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Team reportBundesliga 2026

RBL

A balanced-territory side, aggressive high-press out of possession, with a high-volume attack. Currently #5 of 18 on 12 points (4-0-3, 20:11). Tactical profile below is built from 7 matches of ball-by-ball event data, contextualised against the league.

balanced-territoryaggressive high-presshigh-volume attack
W
W
W
W
W
L
W
L
W
L
W
L
W
L
W
12
points
#5 of 18 · 7 games
20:11
goals
+9 GD
4-0-3
record
W-D-L
1.71
points/game
#5 in the league
2.11
xG for/game
rank #2
1.46
xG against/game
rank #7
#14
press rank
of 18 · most aggressive
54%
field tilt
#8
RBL
League average
Expected goals for / game
2.111.63
Expected goals against / game
1.461.63
Field tilt
54%50%
Pass retention
85%79%
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 RBL and how good was the season?
Read. RBL currently sit #5 of 18 on 12 points (4W-0D-3L, 1.71 pts/game), scoring 20 and conceding 11 for a goal difference of +9. On the ball they are a balanced-territory team (54% field tilt, #8 in the league); off it they defend with a aggressive high-press (#14 of 18 for press intensity). Across the 7 matches of event data, they create 2.11 xG/game (#2) and concede 1.46 (#7), a net of +0.65 xG/game — the underlying performance matches the actual goal difference.

Season Context — the league table

The question
Where did RBL actually finish, and against whom?

The authoritative final table (full season), centred on RBL.

#teamPWDLGFGAGDPts
2Borussia Dortmund7520124+817
3Freiburg7412169+713
4Leverkusen74121612+413
5RBL74032011+912
6Werder Bremen73311514+112
7Mainz73131613+310
8Elversberg73131215-310

Match Momentum — when they create and concede danger

The question
Across a whole game, which periods does RBL 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+'+2.23-0.41
Vision AI Model Insight
RBL tend to come alive after the break — first-half net xG +1.06, second-half +3.50. Their strongest period is 61-75' (net +2.23 xG); their most dangerous period to face is 16-30' (net -0.41 xG against them).

xG Race — the season, match by match

Expected goals piling up over 7 matches: RBL 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 (+4.6 xG). Hover a point for that match.

05111614.810.2Borussia Monchengladbach (H) 3-0 · xG 3.68 to 1.08Werder Bremen (A) 1-3 · xG 1.47 to 1.01Hamburg (H) 5-0 · xG 1.84 to 0.71Leverkusen (A) 0-2 · xG 0.87 to 1.71Eintracht Frankfurt (H) 6-0 · xG 3.57 to 1.30Bayern (A) 1-6 · xG 0.86 to 3.83Elversberg (H) 4-0 · xG 2.51 to 0.61M1M2M3M4M5M6M7
RBLOpponents against RBL

Tactical DNA

The football question
In five genes, what is RBL'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.

72
Build-Up
possession control from the back
53
Progression
moving the ball upfield with threat
72
Creation
manufacturing chances
39
Press
winning the ball high & hard
61
Solidity
resisting the opponent's threat
Genetic identity
RBL are defined most by their Build-Up DNA (72/100 · strong at possession control from the back), and least by their Press DNA (39/100). 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).

Possess79Disrupt71Finish70Press62Defend57
Possess
Deep buildup
95%
Possession
71%
Field tilt
71%
Disrupt
Central progression ~proxy
57%
Dribble tendency
90%
Progressive passes
67%
Finish
Final-third patience
33%
Shot quality
86%
Open-play xG
90%
Press
Forward press ~proxy
38%
High regains
71%
Pressing efficiency ~proxy
76%
Defend
Def-mid press ~proxy
71%
High line ~proxy
38%
PAdj defensive actions
62%
RBL is defined most by Possess (79% in the league) and weakest on Defend (57%). 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 RBL actually control, relative to the rest of the league?

Field tilt = share of final-third touches between the two sides across RBL'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; RBL highlighted. Hover any dot for the team.

Borussia Dortmund: 57% tilt, 2.43 pts/gBayern: 77% tilt, 2.43 pts/gFreiburg: 55% tilt, 1.86 pts/gLeverkusen: 57% tilt, 1.86 pts/gWerder Bremen: 42% tilt, 1.71 pts/gRBL: 54% tilt, 1.71 pts/gMainz: 60% tilt, 1.43 pts/gElversberg: 37% tilt, 1.43 pts/gHamburg: 35% tilt, 1.29 pts/gAugsburg: 44% tilt, 1.29 pts/gHoffenheim: 62% tilt, 1.29 pts/gPaderborn: 46% tilt, 1.14 pts/gEintracht Frankfurt: 48% tilt, 1.14 pts/gKoln: 48% tilt, 1.14 pts/gUnion Berlin: 41% tilt, 0.86 pts/gStuttgart: 56% tilt, 0.86 pts/gSchalke: 38% tilt, 0.86 pts/gBorussia Monchengladbach: 44% tilt, 0.43 pts/gRBL35%49%63%77%0.41.11.82.4FIELD TILT %POINTS / GAME
Vision AI Model Insight
RBL post 54% field tilt, ranking #8 of 18 in the league — comfortably above the league mid-point for territorial control, and their 1.71 points/game sits above the league-average return — territory is genuinely converting into results.

