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Team reportBundesliga 2026

Borussia Monchengladbach

A territorially-dominated side, aggressive high-press out of possession, with a high-volume attack. Currently #18 of 18 on 3 points (1-0-6, 11:22). Tactical profile below is built from 7 matches of ball-by-ball event data, contextualised against the league.

territorially-dominatedaggressive high-presshigh-volume attack
D
D
L
D
D
W
L
W
L
L
L
L
L
L
W
3
points
#18 of 18 · 7 games
11:22
goals
-11 GD
1-0-6
record
W-D-L
0.43
points/game
#18 in the league
1.65
xG for/game
rank #8
2.28
xG against/game
rank #16
#7
press rank
of 18 · most aggressive
44%
field tilt
#12
Borussia Monchengladbach
League average
Expected goals for / game
1.651.63
Expected goals against / game
2.281.63
Field tilt
44%50%
Pass retention
77%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 Borussia Monchengladbach and how good was the season?
Read. Borussia Monchengladbach currently sit #18 of 18 on 3 points (1W-0D-6L, 0.43 pts/game), scoring 11 and conceding 22 for a goal difference of -11. On the ball they are a territorially-dominated team (44% field tilt, #12 in the league); off it they defend with a aggressive high-press (#7 of 18 for press intensity). Across the 7 matches of event data, they create 1.65 xG/game (#8) and concede 2.28 (#16), a net of -0.63 xG/game — the underlying performance matches the actual goal difference.

Season Context — the league table

The question
Where did Borussia Monchengladbach actually finish, and against whom?

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

#teamPWDLGFGAGDPts
15Stuttgart72051013-36
16Schalke713347-36
17Union Berlin7133618-126
18Borussia Monchengladbach71061122-113

Match Momentum — when they create and concede danger

The question
Across a whole game, which periods does Borussia Monchengladbach 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+'+0.32-2.13
Vision AI Model Insight
Borussia Monchengladbach tend to fade after a strong start — first-half net xG -1.67, second-half -2.77. Their strongest period is 31-45' (net +0.32 xG); their most dangerous period to face is 61-75' (net -2.13 xG against them).

xG Race — the season, match by match

Expected goals piling up over 7 matches: Borussia Monchengladbach 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 (-4.4 xG). Hover a point for that match.

06121711.516.0RBL (A) 0-3 · xG 1.08 to 3.68Elversberg (H) 3-4 · xG 2.60 to 2.42Freiburg (A) 0-5 · xG 0.29 to 2.61Mainz (H) 3-4 · xG 3.42 to 2.63Koln (A) 1-2 · xG 1.02 to 1.44Hoffenheim (H) 1-2 · xG 0.69 to 1.48Stuttgart (A) 3-2 · xG 2.46 to 1.72M1M2M3M4M5M6M7
Borussia MonchengladbachOpponents against Borussia Monchengladbach

Tactical DNA

The football question
In five genes, what is Borussia Monchengladbach'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.

25
Build-Up
possession control from the back
19
Progression
moving the ball upfield with threat
37
Creation
manufacturing chances
47
Press
winning the ball high & hard
11
Solidity
resisting the opponent's threat
Genetic identity
Borussia Monchengladbach are defined most by their Press DNA (47/100 · average at winning the ball high & hard), and least by their Solidity DNA (11/100). 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).

Possess40Disrupt24Finish27Press11Defend14
Possess
Deep buildup
52%
Possession
43%
Field tilt
24%
Disrupt
Central progression ~proxy
38%
Dribble tendency
19%
Progressive passes
14%
Finish
Final-third patience
19%
Shot quality
24%
Open-play xG
38%
Press
Forward press ~proxy
10%
High regains
10%
Pressing efficiency ~proxy
14%
Defend
Def-mid press ~proxy
19%
High line ~proxy
19%
PAdj defensive actions
5%
Borussia Monchengladbach is defined most by Possess (40% in the league) and weakest on Press (11%). 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 Borussia Monchengladbach actually control, relative to the rest of the league?

Field tilt = share of final-third touches between the two sides across Borussia Monchengladbach'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; Borussia Monchengladbach 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/gBorussia Monchengladbach35%49%63%77%0.41.11.82.4FIELD TILT %POINTS / GAME
Vision AI Model Insight
Borussia Monchengladbach post 44% field tilt, ranking #12 of 18 in the league — below the league mid-point, spending more of the game defending their own territory than dictating it, and their 0.43 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 Borussia Monchengladbach has the ball, how much of that time is spent building, progressing and creating?

The share of Borussia Monchengladbach'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: 28.2% of the time on the ball28%Progression: 46.3% of the time on the ball46%Chance creation: 23.4% of the time on the ball23%Attacking transition: 2.1% of the time on the ball46%PROGRESSION
PhaseShare of the time
Build-up28.2%
Progression46.3%
Chance creation23.4%
Attacking transition2.1%
Vision AI Model Insight
Progression is where Borussia Monchengladbach spends most of its time on the ball (46% 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.

