Combining every season — this one is larger, give it a moment.
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.
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.
#
team
P
W
D
L
GF
GA
GD
Pts
15
Stuttgart
7
2
0
5
10
13
-3
6
16
Schalke
7
1
3
3
4
7
-3
6
17
Union Berlin
7
1
3
3
6
18
-12
6
18
Borussia Monchengladbach
7
1
0
6
11
22
-11
3
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)
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.
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).
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.
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.
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.
Phase
Share of the time
Build-up
28.2%
Progression
46.3%
Chance creation
23.4%
Attacking transition
2.1%
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.
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
Carries
the ball moved with a player
599
ACTIONS
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.
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.
Channel
Entries
Per match
Share
League share
Left wing
71
10.1
30%
28%
Left half-space
51
7.3
22%
17%
Centre
26
3.7
11%
14%
Right half-space
33
4.7
14%
15%
Right wing
52
7.4
22%
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%
Long Wide53 · 7%
Long Central13 · 2%
Wide → FB255 · 34%
Central80 · 11%
◆ 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.
From channel
To channel
From third
To third
Outcome
Minutes
Channel to channel · click a cell
Third to third · click a cell
Progression4 sections
How They Progress — the mechanism
Not just how many progressive passes, but where they start, who plays them, and whether they actually penetrate. 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%
Long Wide46 · 4%
Long Central9 · 1%
Wide → FB554 · 47%
Central185 · 16%
◆ 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.
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
Kind of attack, then how the shot was set up
Shots
Goals
xG
xG / shot
Attacking transition25% of shots
27
4
3.05
0.11
Cross
1
0
0.14
0.14
Cut-back
2
2
0.43
0.21
Long ball
2
0
0.23
0.12
Pass into the box / short pass
13
2
1.65
0.13
Own creation (carry, dribble, rebound)
9
0
0.60
0.07
Settled attack55% of shots
59
5
5.26
0.09
Cross
6
0
0.52
0.09
Cut-back
6
1
0.69
0.11
Pass into the box / short pass
33
2
2.68
0.08
Own creation (carry, dribble, rebound)
14
2
1.37
0.10
Set piece20% of shots
21
2
3.24
0.15
Corner
18
1
1.62
0.09
Free kick
1
0
0.03
0.03
Penalty
2
1
1.59
0.79
Every attempt to create — and what came of it
Every pass into the box, every cut-back and every pass before a shot. Red led to a shot, gold to a goal; grey ones were tried but no shot came: solid found a team-mate, dashed lost the ball. Filter by outcome, type or player; tap an arrow for the detail.
Who sets the shots up
Player
Set-ups
xG created
Goals
Joe Scally
3
1.52
1
Hugo Bolin
10
0.90
0
Franck Honorat
8
0.89
2
Florian Neuhaus
12
0.82
2
Wael Mohya
7
0.61
1
Who takes them
Player
Shots
xG
Goals
Hugo Bolin
18
1.71
2
Wael Mohya
11
1.62
2
Shuto Machino
9
1.50
2
Kevin Diks
5
1.11
0
Lukas Ullrich
8
0.98
0
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%
Long Wide1 · 1%
Long Central9 · 13%
Wide → FB15 · 21%
Central40 · 57%
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.
Territory
Crosses
Share
Led to a shot
byline · right wing
29
31%
4
advanced · right wing
21
22%
3
byline · left wing
17
18%
0
advanced · left wing
15
16%
0
deep · left wing
5
5%
0
deep · right wing
4
4%
0
deep · left half-space
1
1%
0
advanced · right half-space
1
1%
0
advanced · left half-space
1
1%
0
byline · right half-space
1
1%
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
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.
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
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#7/18
lower = more intense; rank is what matters
Defensive actions / game
166
#12/18
High-press share
23%
#7/18
def actions won high up
Defensive-action height (0–100)
38.9
#7/18
avg pitch height of actions
High recoveries / game
20.0
#14/18
ball won in mid+att third
High-recovery share
55%
#7/18
share of recoveries won high
Opponent completion allowed
81%
#13/18
lower = more disruptive
Opponent progressive allowed
9.1%
#6/18
of opp passes, lower = better
Transition timing
This source carries per-event timestamps, so genuine transition metrics — counter-attack shots, press→shot conversions, time-to-recover — are measured; see the dedicated Transitions section below.
How long they keep it — passes and seconds until the ball is won
The question
Once the opponent has the ball, how many passes and how many seconds pass before 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
Every opponent key pass against them (85 total)
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.
The tactical idea behind each phase of this team's play, written from the same numbers shown above — what they do in build-up, how they progress, where they create, and how they defend it.
How much of the goal threat is manufactured from dead balls — a route to goal that bypasses their open-play mechanism entirely.
Open play
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)
Delivered into the box 100%Short 0%
Left side (16)
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)
Shot 8Goals 1
Left flag (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 type
goals for/g
goals against/g
points/g
vs high-pressing opponents (2m)
2.00
2.00
1.50
vs mid-block opponents (2m)
2.00
3.00
0.00
vs low-block opponents (3m)
1.00
4.00
0.00
Behaviour — how Borussia Monchengladbach actually play against each
opponent type
retention
directness (long balls)
prog. passes/g
xT/g
box entries/g
vs high-pressing opponents
70%
20%
30
7.0
6.0
vs mid-block opponents
78%
15%
56
12.4
16.0
vs low-block opponents
79%
14%
36
10.1
13.0
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.]