Where the time goes — how long each phase lasts

The question
When RBL has the ball, how much of that time is spent building, progressing and creating?

The share of RBL'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: 27.3% of the time on the ball27%Progression: 48.3% of the time on the ball48%Chance creation: 22.6% of the time on the ball23%Attacking transition: 1.8% of the time on the ball48%PROGRESSION
PhaseShare of the time
Build-up27.3%
Progression48.3%
Chance creation22.6%
Attacking transition1.8%
Vision AI Model Insight
Progression is where RBL 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.

Willi Orbán ↔ Ridle Baku: 127 passes, -0.1 combined xTWilli Orbán ↔ Maxime Estève: 120 passes, 0.1 combined xTDavid Raum ↔ Maxime Estève: 83 passes, -0.5 combined xTWilli Orbán ↔ Benjamin Henrichs: 76 passes, -0.1 combined xTDavid Raum ↔ Antonio Nusa: 71 passes, 0.4 combined xTWilli Orbán ↔ Maarten Vandevoordt: 67 passes, -0.1 combined xTWilli Orbán ↔ Neil El Aynaoui: 55 passes, -0.3 combined xTChristopher Nkunku ↔ David Raum: 47 passes, 1.5 combined xTDavid Raum ↔ Ezechiel Banzuzi: 43 passes, -0.4 combined xTWilli Orbán ↔ David Raum: 38 passes, 1.0 combined xTDavid Raum ↔ Maarten Vandevoordt: 38 passes, -0.1 combined xTAntonio Nusa ↔ Ezechiel Banzuzi: 34 passes, 0.5 combined xTEzechiel Banzuzi ↔ Neil El Aynaoui: 33 passes, 0.1 combined xTRidle Baku ↔ Neil El Aynaoui: 32 passes, 0.3 combined xTRidle Baku ↔ Tidiam Gomis: 31 passes, 0.9 combined xTWilli Orbán ↔ Andrija Maksimovic: 30 passes, 0.0 combined xTWilli Orbán ↔ Ezechiel Banzuzi: 29 passes, 0.0 combined xTBenjamin Henrichs ↔ Brajan Gruda: 29 passes, 0.3 combined xTDavid Raum ↔ Nicolas Seiwald: 29 passes, 0.2 combined xTMaxime Estève ↔ Nicolas Seiwald: 27 passes, 0.1 combined xTWilli Orbán ↔ Nicolas Seiwald: 26 passes, 0.0 combined xTRidle Baku ↔ Brajan Gruda: 26 passes, 0.7 combined xTMaarten Vandevoordt ↔ Maxime Estève: 23 passes, -0.0 combined xTMaxime Estève ↔ Ezechiel Banzuzi: 22 passes, -0.4 combined xTWilli Orbán — isolate this player's connectionsOrbánAntonio Nusa — isolate this player's connectionsNusaEzechiel Banzuzi — isolate this player's connectionsBanzuziTidiam Gomis — isolate this player's connectionsGomisNeil El Aynaoui — isolate this player's connectionsAynaouiNicolas Seiwald — isolate this player's connectionsSeiwaldMaarten Vandevoordt — isolate this player's connectionsVandevoordBenjamin Henrichs — isolate this player's connectionsHenrichsChristopher Nkunku — isolate this player's connectionsNkunkuDavid Raum — isolate this player's connectionsRaumRidle Baku — isolate this player's connectionsBakuRômulo — isolate this player's connectionsRômuloBrajan Gruda — isolate this player's connectionsGrudaAndrija Maksimovic — isolate this player's connectionsMaksimovicRocco Reitz — isolate this player's connectionsReitzMaxime Estève — isolate this player's connectionsEstève
width = passes between the paircolour = xT per pass: low → high

Top 10 combinations

Orbán ↔ Baku
127p · -0.1xT
Orbán ↔ Estève
120p · 0.1xT
Raum ↔ Estève
83p · -0.5xT
Orbán ↔ Henrichs
76p · -0.1xT
Raum ↔ Nusa
71p · 0.4xT
Orbán ↔ Vandevoordt
67p · -0.1xT
Orbán ↔ Aynaoui
55p · -0.3xT
Nkunku ↔ Raum
47p · 1.5xT
Raum ↔ Banzuzi
43p · -0.4xT
Orbán ↔ Raum
38p · 1.0xT
Vision AI Model Insight
Busiest connection: Willi Orbán ↔ Ridle Baku (127 passes). Most dangerous per pass: Christopher Nkunku ↔ David Raum (0.03 xT/pass over 47 passes) — the combination that hurts opponents most when it fires, not just the one used most.