Kevin Diks ↔ Philipp Sander: 81 passes, 0.4 combined xTKevin Diks ↔ Moritz Nicolas: 72 passes, -0.5 combined xTPhilipp Sander ↔ Ko Itakura: 43 passes, -0.4 combined xTKevin Diks ↔ Ko Itakura: 42 passes, 0.0 combined xTMoritz Nicolas ↔ Philipp Sander: 38 passes, -0.2 combined xTMoritz Nicolas ↔ Ko Itakura: 34 passes, -0.1 combined xTFranck Honorat ↔ Joe Scally: 32 passes, 0.3 combined xTKevin Diks ↔ Lukas Ullrich: 31 passes, 0.0 combined xTJoe Scally ↔ Ko Itakura: 31 passes, -0.3 combined xTPhilipp Sander ↔ Lukas Ullrich: 29 passes, -0.0 combined xTJoe Scally ↔ Philipp Sander: 28 passes, 0.3 combined xTPhilipp Sander ↔ Hugo Bolin: 24 passes, 0.9 combined xTJoe Scally ↔ Hugo Bolin: 21 passes, 1.5 combined xTKo Itakura ↔ Daiki Hashioka: 21 passes, -0.1 combined xTLukas Ullrich ↔ Wael Mohya: 21 passes, 0.6 combined xTFlorian Neuhaus ↔ Lukas Ullrich: 19 passes, 0.1 combined xTFlorian Neuhaus ↔ Philipp Sander: 18 passes, 0.4 combined xTJoe Scally ↔ Daiki Hashioka: 18 passes, 0.0 combined xTKevin Diks ↔ Daiki Hashioka: 17 passes, 0.1 combined xTKevin Diks ↔ Hugo Bolin: 17 passes, 0.4 combined xTRobin Hack ↔ Lukas Ullrich: 17 passes, 0.5 combined xTPhilipp Sander ↔ Wael Mohya: 17 passes, 0.3 combined xTFranck Honorat ↔ Hugo Bolin: 16 passes, 1.0 combined xTMoritz Nicolas ↔ Daiki Hashioka: 15 passes, -0.1 combined xTMoritz Nicolas — isolate this player's connectionsNicolasRobin Hack — isolate this player's connectionsHackJoe Scally — isolate this player's connectionsScallyPhilipp Sander — isolate this player's connectionsSanderHugo Bolin — isolate this player's connectionsBolinWael Mohya — isolate this player's connectionsMohyaKevin Diks — isolate this player's connectionsDiksIsac Lidberg — isolate this player's connectionsLidbergYannik Engelhardt — isolate this player's connectionsEngelhardtLukas Ullrich — isolate this player's connectionsUllrichShuto Machino — isolate this player's connectionsMachinoDaiki Hashioka — isolate this player's connectionsHashiokaRocco Reitz — isolate this player's connectionsReitzFranck Honorat — isolate this player's connectionsHonoratFlorian Neuhaus — isolate this player's connectionsNeuhausKo Itakura — isolate this player's connectionsItakura
width = passes between the paircolour = xT per pass: low → high

Top 10 combinations

Diks ↔ Sander
81p · 0.4xT
Diks ↔ Nicolas
72p · -0.5xT
Sander ↔ Itakura
43p · -0.4xT
Diks ↔ Itakura
42p · 0.0xT
Nicolas ↔ Sander
38p · -0.2xT
Nicolas ↔ Itakura
34p · -0.1xT
Honorat ↔ Scally
32p · 0.3xT
Diks ↔ Ullrich
31p · 0.0xT
Scally ↔ Itakura
31p · -0.3xT
Sander ↔ Ullrich
29p · -0.0xT
Vision AI Model Insight
Busiest connection: Kevin Diks ↔ Philipp Sander (81 passes). Most dangerous per pass: Joe Scally ↔ Hugo Bolin (0.07 xT/pass over 21 passes) — the combination that hurts opponents most when it fires, not just the one used most.

Touch Density — where Borussia Monchengladbach 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 Borussia Monchengladbach 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
2990
ACTIONS
4%5%13%5%4%7%8%11%12%9%6%3%2%4%5%PASSES
Carries
the ball moved with a player
599
ACTIONS
5%5%12%6%3%7%6%10%13%13%6%3%4%7%CARRIES
Vision AI Model Insight
Their single busiest zone is the centre of the defensive third (13.2% of all actions). 63% of actions run through the inside lanes (centre + both half-spaces) versus 37% down the two wings — an inside-dominant, half-space build. By third: 32% defensive, 48% middle, 20% attacking.

