settled attack: 241 shots (58%)58%241Set piece: 67 shots (16%)16%67418SHOTS
Kind of attack, then how the shot was set upShotsGoalsxGxG / shot
Attacking transition 26% of shots1101511.980.11
Cross1201.630.14
Cut-back911.310.15
Long ball1551.880.13
Pass into the box / short pass5675.690.10
Own creation (carry, dribble, rebound)1821.470.08
Settled attack 58% of shots2411720.100.08
Cross5854.860.08
Cut-back1821.980.11
Long ball1210.720.06
Pass into the box / short pass9147.330.08
Own creation (carry, dribble, rebound)6255.220.08
Set piece 16% of shots6789.180.14
Corner4724.090.09
Free kick1420.330.02
Penalty644.760.79

Every attempt to create — and what came of it

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

Who sets the shots up

PlayerSet-upsxG createdGoals
Jean-Luc Dompé313.180
William Mikelbrencis193.174
Fábio Vieira323.115
Miro Muheim292.013
Nicolás Capaldo221.723

Who takes them

PlayerShotsxGGoals
Ransford Königsdörffer657.355
Rayan Philippe395.475
Fábio Vieira594.497
Luka Vuskovic324.256
Robert Glatzel162.663
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%
right: 24 passes, most from Capaldo24left: 18 passes, most from Capaldo18
Long Wide21 · 7%
right: 11 passes, most from Remberg11left: 10 passes, most from Vieira10
Long Central33 · 11%
left: 18 passes, most from Muheim18right: 15 passes, most from Muheim15
Wide → FB89 · 31%
left: 49 passes, most from Dompé49right: 40 passes, most from Vieira40
Central105 · 36%
right: 57 passes, most from Dompé57left: 48 passes, most from Lokonga48

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.

deep right wing: 31 crosses (7%)7%31deep right half-space: 2 crosses (0%)0%2deep centre: 0 crosses (0%)deep left half-space: 2 crosses (0%)0%2deep left wing: 32 crosses (7%)7%32advanced right wing: 82 crosses (18%)18%82advanced right half-space: 5 crosses (1%)1%5advanced centre: 0 crosses (0%)advanced left half-space: 0 crosses (0%)advanced left wing: 101 crosses (22%)22%101byline right wing: 91 crosses (20%)20%91byline right half-space: 3 crosses (1%)1%3byline centre: 0 crosses (0%)byline left half-space: 3 crosses (1%)1%3byline left wing: 97 crosses (22%)22%97
TerritoryCrossesShareLed to a shot
advanced · left wing10122%20
byline · left wing9722%18
byline · right wing9120%8
advanced · right wing8218%13
deep · left wing327%2
deep · right wing317%5
advanced · right half-space51%0
byline · right half-space31%1
byline · left half-space31%1
deep · right half-space20%0
deep · left half-space20%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

195 of 689 pass attempts lost here (28%).28%195/689Mikelbrenc133 of 766 pass attempts lost here (17%).17%133/766Fernandes413 of 2190 pass attempts lost here (19%).19%413/2190Fernandes147 of 830 pass attempts lost here (18%).18%147/830Fernandes192 of 708 pass attempts lost here (27%).27%192/708Muheim236 of 1201 pass attempts lost here (20%).20%236/1201Capaldo229 of 1322 pass attempts lost here (17%).17%229/1322Vuskovic215 of 1487 pass attempts lost here (14%).14%215/1487Vuskovic212 of 1429 pass attempts lost here (15%).15%212/1429Muheim256 of 1404 pass attempts lost here (18%).18%256/1404Muheim260 of 722 pass attempts lost here (36%).36%260/722Mikelbrenc131 of 368 pass attempts lost here (36%).36%131/368Vieira71 of 245 pass attempts lost here (29%).29%71/245Vuskovic127 of 359 pass attempts lost here (35%).35%127/359Muheim232 of 747 pass attempts lost here (31%).31%232/747Dompéattacking →

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.

Pressing3 sections

Pressing — the full picture

The football question
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

metricvaluerankmeaning
PPDA (opp passes per def action)—#15/18lower = more intense; rank is what matters
Defensive actions / game141#9/18
High-press share16%#17/18def actions won high up
Defensive-action height (0–100)32.9#18/18avg pitch height of actions
High recoveries / game18.1#18/18ball won in mid+att third
High-recovery share47%#17/18share of recoveries won high
Opponent completion allowed81%#12/18lower = more disruptive
Opponent progressive allowed9.6%#10/18of 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

With the ball

Share of possession time spent in each phase.