Touch Density — where RBL 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 RBL 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
3832
ACTIONS
4%5%11%5%4%8%11%11%11%7%6%4%3%4%5%PASSES
Carries
the ball moved with a player
794
ACTIONS
2%6%11%7%3%9%10%16%10%6%6%3%2%4%5%CARRIES
Vision AI Model Insight
Their single busiest zone is the centre of the middle third (11.9% of all actions). 66% of actions run through the inside lanes (centre + both half-spaces) versus 34% down the two wings — an inside-dominant, half-space build. By third: 29% defensive, 50% middle, 21% attacking.

Attack Sides

Which channel they attacked through this season: the share of every action made in the opponent’s half, down the left, through the middle and down the right. RBL leaned on their right (40%). The bars under the pitch put the league’s share beside theirs.

35%Left side25%Middle40%Right sideATTACKING →
RBL
35%25%40%
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. RBL made 338 this season (48.3 a match); 29% 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.

Left wing: 95 entries (28%)9528%LEFT WINGLeft half-space: 56 entries (17%)5617%LEFT HALF-SPACECentre: 43 entries (13%)4313%CENTRERight half-space: 45 entries (13%)4513%RIGHT HALF-SPACERight wing: 99 entries (29%)9929%RIGHT WINGATTACKING ↑
ChannelEntriesPer matchShareLeague share
Left wing9513.628%28%
Left half-space568.017%17%
Centre436.113%14%
Right half-space456.413%15%
Right wing9914.129%26%
Build-up2 sections

Build-Up — the pass map

The football question
How does RBL 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.

1126build-up passes attempted
82%completed
26%reach the middle third
1%go long into the final third

Pass types

393CB · 43%
62Long Wide · 7%
12Long Central · 1%
370Wide → FB · 40%
86Central · 9%

Sequence success — build-up

84build-up sequences this season
11%succeed — reach the next third or a shot
42%turned over with nothing to show
CB393 · 43%
right: 207 passes, most from Orbán207left: 186 passes, most from Lukeba186
Long Wide62 · 7%
left: 32 passes, most from Vandevoordt32right: 30 passes, most from Vandevoordt30
Long Central12 · 1%
left: 6 passes, most from Raum6right: 6 passes, most from Vandevoordt6
Wide → FB370 · 40%
left: 203 passes, most from Vandevoordt203right: 167 passes, most from Vandevoordt167
Central86 · 9%
right: 46 passes, most from Vandevoordt46left: 40 passes, most from Vandevoordt40
◆ Vision AI Insight
RBL build predominantly down the left, with Vandevoordt as the fulcrum (186 build-up passes). Only 27% 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 (82×).

Pass Explorer

Every pass of RBL’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.

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. 315 progressive passes across 7 matches (45.0/game).

Origin lane

Right wing
31%
Right half-space
12%
Central
14%
Left half-space
15%
Left wing
28%
Progression is right-dominant: 43% right side · 14% central · 43% left.

Who generates it

David Raum
74 · 23%
Ridle Baku
40 · 13%
Willi Orbán
24 · 8%
Antonio Nusa
20 · 6%
Castello Lukeba
17 · 5%
Top two — David Raum & Ridle Baku — account for 36% of all progression.
PROGRESSION
45.0 prog passes/game
mostly right
→
PENETRATION
23% reach final third
22% reach the box
→
THREAT
the other 77% is recycled
progress ≠ danger
What came of it. 23% of progressive passes reached the final third and 22% the box; the rest was recycled in front of the block. 36% 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-spacebalanced
Centralbalanced
Left half-spacebalanced
Left wingbalanced

Progression — the pass map

The football question
Once RBL 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.

1895progression passes attempted
90%completed
16%break into the final third
10%go backward into the defensive third

Pass types

609CB · 36%
35Long Wide · 2%
13Long Central · 1%
823Wide → FB · 48%
225Central · 13%

Sequence success — progression

161progression sequences this season
27%succeed — reach the next third or a shot
37%turned over with nothing to show
CB609 · 36%
left: 321 passes, most from Estève321right: 288 passes, most from Orbán288
Long Wide35 · 2%
right: 19 passes, most from Raum19left: 16 passes, most from Raum16
Long Central13 · 1%
right: 9 passes, most from Baku9left: 4 passes, most from Maksimovic4
Wide → FB823 · 48%
right: 423 passes, most from Baku423left: 400 passes, most from Raum400
Central225 · 13%
left: 121 passes, most from Raum121right: 104 passes, most from Aynaoui104
◆ Vision AI Insight
RBL progress predominantly down the right, with Orbán the busiest midfield outlet (292 progression passes). Only 16% of passes that start in midfield actually break the final-third line — the rest (74%) is recycled back into midfield or (10%) played backward, the hallmark of a side that carries progression itself through midfield rather than just passing through it. Their most-repeated pattern is C mid → LHS mid (100×) — 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
49%
Safe / backward
36%
15% of passes are progressive-or-better; the remaining 85% keep possession without advancing it. A control-first side — safe in build-up, reliant on a few players for the incision.