Attack Sides

Which channel they attacked through this season: the share of every action made in the opponent’s half, down the left, through the middle and down the right. Borussia Monchengladbach leaned on their left (44%). The bars under the pitch put the league’s share beside theirs.

44%Left side23%Middle33%Right sideATTACKING →
Borussia Monchengladbach
44%23%33%
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. Borussia Monchengladbach made 233 this season (33.3 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: 71 entries (30%)7130%LEFT WINGLeft half-space: 51 entries (22%)5122%LEFT HALF-SPACECentre: 26 entries (11%)2611%CENTRERight half-space: 33 entries (14%)3314%RIGHT HALF-SPACERight wing: 52 entries (22%)5222%RIGHT WINGATTACKING ↑
ChannelEntriesPer matchShareLeague share
Left wing7110.130%28%
Left half-space517.322%17%
Centre263.711%14%
Right half-space334.714%15%
Right wing527.422%26%
Build-up2 sections

Build-Up — the pass map

The football question
How does Borussia Monchengladbach 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.

963build-up passes attempted
77%completed
28%reach the middle third
2%go long into the final third

Pass types

343CB · 46%
53Long Wide · 7%
13Long Central · 2%
255Wide → FB · 34%
80Central · 11%

Sequence success — build-up

97build-up sequences this season
9%succeed — reach the next third or a shot
34%turned over with nothing to show
CB343 · 46%
right: 177 passes, most from Diks177left: 166 passes, most from Diks166
Long Wide53 · 7%
left: 31 passes, most from Nicolas31right: 22 passes, most from Nicolas22
Long Central13 · 2%
right: 7 passes, most from Nicolas7left: 6 passes, most from Nicolas6
Wide → FB255 · 34%
left: 129 passes, most from Nicolas129right: 126 passes, most from Nicolas126
Central80 · 11%
right: 44 passes, most from Sander44left: 36 passes, most from Nicolas36
◆ Vision AI Insight
Borussia Monchengladbach build predominantly down the right, with Nicolas as the fulcrum (185 build-up passes). Only 31% 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 → RHS def (52×).

Pass Explorer

Every pass of Borussia Monchengladbach’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. 280 progressive passes across 7 matches (40.0/game).

Origin lane

Right wing
32%
Right half-space
12%
Central
19%
Left half-space
16%
Left wing
21%
Progression is right-dominant: 44% right side · 19% central · 38% left.

Who generates it

Moritz Nicolas
38 · 14%
Philipp Sander
30 · 11%
Hugo Bolin
27 · 10%
Lukas Ullrich
25 · 9%
Kevin Diks
22 · 8%
Top two — Moritz Nicolas & Philipp Sander — account for 24% of all progression.
PROGRESSION
40.0 prog passes/game
mostly right
→
PENETRATION
31% reach final third
20% reach the box
→
THREAT
the other 69% is recycled
progress ≠ danger
What came of it. 31% of progressive passes reached the final third and 20% the box; the rest was recycled in front of the block. 24% 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 wingefficient
Right half-spacebalanced
Centralbalanced
Left half-spacebalanced
Left wingbalanced

Progression — the pass map

The football question
Once Borussia Monchengladbach 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.

1432progression passes attempted
83%completed
15%break into the final third
14%go backward into the defensive third

Pass types

397CB · 33%
46Long Wide · 4%
9Long Central · 1%
554Wide → FB · 47%
185Central · 16%

Sequence success — progression

140progression sequences this season
26%succeed — reach the next third or a shot
49%turned over with nothing to show
CB397 · 33%
left: 219 passes, most from Diks219right: 178 passes, most from Diks178
Long Wide46 · 4%
left: 23 passes, most from Ullrich23right: 23 passes, most from Sander23
Long Central9 · 1%
left: 5 passes, most from Sander5right: 4 passes, most from Honorat4
Wide → FB554 · 47%
left: 351 passes, most from Sander351right: 203 passes, most from Scally203
Central185 · 16%
right: 99 passes, most from Sander99left: 86 passes, most from Sander86
◆ Vision AI Insight
Borussia Monchengladbach progress predominantly down the left, with Sander the busiest midfield outlet (191 progression passes). Only 15% of passes that start in midfield actually break the final-third line — the rest (72%) is recycled back into midfield or (14%) 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 (88×) — 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)
14%
Progressive
1%
Neutral / sideways
50%
Safe / backward
35%
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 Borussia Monchengladbach actually progress the ball with their feet, not a pass?

134 progressive carries this season across the whole squad (22% of 599 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 44m. Most active carrier: Diks (17). Filter to one player below.

First (43)Second (36)Third (33)Fourth (22)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 Borussia Monchengladbach'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.