Build-up 32.4%Progression 46.8%Chance creation 18.1%Attacking transition 2.7%

Without the ball

How Hamburg defend when the opponent has it.

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

PhaseSegmentsAvg lengthAvg actionsEnds in shotTurned over
Build-up2,1515.0s3.00.2%52.3%
Progression2,4656.3s3.70.5%49.1%
Chance creation1,2564.8s2.829.5%51.4%
Attacking transition1296.9s4.03.9%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.

RouteTimesEnds in shot
build_up9090.0%
progression6490.2%
build_up → progression3410.0%
chance_creation27836.0%
progression → chance_creation27533.8%
build_up → progression → chance_creation16737.1%

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

← opponent shot at own goal

Every opponent key pass against them (339 total)

3%3%4%2%19%17%19%17%15%Kevin StögerLukas UllrichKevin StögerFranck HonoratRobin HackRobin HackNico ElvediFranck HonoratRobin HackJoe ScallyRobin HackFranck HonoratFlorian NeuhausRocco ReitzArkadiusz PyrkaMathias Pereira LageDanel SinaniAndréas HountondjiArkadiusz PyrkaJoel Chima FujitaMathias Pereira LageMathias Pereira LageDanel SinaniManolis SaliakasMartijn KaarsDanel SinaniKonrad LaimerHarry KaneHarry KaneHarry KaneJoshua KimmichKonrad LaimerKonrad LaimerLuis DíazHarry KaneKonrad LaimerJoshua KimmichNicolas JacksonMichael OliseJoshua KimmichJoshua KimmichKonrad LaimerJoshua KimmichLee Jae-SungDanny da CostaKaishu SanoArnaud NordinNelson WeiperNadiem AmiriPaul NebelLennard MaloneyPaul NebelJonas FöhrenbachSirlord ContehArijon IbrahimovicSirlord ContehSirlord ContehJonas FöhrenbachMikkel KaufmannSirlord ContehNiklas DorschYannik WagnerAdrian BeckOmar Haktab TraoréMarnon BuschVladimír CoufalWouter BurgerGrischa PrömelAndrej KramaricBazoumana TouréWouter BurgerAlexander PrassIhlas BebouAlbian HajdariChristopher TrimmelAndrej IlicChristopher TrimmelLeopold QuerfeldChristopher TrimmelChristopher TrimmelNathaniel BrownOscar HøjlundRitsu DoanNathaniel BrownCan UzunNnamdi CollinsYuito SuzukiChristian GünterMatthias GinterPhilipp TreuChristian GünterVincenzo GrifoLucas HölerMaximilian EggesteinPhilipp TreuJohan ManzambiJohan ManzambiChristian GünterPhilipp TreuPhilipp TreuChristian GünterNiklas BesteAssan OuédraogoDavid RaumAssan OuédraogoRidle BakuJohan BakayokoAntonio NusaRômuloChristoph BaumgartnerEzechiel BanzuziArijon IbrahimovicOmar Haktab TraoréArijon IbrahimovicArijon IbrahimovicMarnon BuschMarnon BuschHennes BehrensNiklas DorschNiklas DorschMarnon BuschNiklas DorschMathias HonsakBudu ZivzivadzeChristian EriksenJonas WindMohammed AmouraBence DárdaiKamil GrabaraDerrick KöhnDerrick KöhnRani KhediraTim SkarkeLivan BurcuChristopher TrimmelOliver BurkeLivan BurcuOliver BurkeKristoffer LundÍsak Bergmann JóhannessonFlorian KainzÍsak Bergmann