Carries Map — progressive ball-carries, squad-wide

The question
Where does RBL actually progress the ball with their feet, not a pass?

155 progressive carries this season across the whole squad (20% of 794 tracked carries total), grouped into up to 4 spatial patterns by where each carry starts and ends together — not just its origin zone. Median 13m, longest 55m. Most active carrier: Orbán (21). Filter to one player below.

First (51)Second (47)Third (31)Fourth (26)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 RBL'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.

120shots
14.8xG
0.12xG per shot
12big chances (0.3+ xG)
20goals
#7key passes / game in the league
#7box entries / game in the league
Attacking transition: 28 shots (23%)23%28Settled attack: 62 shots (52%)52%62Set piece: 30 shots (25%)25%30120SHOTS
Kind of attack, then how the shot was set upShotsGoalsxGxG / shot
Attacking transition 23% of shots2884.110.15
Cross210.620.31
Long ball110.120.12
Pass into the box / short pass1632.500.16
Own creation (carry, dribble, rebound)930.860.10
Settled attack 52% of shots62116.730.11
Cross1010.840.08
Cut-back320.400.13
Long ball200.180.09
Pass into the box / short pass3584.550.13
Own creation (carry, dribble, rebound)1200.760.06
Set piece 25% of shots3013.960.13
Corner2813.910.14
Free kick200.050.02

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

PlayerSet-upsxG createdGoals
Antonio Nusa132.195
David Raum121.491
Tidiam Gomis60.722
Rômulo30.711
Abdoul Koné10.711

Who takes them

PlayerShotsxGGoals
Ezechiel Banzuzi92.221
Christopher Nkunku142.054
Rômulo81.983
Willi Orbán101.771
Tidiam Gomis191.643
Vision AI Model Insight
The biggest source of RBL's chances, by xG, is settled attack (6.7 of 14.8 xG, 62 of 120 shots), mostly pass into the box / short pass. 28 of the 120 shots (23%) came within 15 seconds of winning the ball. 30 of the 120 shots came from set pieces; 20 goals from 14.8 xG (+5.2).

Chance Creation — the pass map

The football question
How does RBL 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

2CB · 2%
4Long Wide · 5%
9Long Central · 11%
24Wide → FB · 29%
44Central · 53%

Sequence success — final-third

139final-third sequences this season
28%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
CB2 · 2%
Long Wide4 · 5%
left: 3 passes, most from Aynaoui3
Long Central9 · 11%
left: 7 passes, most from Raum7right: 2 passes, most from Raum2
Wide → FB24 · 29%
left: 13 passes, most from Nkunku13right: 11 passes, most from Reitz11
Central44 · 53%
right: 23 passes, most from Raum23left: 21 passes, most from Raum21

Who creates — volume × threat

22Raum
12Nusa
6Gruda
6Gomis
5Aynaoui
5Reitz
5Nkunku
83key passes #7 of 18
16.6box entries/game #7 of 18
40.4final-3rd entries/game #6 of 18
2.11xG/game #2 of 18
20goals #2 of 18
+5.2finishing vs xG #1 of 18
◆ Vision AI Insight
RBL lean left — 40% of their key passes come from that flank and its half-space (33 of 83), most often from Raum (12). They funnel chances into the c of the final third, most often via RW att → C att (11 key passes, most often from Raum). Raum is the primary source overall. They generate 2.11 xG a game and finish it clinical — they beat their xG, led by Christopher Nkunku (4G), Antonio Nusa (3G), Rômulo (3G).

Crosses & Cutbacks — squad-wide

The question
How does RBL deliver into the box from wide areas, and what comes of it?

78 crosses: 36 found a team-mate (46%), 16 led to a shot (21%). 17 cut-backs: 12 found a team-mate (71%), 4 led to a shot (24%). 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 RBL actually cross from?