107shots
11.5xG
0.11xG per shot
6big chances (0.3+ xG)
11goals
#12key passes / game in the league
#15box entries / game in the league
Attacking transition: 27 shots (25%)25%27Settled attack: 59 shots (55%)55%59Set piece: 21 shots (20%)20%21107SHOTS
Kind of attack, then how the shot was set upShotsGoalsxGxG / shot
Attacking transition 25% of shots2743.050.11
Cross100.140.14
Cut-back220.430.21
Long ball200.230.12
Pass into the box / short pass1321.650.13
Own creation (carry, dribble, rebound)900.600.07
Settled attack 55% of shots5955.260.09
Cross600.520.09
Cut-back610.690.11
Pass into the box / short pass3322.680.08
Own creation (carry, dribble, rebound)1421.370.10
Set piece 20% of shots2123.240.15
Corner1811.620.09
Free kick100.030.03
Penalty211.590.79

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
Joe Scally31.521
Hugo Bolin100.900
Franck Honorat80.892
Florian Neuhaus120.822
Wael Mohya70.611

Who takes them

PlayerShotsxGGoals
Hugo Bolin181.712
Wael Mohya111.622
Shuto Machino91.502
Kevin Diks51.110
Lukas Ullrich80.980
Vision AI Model Insight
The biggest source of Borussia Monchengladbach's chances, by xG, is settled attack (5.3 of 11.5 xG, 59 of 107 shots), mostly pass into the box / short pass. 27 of the 107 shots (25%) came within 15 seconds of winning the ball. 21 of the 107 shots came from set pieces; 11 goals from 11.5 xG (-0.5).

Chance Creation — the pass map

The football question
How does Borussia Monchengladbach consistently create dangerous chances — from which zones, and who creates them?

Key passes (the pass immediately before a shot) grouped into 5 real categories: CB, Wide→FB, Central, Long Wide, Long Central. Each gets its own panel below, so nothing competes for attention. Arrow width = volume, labelled with the exact count; hover for the top creator.

Pass types

5CB · 7%
1Long Wide · 1%
9Long Central · 13%
15Wide → FB · 21%
40Central · 57%

Sequence success — final-third

100final-third sequences this season
21%succeed — produce a shot
34%turned over with nothing to show

Danger Funnel — which service carries threat

Every key pass pooled by where it was played FROM, then drawn with its arrow width set by total xT rather than by count. A zone that produces a lot of cheap balls shrinks; one that produces a few decisive ones dominates. Where this disagrees with the volume map, the volume map is the misleading one.

0 key passes shown● low threat● mid● high threatarrow width = total xT, not count
CB5 · 7%
right: 4 passes, most from Leszczynski4
Long Wide1 · 1%
Long Central9 · 13%
right: 7 passes, most from Honorat7left: 2 passes, most from Sander2
Wide → FB15 · 21%
left: 11 passes, most from Neuhaus11right: 4 passes, most from Honorat4
Central40 · 57%
left: 21 passes, most from Mohya21right: 19 passes, most from Bolin19

Who creates — volume × threat

11Honorat
10Neuhaus
10Bolin
6Mohya
5Sander
3Hack
3Nguefack
70key passes #12 of 18
11.9box entries/game #15 of 18
27.6final-3rd entries/game #14 of 18
1.65xG/game #8 of 18
11goals #11 of 18
-0.5finishing vs xG #13 of 18
◆ Vision AI Insight
Borussia Monchengladbach lean right — 47% of their key passes come from that flank and its half-space (33 of 70), most often from Bolin (8). They funnel chances into the c of the final third, most often via RHS att → C att (18 key passes, most often from Bolin). Honorat is the primary source overall. They generate 1.65 xG a game and finish it wasteful — they under-perform their xG, led by Joe Scally (3G), Shuto Machino (2G), Hugo Bolin (2G).

Crosses & Cutbacks — squad-wide

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

95 crosses: 29 found a team-mate (31%), 7 led to a shot (7%). 22 cut-backs: 11 found a team-mate (50%), 9 led to a shot (41%). 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 Borussia Monchengladbach actually cross from?

95 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. 31% come from one territory, the byline right wing; 4 come from the half-spaces and 0 from the centre.

deep right wing: 4 crosses (4%)4%4deep right half-space: 0 crosses (0%)deep centre: 0 crosses (0%)deep left half-space: 1 crosses (1%)1%1deep left wing: 5 crosses (5%)5%5advanced right wing: 21 crosses (22%)22%21advanced right half-space: 1 crosses (1%)1%1advanced centre: 0 crosses (0%)advanced left half-space: 1 crosses (1%)1%1advanced left wing: 15 crosses (16%)16%15byline right wing: 29 crosses (31%)31%29byline right half-space: 1 crosses (1%)1%1byline centre: 0 crosses (0%)byline left half-space: 0 crosses (0%)byline left wing: 17 crosses (18%)18%17
TerritoryCrossesShareLed to a shot
byline · right wing2931%4
advanced · right wing2122%3
byline · left wing1718%0
advanced · left wing1516%0
deep · left wing55%0
deep · right wing44%0
deep · left half-space11%0
advanced · right half-space11%0
advanced · left half-space11%0
byline · right half-space11%0
Finishing3 sections

Shot Map

107 shots for 11.5 xG (1.65/game, #8 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.