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)
Actions
OBV
per 100
Hugo Bolin
275
+2.22
+0.81
Joe Scally
256
+1.67
+0.65
Kevin Diks
449
+1.41
+0.31
Shuto Machino
73
+1.33
+1.82
Wael Mohya
183
+1.07
+0.58
Florian Neuhaus
162
+1.00
+0.62
Philipp Sander
518
+0.82
+0.16
Franck Honorat
143
+0.68
+0.48
Biggest single moves
OBV
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.
Player
Corners taken
Into the box
Set-piece shots
Goals
xG
Free-kick shots
Penalties
Franck Honorat
9
8 (89%)
2
—
0.04
—
—
Shuto Machino
—
—
3
—
0.29
—
1/1
Hugo Bolin
5
4 (80%)
2
1
0.45
—
—
Robin Hack
11
10 (91%)
—
—
0.00
—
—
Philipp Sander
—
—
3
—
0.15
1
—
Ko Itakura
—
—
2
—
0.42
—
—
Joe Scally
—
—
2
—
0.03
—
—
Mathieu Nguefack
2
2 (100%)
1
—
0.02
—
—
David Herold
3
2 (67%)
—
—
0.00
—
—
Kevin Stöger
3
3 (100%)
—
—
0.00
—
—
Lukas Ullrich
3
3 (100%)
—
—
0.00
—
—
Nicolas Kühn
3
3 (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.
Chance creation leaders — key passes /90 and box entries.
player
pos
min
key P /90
box entries
xT
Hugo Bolin
M
574
1.57
15
6.1
Franck Honorat
M
241
4.11
12
4.1
Florian Neuhaus
M
283
3.18
5
2.4
Wael Mohya
M
387
1.40
8
1.7
Philipp Sander
M
663
0.68
9
7.9
Lukas Ullrich
M
523
0.52
5
4.6
Robin Hack
M
274
0.99
5
2.3
Joe Scally
M
522
0.52
3
3.4
Wide Delivery Leaders
Crosses and cutbacks — wingers and full-backs only (by real average position, not the raw D/M position code), ranked by combined volume.
player
pos
min
crosses (completed)
cutbacks (completed)
Franck Honorat
W
241
4 (25%)
5 (20%)
Wael Mohya
W
387
0
4 (75%)
Defensive Volume Leaders
Who does the defensive work — tackles, interceptions, recoveries.
player
pos
min
def actions
tackles
intercept
recoveries
Philipp Sander
M
663
150
18
12
38
Kevin Diks
D
569
99
7
3
14
Joe Scally
M
522
98
14
1
18
Lukas Ullrich
M
523
94
12
13
19
Hugo Bolin
M
574
81
11
2
28
Daiki Hashioka
D
304
60
3
5
7
Isac Lidberg
F
288
54
1
2
5
Wael Mohya
M
387
53
5
2
14
Full Squad Data
Every player with 100+ minutes (21 players).
player
pos
apps
min
goals
xG
key P
prog
def
xT
Philipp Sander
M
7
663
0
0.4
5
30
150
7.9
Moritz Nicolas
G
7
663
0
0.0
0
38
15
8.3
Hugo Bolin
M
7
574
2
1.7
10
27
81
6.1
Kevin Diks
D
6
569
0
1.1
1
22
99
7.7
Lukas Ullrich
M
6
523
0
1.0
3
25
94
4.6
Joe Scally
M
7
522
3
0.9
3
19
98
3.4
Wael Mohya
M
7
387
2
1.6
6
11
53
1.7
Ko Itakura
D
4
366
0
0.7
1
9
50
3.3
Daiki Hashioka
D
5
304
0
0.1
1
12
60
2.5
Isac Lidberg
F
6
288
1
0.7
1
0
54
0.2
Florian Neuhaus
M
4
283
1
0.5
10
6
51
2.4
Robin Hack
M
7
274
0
0.2
3
11
35
2.3
Tim Kleindienst
F
3
264
0
0.4
1
6
43
1.1
Franck Honorat
M
4
241
0
0.1
11
20
27
4.1
Shuto Machino
F
5
221
2
1.5
1
4
38
1.0
Zento Uno
M
3
201
0
0.1
1
3
32
0.9
Jan Leszczynski
D
3
195
0
0.1
2
8
46
2.9
Fabio Chiarodia
D
3
169
0
0.2
0
6
25
1.7
Kevin Stöger
M
3
167
0
0.0
3
9
21
2.7
Mathieu Nguefack
M
5
164
0
0.1
3
4
32
1.4
David Herold
D
4
159
0
0.0
2
4
30
1.2
Player Role Architecture
The question
Who does what — and what tactical job does each key player actually perform?
The nine most-used players (450+ minutes), each with the role their event fingerprint points to — not just their listed position.
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).