78 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. 27% come from one territory, the advanced right wing; 6 come from the half-spaces and 0 from the centre.

deep right wing: 6 crosses (8%)8%6deep right half-space: 1 crosses (1%)1%1deep centre: 0 crosses (0%)deep left half-space: 0 crosses (0%)deep left wing: 3 crosses (4%)4%3advanced right wing: 21 crosses (27%)27%21advanced right half-space: 1 crosses (1%)1%1advanced centre: 0 crosses (0%)advanced left half-space: 2 crosses (3%)3%2advanced left wing: 15 crosses (19%)19%15byline right wing: 14 crosses (18%)18%14byline right half-space: 1 crosses (1%)1%1byline centre: 0 crosses (0%)byline left half-space: 1 crosses (1%)1%1byline left wing: 13 crosses (17%)17%13
TerritoryCrossesShareLed to a shot
advanced · right wing2127%3
advanced · left wing1519%2
byline · right wing1418%4
byline · left wing1317%3
deep · right wing68%0
deep · left wing34%1
advanced · left half-space23%1
deep · right half-space11%1
advanced · right half-space11%0
byline · right half-space11%1
byline · left half-space11%0
Finishing3 sections

Shot Map

120 shots for 14.8 xG (2.11/game, #2 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.

High-volume attack. 17.1 shots/game at 0.123 xG/shot. Box entries 17/game (#7). Threat created 11.5 xT/game (#7).

Goal Sequences

The move behind each of RBL’s 20 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 — 20 goals traced.

How Do They Score?

The question
Where do the goals actually come from — open play, dead balls, which foot?

Every goal RBL scored across the loaded matches, broken down by how it was created and finished.

By situation

Open play
19 · 95%
Set pieces
1 · 5%
Penalties
0 · 0%

By finish

Right foot
11 · 55%
Left foot
7 · 35%
Header
2 · 10%
Vision AI Model Insight
20 goals scored. 95% come in open play; set pieces add 5%. 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

48 of 161 pass attempts lost here (30%).30%48/161Baku26 of 180 pass attempts lost here (14%).14%26/180Orbán71 of 439 pass attempts lost here (16%).16%71/439Vandevoord22 of 201 pass attempts lost here (11%).11%22/201Lukeba36 of 145 pass attempts lost here (25%).25%36/145Raum51 of 318 pass attempts lost here (16%).16%51/318Baku41 of 425 pass attempts lost here (10%).10%41/425Aynaoui33 of 427 pass attempts lost here (8%).8%33/427Orbán32 of 440 pass attempts lost here (7%).7%32/440Raum33 of 285 pass attempts lost here (12%).12%33/285Nusa61 of 242 pass attempts lost here (25%).25%61/242Baku47 of 147 pass attempts lost here (32%).32%47/147Baku18 of 97 pass attempts lost here (19%).19%18/97Gruda38 of 141 pass attempts lost here (27%).27%38/141Nusa32 of 184 pass attempts lost here (17%).17%32/184Raumattacking →

How they lose it

Incomplete pass — attacking third
196
Failed forward pass
130
Failed take-on
93
Lost in the defensive third
203
Their weakest zone is the right half-space in the attacking third: 32% of 147 pass attempts there are lost (47 times). Separately, 33% of all losses happen in the attacking third — a fair share happen deeper, which invites transitions.
Out of possession

Out of possession

How they press, where they win the ball back, and how they concede.

Pressing2 sections

Pressing — the full picture

The football question
How hard does RBL 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.

54
Pressing Index
/100 · average
Intensity39
PPDA, high-press share, high-zone actions
Height56
how high up the pitch they defend
Effectiveness81
high recoveries & high-regain share
Disruption36
opponent completion & progression allowed

Every defensive action & where it happens

Where they win the ball back — by lane

RW
18%
RHS
19%
C
23%
LHS
24%
LW
15%
By third (high / mid / low regain)
Def 45%
Mid 41%
Att 14%
Read
Ranked #14 of 18 for press intensity, this is a high press. 55% of defensive actions happen in the middle & attacking thirds, at an average height of 39. Of recoveries, 59% are won high up the pitch.

Every pressing metric, ranked in the league

metricvaluerankmeaning
PPDA (opp passes per def action)—#14/18lower = more intense; rank is what matters
Defensive actions / game147#16/18
High-press share24%#5/18def actions won high up
Defensive-action height (0–100)38.8#8/18avg pitch height of actions
High recoveries / game24.6#5/18ball won in mid+att third
High-recovery share59%#2/18share of recoveries won high
Opponent completion allowed83%#16/18lower = more disruptive
Opponent progressive allowed9.2%#7/18of 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 RBL win it back with a defensive action?

Counted over the 224 opposition possessions that RBL 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 RBL step in. RBL win it after 5.8 opponent passes in the first half-hour and after 4.8 after the hour mark (sooner).