High-volume attack. 15.3 shots/game at 0.108 xG/shot. Box entries 12/game (#15). Threat created 9.8 xT/game (#14).

Goal Sequences

The move behind each of Borussia Monchengladbach’s 11 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 — 11 goals traced.

How Do They Score?

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

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

By situation

Open play
9 · 82%
Set pieces
1 · 9%
Penalties
1 · 9%

By finish

Right foot
6 · 55%
Left foot
2 · 18%
Header
3 · 27%
Vision AI Model Insight
11 goals scored. 82% come in open play; set pieces add 9%. 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

40 of 133 pass attempts lost here (30%).30%40/133Sander27 of 149 pass attempts lost here (18%).18%27/149Nicolas82 of 403 pass attempts lost here (20%).20%82/403Nicolas31 of 154 pass attempts lost here (20%).20%31/154Ullrich39 of 124 pass attempts lost here (31%).31%39/124Ullrich52 of 220 pass attempts lost here (24%).24%52/220Hashioka48 of 235 pass attempts lost here (20%).20%48/235Sander42 of 336 pass attempts lost here (12%).12%42/336Neuhaus53 of 357 pass attempts lost here (15%).15%53/357Sander46 of 284 pass attempts lost here (16%).16%46/284Ullrich67 of 171 pass attempts lost here (39%).39%67/171Honorat33 of 96 pass attempts lost here (34%).34%33/96Scally21 of 57 pass attempts lost here (37%).37%21/57Machino46 of 117 pass attempts lost here (39%).39%46/117Bolin61 of 154 pass attempts lost here (40%).40%61/154Hackattacking →

How they lose it

Incomplete pass — attacking third
228
Failed forward pass
150
Failed take-on
58
Lost in the defensive third
219
Their weakest zone is the left wing in the attacking third: 40% of 154 pass attempts there are lost (61 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 Borussia Monchengladbach 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.

51
Pressing Index
/100 · average
Intensity57
PPDA, high-press share, high-zone actions
Height61
how high up the pitch they defend
Effectiveness39
high recoveries & high-regain share
Disruption47
opponent completion & progression allowed

Every defensive action & where it happens

Where they win the ball back — by lane

RW
17%
RHS
20%
C
27%
LHS
21%
LW
15%
By third (high / mid / low regain)
Def 47%
Mid 38%
Att 15%
Read
Ranked #7 of 18 for press intensity, this is a high press. 53% of defensive actions happen in the middle & attacking thirds, at an average height of 39. Of recoveries, 55% are won high up the pitch.

Every pressing metric, ranked in the league

metricvaluerankmeaning
PPDA (opp passes per def action)—#7/18lower = more intense; rank is what matters
Defensive actions / game166#12/18
High-press share23%#7/18def actions won high up
Defensive-action height (0–100)38.9#7/18avg pitch height of actions
High recoveries / game20.0#14/18ball won in mid+att third
High-recovery share55%#7/18share of recoveries won high
Opponent completion allowed81%#13/18lower = more disruptive
Opponent progressive allowed9.1%#6/18of 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 Borussia Monchengladbach win it back with a defensive action?

Counted over the 191 opposition possessions that Borussia Monchengladbach 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 Borussia Monchengladbach step in. Borussia Monchengladbach win it after 6.0 opponent passes in the first half-hour and after 5.5 after the hour mark (sooner).

PASSES THEY GET
5.4passes
median 4 · before Borussia Monchengladbach win it
TIME THEY KEEP IT
16sec
median 9s · 27.3 wins per game
How many passes the opponent got in before the ball was won
37%
26%
19%
18%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
6.9
21s
15–30′
5.0
16s
30–45+′
4.2
12s
45–60′
5.5
15s
60–75′
7.1
20s
75–90+′
4.5
12s
average passesaverage seconds