JóhannessonRagnar AcheJakub KaminskiMarvin SchwäbeCenk ÖzkacarJakub KaminskiJakub KaminskiSebastian SebulonsenDenis HuseinbasicJakub KaminskiSilas Katompa MvumpaDanny da CostaNadiem AmiriDanny da CostaSheraldo BeckerNadiem AmiriStefan PoschPaul NebelNikolas VeratschnigNadiem AmiriPhillipp MwenePascal GroßPascal GroßCarney ChukwuemekaCarney ChukwuemekaMaximilian BeierMaximilian BeierJulian RyersonCarney ChukwuemekaNico SchlotterbeckKarim AdeyemiPascal GroßDavid RaumDavid RaumRômuloDavid RaumRidle BakuYan DiomandeRômuloRômuloDavid RaumYan DiomandeAnton KadeDimitrios GiannoulisKeven SchlotterbeckFabian RiederAnton KadeAnton KadeDimitrios GiannoulisDimitrios GiannoulisChristian EriksenChristian EriksenChristian EriksenChristian EriksenMohammed AmouraKonstantinos KoulierakisJosha VagnomanChema AndresChris FührichBilal El KhannoussNikolas NarteyBilal El KhannoussBilal El KhannoussBilal El KhannoussChema AndresBadredine BouananiMaximilian MittelstädtLazar JovanovicCenk ÖzkacarÍsak Bergmann JóhannessonJakub KaminskiJakub KaminskiLinton MainaJens StageRomano SchmidRomano SchmidMarco GrüllRomano SchmidVictor BonifaceJustin NjinmahJulian RyersonJulian RyersonMarcel SabitzerJulian RyersonMaximilian BeierDaniel SvenssonFelix NmechaRamy BensebainiJulian RyersonFábio SilvaIbrahim MazaAleix GarcíaExequiel PalaciosIbrahim MazaIbrahim MazaAlejandro GrimaldoRobert AndrichMontrell CulbreathIbrahim MazaAlejandro GrimaldoFranck HonoratNico ElvediKevin DiksRocco ReitzMathias Pereira LageKarol MetsDanel SinaniArkadiusz PyrkaHarry KaneAleksandar PavlovicJoshua KimmichHarry KaneSerge GnabryAleksandar PavlovicSerge GnabryMichael OliseJoshua KimmichAlexis Claude-MauriceCédric ZesigerAlexis Claude-MauriceFabian RiederAlexis Claude-MauriceAnton KadeAlexis Claude-MauriceMarius WolfMarius WolfMarius WolfDimitrios GiannoulisFabian RiederJamie LewelingErmedin DemirovicDeniz UndavJeff ChabotDeniz UndavChris FührichJamie LewelingMaximilian MittelstädtChris FührichErmedin DemirovicDeniz UndavChris FührichChris FührichChema AndresChema AndresDeniz UndavTiago TomásJovan MilosevicJustin NjinmahCameron PuertasYukinari SugawaraRomano SchmidCameron PuertasCameron PuertasRomano SchmidOlivier DemanJustin NjinmahOlivier DemanRomano SchmidRomano SchmidCameron PuertasMarco GrüllBazoumana TouréVladimír CoufalAlbian HajdariBazoumana TouréBazoumana TouréWouter BurgerAlbian HajdariAndrej KramaricOzan KabakTim LemperleBernardoCole CampbellHugo LarssonAnsgar KnauffJonathan BurkardtCan UzunFarès ChaïbiLukas KüblerMatthias GinterIgor MatanovicVincenzo GrifoExequiel PalaciosAlejandro GrimaldoMalik TillmanAlejandro GrimaldoAleix GarcíaAleix GarcíaMalik TillmanExequiel PalaciosIbrahim MazaEdmond TapsobaArthurArthurAleix GarcíaJonas HofmannLucas VázquezLucas Vázquez← own goal