PASSES THEY GET
4.9passes
median 4 · before RBL win it
TIME THEY KEEP IT
14sec
median 9s · 32.0 wins per game
How many passes the opponent got in before the ball was won
37%
30%
21%
12%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
5.6
16s
15–30′
5.9
17s
30–45+′
4.6
13s
45–60′
3.6
9s
60–75′
4.3
12s
75–90+′
5.2
15s
average passesaverage seconds

Attitude in each third

Every other style number here says how much RBL 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 RBL 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) 1,126 passes

nothing unusual in this zone

Width of use-0.3720.23
Wide-channel share-0.300.29
Side bias-0.23-0.71
Pass length-0.6220.45
Long-ball share-0.530.18
Forward gain per pass-0.509.28
Backward-pass share-0.250.19
Deep-pass share-0.390.25
Completion+0.310.82
Seconds between passes-0.543.00
Side used

left 27% · central 45% · right 28%

Progression (middle third) 1,895 passes

kept on the floor, playing short

Width of use-0.5324.45
Wide-channel share-0.770.35
Side bias-0.05-0.12
Pass length-0.6816.27
Long-ball share-1.090.06
Forward gain per pass-0.632.66
Backward-pass share+0.250.35
Deep-pass share-0.860.12
Completion+0.830.90
Seconds between passes-0.443.00
Side used

left 35% · central 31% · right 34%

Final third 811 passes

nothing unusual in this zone

Width of use-0.5630.24
Wide-channel share-0.640.53
Side bias-0.37-3.10
Pass length-0.4414.79
Long-ball share-0.360.08
Forward gain per pass-0.430.47
Backward-pass share+0.280.40
Deep-pass share-0.010.09
Completion+0.660.75
Seconds between passes+0.233.57
Side used

left 37% · central 18% · right 45%

Reference measured across roughly 3,500 match-teams in this competition, per third, so the comparison is with RBL'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 RBL 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 share17%
#11 of 18 · reaches final third in ≤3 passes
Patient build-up share30%
#5 of 18 · spells of 6+ passes
Tempo (possession length)18.6s
#16 of 18 · shorter = faster tempo
Passes per possession4.9
#6 of 18
Counter-attack shots /game1.39
#4 of 18 · shot within 12s of a regain in own half
Press→shot /game3.68
#3 of 18 · shot within 8s of a high regain
Speed to final third after regain12.1s
#10 of 18 · lower = faster break

Sequence success — attacking transition

9attacking transition sequences this season
11%succeed — produce a shot
22%turned over with nothing to show

Sequence success — defensive transition

39defensive transition sequences this season
51%succeed — reach the next third or a shot
0%turned over with nothing to show
Vision AI Model Insight
RBL are a patient build-up side: 17% of possessions reach the final third in 3 passes or fewer (#11 of 18), against 30% built patiently over 6+ passes (#5 of 18). Winning the ball back leads to a shot inside 12 seconds 1.39 times a game (#4), and a high regain converts to a shot within 8 seconds 3.68 times a game (#3 of 18) — a genuine transition weapon.
Defending the box2 sections

Vulnerability Map — how opponents hurt them

The report flipped: where do opponents create their threat against RBL? 312 opponent progressive passes and 107 shots faced (10.2 xG against) across the season.

Opponent threat by lane (attacking into RBL)

Right wing
32%
Right half-space
11%
Central
16%
Left half-space
15%
Left wing
27%
Where the danger came from. Opponents generated most of their threat through the right wing (32% of the danger conceded).

How Do They Concede?

The question
Where do the goals against RBL actually come from — open play, dead balls, and who supplies them?

Every goal RBL conceded across the loaded matches — the opponent's shots in RBL'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 (77 total)

1%1%6%4%1%4%14%23%21%14%9%Lukas PetkovFrederik SchmahlLuca SirchLasse GüntherRobin HackWael MohyaHugo BolinFranck HonoratFranck HonoratPhilipp SanderMathieu NguefackFranck HonoratFranck HonoratFranck HonoratFranck HonoratCan UzunCan UzunKeita KosugiJonathan BurkardtCan UzunRitsu DoanCan UzunYounes EbnoutalibRitsu DoanElias BaumAnsgar KnauffFarès ChaïbiYouri RegeerMarco GrüllKarl HeinMarco GrüllChukiCedric IttenDavid Møller WolfeZakaria El OuahdiPatson DakaPatson DakaAlbert GrønbækTerem MoffiAlbert GrønbækAlbert GrønbækAleix GarcíaIbrahim MazaMiguel GutiérrezIbrahim MazaEqui FernándezMoussa DiabyIbrahim MazaIbrahim MazaIbrahim MazaJarell QuansahJarell QuansahAleksandar PavlovicJoshua KimmichHarry KaneLuis DíazMichael OliseLuis DíazKonrad LaimerDayot UpamecanoHarry KaneIsmael SaibariHarry KaneMichael OliseNathaniel BrownMichael OliseMichael OliseLuis DíazLennart KarlMichael OliseJosip StanisicLennart KarlNathaniel BrownLennart KarlAleksandar PavlovicAleksandar PavlovicJoshua Kimmich← own goal

By situation

Open play
9 · 82%
Set pieces
2 · 18%
Penalties
0 · 0%
Vision AI Model Insight
11 goals conceded. 82% come in open play; set pieces account for only 18%, so dead-ball defending is not the main issue. 9% 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 (23% 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
19 g · 10.8 xG
Set pieces
1 g · 4.0 xG
Penalties
0 g
5% of goals come from set pieces (1 of 20), worth 4.0 xG from 30 shots. Set pieces are a secondary threat; the danger is in open play.