Attitude in each third

Every other style number here says how much Borussia Monchengladbach 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 Borussia Monchengladbach 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) 963 passes

narrow, central

Width of use-0.9019.14
Wide-channel share-0.460.28
Side bias+0.000.17
Pass length+0.3223.86
Long-ball share+0.470.28
Forward gain per pass+0.3212.85
Backward-pass share+0.110.20
Deep-pass share+0.360.32
Completion-0.370.76
Seconds between passes-0.073.29
Side used

left 26% · central 50% · right 25%

Progression (middle third) 1,432 passes

nothing unusual in this zone

Width of use-0.5224.48
Wide-channel share-0.550.36
Side bias+0.823.29
Pass length+0.0217.26
Long-ball share-0.090.10
Forward gain per pass+0.023.63
Backward-pass share+0.140.34
Deep-pass share+0.300.18
Completion-0.230.83
Seconds between passes-0.123.14
Side used

left 40% · central 32% · right 29%

Final third 595 passes

riskier, more direct

Width of use-0.0531.49
Wide-channel share-0.310.56
Side bias+0.05-0.13
Pass length+0.4016.09
Long-ball share+0.990.14
Forward gain per pass+0.411.59
Backward-pass share-0.360.36
Deep-pass share-0.260.08
Completion-1.150.60
Seconds between passes-0.153.29
Side used

left 43% · central 16% · right 40%

Reference measured across roughly 3,500 match-teams in this competition, per third, so the comparison is with Borussia Monchengladbach'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 Borussia Monchengladbach 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 share15%
#17 of 18 · reaches final third in ≤3 passes
Patient build-up share26%
#9 of 18 · spells of 6+ passes
Tempo (possession length)16.3s
#9 of 18 · shorter = faster tempo
Passes per possession4.5
#8 of 18
Counter-attack shots /game0.90
#15 of 18 · shot within 12s of a regain in own half
Press→shot /game2.78
#16 of 18 · shot within 8s of a high regain
Speed to final third after regain12.4s
#11 of 18 · lower = faster break

Sequence success — attacking transition

5attacking transition sequences this season
20%succeed — produce a shot
40%turned over with nothing to show

Sequence success — defensive transition

26defensive transition sequences this season
58%succeed — reach the next third or a shot
4%turned over with nothing to show
Vision AI Model Insight
Borussia Monchengladbach are a patient build-up side: 15% of possessions reach the final third in 3 passes or fewer (#17 of 18), against 26% built patiently over 6+ passes (#9 of 18). Winning the ball back leads to a shot inside 12 seconds 0.90 times a game (#15), and a high regain converts to a shot within 8 seconds 2.78 times a game (#16 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 Borussia Monchengladbach? 329 opponent progressive passes and 123 shots faced (16.0 xG against) across the season.

Opponent threat by lane (attacking into Borussia Monchengladbach)

Right wing
28%
Right half-space
12%
Central
16%
Left half-space
15%
Left wing
29%
Where the danger came from. Opponents generated most of their threat through the left wing (29% of the danger conceded).

How Do They Concede?

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

Every goal Borussia Monchengladbach conceded across the loaded matches — the opponent's shots in Borussia Monchengladbach'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 (85 total)

2%1%5%7%4%4%18%11%16%14%19%Chris FührichDzenan PejcinovicChris FührichLeo SauerChris FührichChris FührichAtakan KarazorBilal El KhannoussChris FührichMaximilian MittelstädtJamie LewelingChris FührichTidiam GomisRocco ReitzAntonio NusaDavid RaumDavid RaumDavid RaumRocco ReitzBrajan GrudaTidiam GomisEzechiel BanzuziRocco ReitzDavid RaumBrajan GrudaChristopher NkunkuAntonio NusaConrad HarderYuito SuzukiYuito SuzukiDerry ScherhantMatthias GinterMio BackhausDerry ScherhantNiklas BesteVincenzo GrifoJordy MakengoMatthias GinterKeisuke GotoKeisuke GotoLee Jae-SungFabio GruberAnthony CaciSheraldo BeckerAnthony CaciKaishu SanoDavid MokwaFelix KeidelCole CampbellDavid MokwaJan GyamerahNicholas MickelsonLukas PetkovNoel FutkeuNoel FutkeuDanny da CostaPhillipp MweneDanny da CostaKaishu SanoNadiem AmiriNadiem AmiriLinton MainaEllyes SkhiriÍsak JóhannessonBorna SosaÍsak JóhannessonSaïd El MalaBorna SosaMikey MooreMarius BülterLinton MainaBorna SosaLinton MainaLinton MainaJahmai Simpson-PuseyTom KraußAlexander PrassBambasé ContéBambasé ContéFisnik AsllaniAdam HlozekFisnik AsllaniAlexander PrassBambasé ContéVladimír Coufal← own goal

By situation

Open play
18 · 82%
Set pieces
4 · 18%
Penalties
0 · 0%
Vision AI Model Insight
22 goals conceded. 82% come in open play; set pieces account for only 18%, so dead-ball defending is not the main issue. 23% are headers — aerial defending is not where the damage comes from. Their supply line runs mainly through the left wing 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
9 g · 8.3 xG
Set pieces
1 g · 1.7 xG
Penalties
1 g
9% of goals come from set pieces (1 of 11), worth 1.7 xG from 19 shots. Set pieces are a secondary threat; the danger is in open play.