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.

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

Set-Piece Threat

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

Open play
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)

8%8%11%11%2%17%26%15%2%
Delivered into the box 97%Short 3%

Left side (58)

2%7%26%14%16%2%24%3%3%
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)

17%11%35%20%4%13%
Shot 46Goals 4

Left flag (43)

14%19%19%28%7%14%
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 typegoals for/ggoals against/gpoints/g
vs high-pressing opponents (11m)1.451.551.45
vs mid-block opponents (11m)1.271.821.09
vs low-block opponents (12m)0.831.420.83

Behaviour — how Hamburg actually play against each

opponent typeretentiondirectness (long balls)prog. passes/gxT/gbox entries/g
vs high-pressing opponents74%20%347.68.3
vs mid-block opponents79%16%399.610.5
vs low-block opponents82%14%4510.812.5
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.]
Squad

Players

Who drives the numbers.

Against Their Own Norm

For each of ten match numbers, the one match this season that departed furthest from 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
Expected goalsvs Wolfsburg · 0-1 · 2025-10-253.06avg 1.21
Progressive passesvs Wolfsburg · 0-1 · 2025-10-2568avg 39
Field tiltvs Wolfsburg · 0-1 · 2025-10-2574%avg 40%
Defensive actionsat Koln · 1-4 · 2025-11-0295avg 141
Threat from passingvs Wolfsburg · 0-1 · 2025-10-259.04avg 5.12
Final-third entriesat Borussia Dortmund · 2-3 · 2026-03-2110avg 32

On-Ball Value — who moved the game

Every pass, carry, take-on, shot and ball win is worth the change it made to the chance of scoring minus the chance of conceding in the next 10 seconds. This is 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)ActionsOBVper 100
Luka Vuskovic550+2.49+0.45
Fábio Vieira572+2.17+0.38
William Mikelbrencis407+1.72+0.42
Ransford Königsdörffer283+1.45+0.51
Jean-Luc Dompé79+1.43+1.81
Nicolás Capaldo262+1.32+0.50
Albert Grønbæk241+1.08+0.45
Robert Glatzel121+0.93+0.77
123456
Biggest single movesOBV
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.

PlayerCorners takenInto the boxSet-piece shotsGoalsxGFree-kick shotsPenalties
Miro Muheim5247 (90%)5—0.134—
Fábio Vieira3325 (76%)810.2031/1
Luka Vuskovic——1421.4231/1
Jean-Luc Dompé2321 (91%)4—0.1131/1
Rayan Philippe——6—0.35—0/1
Ransford Königsdörffer22 (100%)3—0.61—0/1
Albert Grønbæk54 (80%)210.061—
Jordan Torunarigha——3—0.15——
Albert Sambi Lokonga10 (0%)2—0.10——
Alexander Røssing-Lelesiit63 (50%)——0.00——
Bakery Jatta——2—0.08——
Nicolai Remberg——2—0.39——
William Mikelbrencis——2—0.05——
Philip Otele10 (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.

playerposminVision AI Scorecapability blocks
Daniel Heuer FernandesGK32025.5Shot-Stopping 55%
Nicolás CapaldoCB21013.0Ball-Winning 64%Progression 32%Ball-Carrying 21%Aerial & Box Defending 4%

Top Creators

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

playerposminkey P /90box entriesxT
Miro MuheimM26381.365442.6
Fábio VieiraM23451.425428.5
Jean-Luc DompéF12272.424415.3
Albert Sambi LokongaM18151.142016.6
Nicolás CapaldoD21010.902018.5
William MikelbrencisM14750.981913.5
Ransford KönigsdörfferF23840.64168.0
Rayan PhilippeF13760.92175.7

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.

playerposmincrosses (completed)cutbacks (completed)
Jean-Luc DompéW122740 (48%)17 (41%)

Defensive Volume Leaders

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

playerposmindef actionstacklesinterceptrecoveries
Luka VuskovicD26416572240126
Nicolai RembergM31114836042138
Jordan TorunarighaD2085433373061
Miro MuheimM26383906227126
Nicolás CapaldoD2101379683593
Warmed OmariD1715269222751
Ransford KönigsdörfferF23842366653
Fábio VieiraM23452362013126

Full Squad Data

Every player with 100+ minutes (28 players).

playerposappsmingoalsxGkey PprogdefxT
Daniel Heuer FernandesG33320200.2018810546.9
Nicolai RembergM32311101.2115248317.5
Luka VuskovicD28264164.298865725.9
Miro MuheimM29263801.14020839042.6
Ransford KönigsdörfferF33238457.317402368.0
Fábio VieiraM29234574.53711223628.5
Nicolás CapaldoD26210112.1218937918.5
Jordan TorunarighaD24208500.328243320.4
Albert Sambi LokongaM26181551.6235218216.6
Warmed OmariD20171500.175926915.0
William MikelbrencisM26147500.9167120413.5
Daniel ElfadliD21140900.32212008.7
Rayan PhilippeF28137655.514221425.7
Jean-Luc DompéF20122732.133833915.3
Giorgi GocholeishviliM24109200.910531519.1
Bakery JattaM1594510.94331155.9
Robert GlatzelF1970532.758702.2
Damion DownsF1258500.562541.1
Fabio BaldéF1652010.946652.1
Albert GrønbækM751110.5822704.4
Philip OteleM950111.2918553.6
Yussuf PoulsenF1543710.727941.6
Alexander Røssing-LelesiitF1138500.157401.8
Otto StangeF934400.913250.5
Jonas MeffertM415700.022250.7
Aboubaka SoumahoroD415000.010150.5
Emir SahitiF212000.010100.4
Guilherme RamosD810700.202220.5

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).
Vision AI · building report