Where do RBL's corners go?

37 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 (28)

7%18%21%4%4%11%36%
Delivered into the box 100%Short 0%

Left side (9)

11%44%11%11%
Delivered into the box 78%Short 22%

Where opponents attack RBL from set pieces

21 shots from opponent corners against RBL, 2 conceded, split by the flag the corner came from — arrows from the flag, dots where the shot was taken (gold = goal), shaded by the % of that flag's shots in each zone. Tap or hover a shot for who took it, the minute and the match; filter to one match and the shading recomputes for just that game.

Right flag (5)

40%60%
Shot 5Goals 0

Left flag (16)

25%19%19%38%
Shot 16Goals 2

Tactical Strengths & Weaknesses

Strengths (top-third vs league)
  • xG created /game — 2.11 (89th pct)
  • pass retention % — 84.63 (89th pct)
  • shots /game — 17.14 (67th pct)
Weaknesses (bottom-third vs league)
  • press intensity (PPDA) — 5.82 (22th 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.

Leverkusen
77%
Werder Bremen
59%
Bayern
51%
Freiburg
39%
Mainz
24%

Performance vs Opponent Styles

The question
Not just how RBL 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 RBL's own passing behaviour to the result.

Results

opponent typegoals for/ggoals against/gpoints/g
vs high-pressing opponents (2m)2.503.001.50
vs mid-block opponents (2m)4.500.003.00
vs low-block opponents (3m)2.001.671.00

Behaviour — how RBL actually play against each

opponent typeretentiondirectness (long balls)prog. passes/gxT/gbox entries/g
vs high-pressing opponents81%11%368.710.5
vs mid-block opponents85%12%5011.722.5
vs low-block opponents86%8%4813.216.7
Vision AI Model Insight
Facing a high press, retention drops to 81% vs 86% against a low block (5pt lower under pressure), and directness (long-ball share) sits at 11% when pressed vs 8% against a low block — a mild adjustment, not a wholesale change.

Scouting Summary — the team in brief

In brief. RBL are balanced-territory with a aggressive high-press. Their attack creates 2.11 xG a game and they concede 1.46 a game. [Event-data based — no tracking, so off-ball structure and marking scheme need video.]
Squad

Players

Who drives the numbers.

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 RBL’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).

+16.2on-ball value, 7 matches
Where the value came from
Passes3832 actions+7.23
Carries794 actions+0.13
Dribbles173 actions-0.01
Shots120 actions+6.10
Ball wins426 actions+2.77
Most on-ball value (OBV)ActionsOBVper 100
Brajan Gruda250+2.17+0.87
Antonio Nusa286+2.13+0.74
Neil El Aynaoui368+1.31+0.35
Willi Orbán696+1.30+0.19
Rômulo102+1.09+1.07
Christopher Nkunku168+1.09+0.65
Ezechiel Banzuzi320+1.02+0.32
Maarten Vandevoordt306+0.90+0.30
123456
Biggest single movesOBV
1  Willi Orbán · shot+0.523
2  Brajan Gruda · pass+0.506
3  David Raum · pass+0.492
4  Ezechiel Banzuzi · shot+0.415
5  Willi Orbán · shot+0.383
6  Abdoul Koné · pass+0.367

Set Pieces by Player

Who delivers RBL’s dead balls and who finishes them this season. 37 corners taken, most by David Raum (32); 30 shots from corners and free kicks, 1 goals, led by Christoph Baumgartner (1 goals, 0.34 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
David Raum3227 (84%)3—0.072—
Willi Orbán——7—1.18——
Brajan Gruda21 (50%)3—0.08——
Antonio Nusa——3—0.13——
Christopher Nkunku——3—0.20——
Rocco Reitz22 (100%)2—0.21——
Neil El Aynaoui——2—0.19——
Ezechiel Banzuzi——2—1.15——
Maxime Estève——1—0.31——
Christoph Baumgartner——110.34——
Castello Lukeba——1—0.02——
Tidiam Gomis——1—0.07——
Johan Bakayoko——1—0.02——
Andrija Maksimovic11 (100%)——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.