Where do Borussia Monchengladbach's corners go?

41 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 (25)

4%12%32%4%16%28%4%
Delivered into the box 100%Short 0%

Left side (16)

6%12%6%6%6%25%31%6%
Delivered into the box 100%Short 0%

Where opponents attack Borussia Monchengladbach from set pieces

22 shots from opponent corners against Borussia Monchengladbach, 3 conceded, split by the flag the corner came from — arrows from the flag, dots where the shot was taken (gold = goal), shaded by the % of that flag's shots in each zone. Tap or hover a shot for who took it, the minute and the match; filter to one match and the shading recomputes for just that game.

Right flag (8)

12%12%50%12%12%
Shot 8Goals 1

Left flag (14)

36%29%21%14%
Shot 14Goals 2

Tactical Strengths & Weaknesses

Strengths (top-third vs league)
  • none stand out
Weaknesses (bottom-third vs league)
  • xG conceded /game — 2.28 (11th pct)
  • progression /game — 40.00 (17th pct)
  • box entries /game — 11.86 (17th pct)
  • pass retention % — 76.99 (17th pct)
  • threat created /game — 9.84 (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.

Elversberg
84%
Eintracht Frankfurt
83%
Augsburg
59%
Union Berlin
55%
Hamburg
55%

Performance vs Opponent Styles

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

Results

opponent typegoals for/ggoals against/gpoints/g
vs high-pressing opponents (2m)2.002.001.50
vs mid-block opponents (2m)2.003.000.00
vs low-block opponents (3m)1.004.000.00

Behaviour — how Borussia Monchengladbach actually play against each

opponent typeretentiondirectness (long balls)prog. passes/gxT/gbox entries/g
vs high-pressing opponents70%20%307.06.0
vs mid-block opponents78%15%5612.416.0
vs low-block opponents79%14%3610.113.0
Vision AI Model Insight
Facing a high press, retention drops to 70% vs 79% against a low block (9pt 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. Borussia Monchengladbach are territorially-dominated with a aggressive high-press. Their attack creates 1.65 xG a game and they concede 2.28 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 Borussia Monchengladbach’s last 7 matches added up: where the value came from, who produced it, and the six biggest single moves. OBV only measures what a player does with the ball: it shows who added the most on-ball value, not who was the most important player (defending off the ball, positioning and pressing are not in it).

+10.8on-ball value, 7 matches
Where the value came from
Passes2990 actions+3.10
Carries599 actions+0.07
Dribbles91 actions-0.29
Shots107 actions+5.87
Ball wins373 actions+2.07
Most on-ball value (OBV)ActionsOBVper 100
Hugo Bolin275+2.22+0.81
Joe Scally256+1.67+0.65
Kevin Diks449+1.41+0.31
Shuto Machino73+1.33+1.82
Wael Mohya183+1.07+0.58
Florian Neuhaus162+1.00+0.62
Philipp Sander518+0.82+0.16
Franck Honorat143+0.68+0.48
123456
Biggest single movesOBV
1  Kevin Diks · shot+0.803
2  Shuto Machino · shot+0.698
3  Joe Scally · pass+0.573
4  Hugo Bolin · shot+0.569
5  Joe Scally · pass+0.548
6  Joe Scally · shot+0.410

Set Pieces by Player

Who delivers Borussia Monchengladbach’s dead balls and who finishes them this season. 41 corners taken, most by Robin Hack (11); 19 shots from corners and free kicks, 1 goals, led by Hugo Bolin (1 goals, 0.45 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
Franck Honorat98 (89%)2—0.04——
Shuto Machino——3—0.29—1/1
Hugo Bolin54 (80%)210.45——
Robin Hack1110 (91%)——0.00——
Philipp Sander——3—0.151—
Ko Itakura——2—0.42——
Joe Scally——2—0.03——
Mathieu Nguefack22 (100%)1—0.02——
David Herold32 (67%)——0.00——
Kevin Stöger33 (100%)——0.00——
Lukas Ullrich33 (100%)——0.00——
Nicolas Kühn33 (100%)——0.00——
Florian Neuhaus——1—0.11——
Jan Leszczynski——1—0.07——

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
Moritz NicolasGK6637.5Shot-Stopping 75%
Philipp SanderCM6634.4Progression 66%Chance Creation 53%Ball-Carrying 48%Successful Dribble 9%
Joe ScallyCM5224.1Successful Dribble 62%Ball-Carrying 43%Progression 30%Chance Creation 30%
Kevin DiksCB5693.4Progression 54%Ball-Winning 36%Ball-Carrying 33%Aerial & Box Defending 13%
Lukas UllrichCM5232.5Successful Dribble 48%Ball-Carrying 39%Progression 38%Chance Creation 26%
Hugo BolinAM5742.5Box Threat 67%Ball-Carrying 22%Chance Creation 20%

