Combining every season — this one is larger, give it a moment.
Team reportBundesliga 2025/26
Borussia Monchengladbach
A territorially-dominated side, aggressive high-press out of possession, with a moderate attack. Finished #14 of 21 on 41 points (10-11-20, 53:75). Tactical profile below is built from 41 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 finished #14 of 21 on 41 points (10W-11D-20L, 1.00 pts/game), scoring 53 and conceding 75 for a goal difference of -22.
On the ball they are a territorially-dominated team (42% field tilt, #16 in the league); off it they defend with a aggressive high-press (#19 of 21 for press intensity).
Across the 41 matches of event data, they create 1.31 xG/game (#12) and concede 1.55 (#15), a net of -0.24 xG/game — the underlying performance diverges from 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
11
Hamburg
41
12
11
18
46
70
-24
47
12
Union Berlin
41
11
12
18
50
76
-26
45
13
Werder Bremen
41
11
11
19
52
74
-22
44
14
Borussia Monchengladbach
41
10
11
20
53
75
-22
41
15
Koln
41
9
13
19
57
74
-17
40
16
Wolfsburg
34
7
8
19
45
69
-24
29
17
Heidenheim
34
6
8
20
41
72
-31
26
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 -3.20, second-half -6.45. Their strongest period is 76-90+' (net -0.12 xG); their most dangerous period to face is 61-75' (net -5.56 xG against them).
xG Race — the season, match by match
Expected goals piling up over 41 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 (-9.6 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.
43
Build-Up
possession control from the back
24
Progression
moving the ball upfield with threat
32
Creation
manufacturing chances
17
Press
winning the ball high & hard
29
Solidity
resisting the opponent's threat
Genetic identity
Borussia Monchengladbach are defined most by their Build-Up DNA (43/100 · average at possession control from the back), and least by their Press DNA (17/100). Percentiles vs the 21 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 42% field tilt, ranking #16 of 21 in the league — below the league mid-point, spending more of the game defending their own territory than dictating it, and their 1.00 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
30.6%
Progression
48.0%
Chance creation
19.4%
Attacking transition
2.0%
Vision AI Model Insight
Progression is where Borussia Monchengladbach spends most of its time on the ball (48% of its time on the ball).
Passing Network
The question
Who actually plays with whom — the real on-pitch connections, not just average positions?
Every pass linked to its likely receiver (the next same-team touch), aggregated into player-pair combinations at each player's average position. Line width = how often the pair combines; colour = threat generated per pass (blue low → red high). Hover a line for the pair, or click a player to isolate their connections.
width = passes between the paircolour = xT per pass: low → high
Top 10 combinations
Elvedi ↔ Sander
523p · 13.0xT
Diks ↔ Elvedi
479p · 10.4xT
Elvedi ↔ Nicolas
382p · 12.9xT
Diks ↔ Nicolas
330p · 11.5xT
Elvedi ↔ Engelhardt
320p · 9.9xT
Diks ↔ Engelhardt
310p · 12.0xT
Elvedi ↔ Scally
282p · 12.9xT
Elvedi ↔ Reitz
233p · 9.9xT
Sander ↔ Reitz
225p · 10.7xT
Scally ↔ Reitz
223p · 10.5xT
Vision AI Model Insight
Busiest connection: Nico Elvedi ↔ Philipp Sander (523 passes). Most dangerous per pass: Franck Honorat ↔ Philipp Sander (0.08 xT/pass over 131 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 369 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
19312
ACTIONS
Carries
the ball moved with a player
4563
ACTIONS
Vision AI Model Insight
Their single busiest zone is the centre of the defensive third (12.8% of all actions). 62% of actions run through the inside lanes (centre + both half-spaces) versus 38% down the two wings — an inside-dominant, half-space build. By third: 33% defensive, 49% middle, 18% attacking.
Attack Sides
Which channel they attacked through this season: the share of every action made in the opponent’s half, down the left, through the middle and down the right. Borussia Monchengladbach leaned on their right (39%). The bars under the pitch put the league’s share beside theirs.
Borussia Monchengladbach
37%24%39%
League
38%24%38%
Final-Third Entries
Every completed pass or carry that took the ball from outside the final third into it, by the channel it arrived in. Borussia Monchengladbach made 1426 this season (34.8 a match); 28% came into the right wing. Longer, bolder arrows mean more entries. Entries by carrying the ball in count as well as completed passes; dribbles are not counted.
Channel
Entries
Per match
Share
League share
Left wing
353
8.6
25%
27%
Left half-space
235
5.7
16%
16%
Centre
201
4.9
14%
14%
Right half-space
240
5.9
17%
15%
Right wing
397
9.7
28%
28%
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.