playerposminVision AI Scorecapability blocks
David RaumFB5627.2Ball-Carrying 75%Wide Delivery 46%Ball-Winning 31%
Willi OrbánCB6577.0Ball-Carrying 68%Progression 50%Ball-Winning 47%Aerial & Box Defending 44%
Neil El AynaouiCM5056.2Successful Dribble 76%Ball-Carrying 76%Progression 62%Chance Creation 32%
Maarten VandevoordtGK5624.9Shot-Stopping 49%
Antonio NusaW5414.6Ball-Carrying 66%Box Threat 54%Wide Delivery 0%Chance Creation 0%
Ezechiel BanzuziCM5253.0Successful Dribble 38%Ball-Carrying 31%Chance Creation 30%Progression 22%

Top Creators

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

playerposminkey P /90box entriesxT
David RaumD5623.522513.2
Antonio NusaM5412.00154.3
Brajan GrudaF3171.70133.4
Rocco ReitzM2331.9381.8
Christopher NkunkuM3641.2472.3
Tidiam GomisF4021.3461.6
Ezechiel BanzuziM5250.6963.8
Neil El AynaouiM5050.8944.6

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)
Antonio NusaW5418 (50%)4 (100%)
David RaumFB56211 (45%)1 (100%)
Brajan GrudaW3177 (29%)2 (100%)
Ridle BakuFB3283 (67%)5 (40%)

Defensive Volume Leaders

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

playerposmindef actionstacklesinterceptrecoveries
Willi OrbánD6571329820
Ezechiel BanzuziM52511119235
Neil El AynaouiM50586131027
Ridle BakuD328755124
David RaumD562748822
Castello LukebaD353624915
Antonio NusaM541608120
Maxime EstèveD304583316

Full Squad Data

Every player with 100+ minutes (18 players).

playerposappsmingoalsxGkey PprogdefxT
Willi OrbánD765711.80241329.7
David RaumD656200.122747413.2
Maarten VandevoordtG656200.0014214.0
Antonio NusaM754130.81220604.3
Ezechiel BanzuziM752512.24111113.8
Neil El AynaouiM650500.7510864.6
Tidiam GomisF740231.669371.6
Christopher NkunkuM636442.153322.3
Castello LukebaD435300.0117625.6
Ridle BakuD532820.6140756.5
Nicolas SeiwaldM632500.116402.5
Brajan GrudaF531700.5614463.4
Maxime EstèveD430400.3115586.0
Benjamin HenrichsD627100.1215503.0
RômuloF526232.023390.6
Rocco ReitzM423310.656231.8
Andrija MaksimovicM520010.248132.2
Max FinkgräfeD314000.0011332.1

Player Role Architecture

The question
Who does what — and what tactical job does each key player actually perform?

The nine most-used players (450+ minutes), each with the role their event fingerprint points to — not just their listed position.

Willi Orbán
Stopper / marker · Defender
minutes657
goals · xG1 · 1.8
prog passes /903.3
key passes /900.00
def actions /9018.1
pass accuracy92%
David Raum
Advanced creator · Attacking mid
minutes562
goals · xG0 · 0.1
prog passes /9011.9
key passes /903.52
def actions /9011.9
pass accuracy82%
Maarten Vandevoordt
Goalkeeper · Goalkeeper
minutes562
goals · xG0 · 0.0
prog passes /902.2
key passes /900.00
def actions /903.4
pass accuracy80%
Antonio Nusa
Advanced creator · Attacking mid
minutes541
goals · xG3 · 0.8
prog passes /903.3
key passes /902.00
def actions /9010.0
pass accuracy72%
Ezechiel Banzuzi
Ball-winner / destroyer · Midfielder
minutes525
goals · xG1 · 2.2
prog passes /901.9
key passes /900.69
def actions /9019.0
pass accuracy85%
Neil El Aynaoui
Ball-winner / destroyer · Midfielder
minutes505
goals · xG0 · 0.7
prog passes /901.8
key passes /900.89
def actions /9015.3
pass accuracy92%
Tidiam Gomis
Box-to-box midfielder · Midfielder
minutes402
goals · xG3 · 1.6
prog passes /902.0
key passes /901.34
def actions /908.3
pass accuracy77%
Christopher Nkunku
Poacher / finisher · Forward
minutes364
goals · xG4 · 2.1
prog passes /900.7
key passes /901.24
def actions /907.9
pass accuracy93%
Castello Lukeba
Stopper / marker · Defender
minutes353
goals · xG0 · 0.0
prog passes /904.3
key passes /900.25
def actions /9015.8
pass accuracy91%

Recruitment Intelligence — profiles to sign

The question
If you were sporting director, which player profiles would move the needle most?

Each recruitment target is derived directly from the team's weakest areas vs the league — the gap, why it shows in the data, and the profile that closes it.

Need — bottom-third: press intensity (PPDA) (22th pct)
Sign a high-energy presser to lift defensive intensity. The data flags this because the press is passive (5.82, 22th percentile vs the league).
Need — below-average: progression /game (44th pct)
Sign a progressive passer / ball-carrier to break lines. The data flags this because progression is limited (45.00, 44th percentile vs the league).
Vision AI · building report