Top Creators

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

playerposminkey P /90box entriesxT
Hugo BolinM5741.57156.1
Franck HonoratM2414.11124.1
Florian NeuhausM2833.1852.4
Wael MohyaM3871.4081.7
Philipp SanderM6630.6897.9
Lukas UllrichM5230.5254.6
Robin HackM2740.9952.3
Joe ScallyM5220.5233.4

Wide Delivery Leaders

Crosses and cutbacks — wingers and full-backs only (by real average position, not the raw D/M position code), ranked by combined volume.

playerposmincrosses (completed)cutbacks (completed)
Franck HonoratW2414 (25%)5 (20%)
Wael MohyaW38704 (75%)

Defensive Volume Leaders

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

playerposmindef actionstacklesinterceptrecoveries
Philipp SanderM663150181238
Kevin DiksD569997314
Joe ScallyM5229814118
Lukas UllrichM52394121319
Hugo BolinM5748111228
Daiki HashiokaD30460357
Isac LidbergF28854125
Wael MohyaM387535214

Full Squad Data

Every player with 100+ minutes (21 players).

playerposappsmingoalsxGkey PprogdefxT
Philipp SanderM766300.45301507.9
Moritz NicolasG766300.0038158.3
Hugo BolinM757421.71027816.1
Kevin DiksD656901.1122997.7
Lukas UllrichM652301.0325944.6
Joe ScallyM752230.9319983.4
Wael MohyaM738721.6611531.7
Ko ItakuraD436600.719503.3
Daiki HashiokaD530400.1112602.5
Isac LidbergF628810.710540.2
Florian NeuhausM428310.5106512.4
Robin HackM727400.2311352.3
Tim KleindienstF326400.416431.1
Franck HonoratM424100.11120274.1
Shuto MachinoF522121.514381.0
Zento UnoM320100.113320.9
Jan LeszczynskiD319500.128462.9
Fabio ChiarodiaD316900.206251.7
Kevin StögerM316700.039212.7
Mathieu NguefackM516400.134321.4
David HeroldD415900.024301.2

Player Role Architecture

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

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

Philipp Sander
Ball-winner / destroyer · Midfielder
minutes663
goals · xG0 · 0.4
prog passes /904.1
key passes /900.68
def actions /9020.4
pass accuracy79%
Moritz Nicolas
Goalkeeper · Goalkeeper
minutes663
goals · xG0 · 0.0
prog passes /905.2
key passes /900.00
def actions /902.0
pass accuracy68%
Hugo Bolin
Ball-winner / destroyer · Midfielder
minutes574
goals · xG2 · 1.7
prog passes /904.2
key passes /901.57
def actions /9012.7
pass accuracy73%
Kevin Diks
Stopper / marker · Defender
minutes569
goals · xG0 · 1.1
prog passes /903.5
key passes /900.16
def actions /9015.7
pass accuracy89%
Lukas Ullrich
Ball-winner / destroyer · Midfielder
minutes523
goals · xG0 · 1.0
prog passes /904.3
key passes /900.52
def actions /9016.2
pass accuracy71%
Joe Scally
Ball-winner / destroyer · Midfielder
minutes522
goals · xG3 · 0.9
prog passes /903.3
key passes /900.52
def actions /9016.9
pass accuracy80%
Wael Mohya
Ball-winner / destroyer · Midfielder
minutes387
goals · xG2 · 1.6
prog passes /902.6
key passes /901.40
def actions /9012.3
pass accuracy84%
Ko Itakura
Stopper / marker · Defender
minutes366
goals · xG0 · 0.7
prog passes /902.2
key passes /900.25
def actions /9012.3
pass accuracy89%
Daiki Hashioka
Stopper / marker · Defender
minutes304
goals · xG0 · 0.1
prog passes /903.6
key passes /900.30
def actions /9017.8
pass accuracy69%

Recruitment Intelligence — profiles to sign

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

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

Need — bottom-third: xG conceded /game (11th pct)
Sign a defensive organiser / shot-suppressing centre-back. The data flags this because too much xG is conceded (2.28, 11th percentile vs the league).
Need — bottom-third: progression /game (17th pct)
Sign a progressive passer / ball-carrier to break lines. The data flags this because progression is limited (40.00, 17th percentile vs the league).
Need — bottom-third: box entries /game (17th pct)
Sign a penetrative wide player or runner to create box entries. The data flags this because few entries into the box (11.86, 17th percentile vs the league).
Vision AI · building report