6469build-up passes attempted
81%completed
27%reach the middle third
1%go long into the final third
Pass types
2609CB · 50%
292Long Wide · 6%
88Long Central · 2%
1700Wide → FB · 33%
520Central · 10%
Sequence success — build-up
1216build-up sequences this season
8%succeed — reach the next third or a shot
44%turned over with nothing to show
CB2609 · 50%
Long Wide292 · 6%
Long Central88 · 2%
Wide → FB1700 · 33%
Central520 · 10%
◆ Vision AI Insight
Borussia Monchengladbach build predominantly down the right, with Nicolas as the fulcrum (1128 build-up passes). Only 29% 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 (343×).
Pass Explorer
Every pass of Borussia Monchengladbach’s last 10 matches as an arrow, theirs first and the opponents’ with other team, each side attacking left to right (solid = completed, dashed grey = failed). Eight families per team — click any map to open it wide, then filter by channel or third, from one to another (or click a cell of the flow grids), by outcome, half, minute and passer. Channels: right wing, right half-space, centre, left half-space, left wing.
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. 1657 progressive passes across 41 matches (40.4/game).
Origin lane
Right wing
34%
Right half-space
13%
Central
18%
Left half-space
13%
Left wing
23%
Progression is right-dominant: 46% right side · 18% central · 36% left.
Who generates it
Moritz Nicolas
217 · 13%
Philipp Sander
163 · 10%
Franck Honorat
144 · 9%
Rocco Reitz
138 · 8%
Kevin Diks
136 · 8%
Top two — Moritz Nicolas & Philipp Sander — account for 23% of all progression.
PROGRESSION
40.4 prog passes/game
mostly right
→
PENETRATION
28% reach final third
21% reach the box
→
THREAT
the other 72% is recycled
progress ≠ danger
What came of it.28% of progressive passes reached the final third and 21% the box; the rest was recycled in front of the block. 23% 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.
9317progression passes attempted
85%completed
13%break into the final third
13%go backward into the defensive third
Pass types
3228CB · 41%
192Long Wide · 2%
43Long Central · 1%
3435Wide → FB · 44%
988Central · 13%
Sequence success — progression
1777progression sequences this season
25%succeed — reach the next third or a shot
43%turned over with nothing to show
CB3228 · 41%
Long Wide192 · 2%
Long Central43 · 1%
Wide → FB3435 · 44%
Central988 · 13%
◆ Vision AI Insight
Borussia Monchengladbach progress predominantly down the right, with Sander the busiest midfield outlet (954 progression passes). Only 13% of passes that start in midfield actually break the final-third line — the rest (74%) is recycled back into midfield or (13%) played backward, the hallmark of a side that carries progression itself through midfield rather than just passing through it. Their most-repeated pattern is RHS mid → RW mid (477×) — press that lane and midfield progression stalls.
Decision Quality — not all completed passes are equal
A 90% pass completion can hide a sideways habit. Grading every pass by the threat it adds separates value creation from safe circulation.
Excellent (high xT gain)
13%
Progressive
1%
Neutral / sideways
50%
Safe / backward
36%
14% of passes are progressive-or-better; the remaining 86% keep possession without advancing it. A control-first side — safe in build-up, reliant on a few players for the incision.
Where does Borussia Monchengladbach actually progress the ball with their feet, not a pass?
987 progressive carries this season across the whole squad (23% of 4317 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 58m. Most active carrier: Elvedi (193). Filter to one player below.
First (297)Second (260)Third (232)Fourth (198)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.
502shots
53.7xG
0.11xG per shot
34big chances (0.3+ xG)
51goals
#15key passes / game in the league
#16box entries / game in the league
Kind of attack, then how the shot was set up
Shots
Goals
xG
xG / shot
Attacking transition22% of shots
112
17
11.96
0.11
Cross
14
1
1.82
0.13
Cut-back
9
3
1.13
0.13
Long ball
5
0
0.48
0.10
Pass into the box / short pass
60
11
6.26
0.10
Own creation (carry, dribble, rebound)
24
2
2.28
0.09
Settled attack60% of shots
302
21
27.79
0.09
Cross
58
3
5.94
0.10
Cut-back
26
8
3.73
0.14
Long ball
9
1
0.89
0.10
Pass into the box / short pass
132
5
11.17
0.08
Own creation (carry, dribble, rebound)
77
4
6.06
0.08
Set piece18% of shots
88
13
13.98
0.16
Corner
74
7
6.69
0.09
Free kick
5
1
0.14
0.03
Penalty
9
5
7.15
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
Franck Honorat
47
6.27
8
Rocco Reitz
23
3.02
5
Joe Scally
21
2.89
4
Shuto Machino
17
1.85
0
Philipp Sander
12
1.56
0
Who takes them
Player
Shots
xG
Goals
Haris Tabakovic
67
12.13
13
Kevin Diks
28
5.68
5
Shuto Machino
35
4.49
5
Jens Castrop
30
3.21
3
Hugo Bolin
33
2.68
3
Vision AI Model Insight
The biggest source of Borussia Monchengladbach's chances, by xG, is settled attack (27.8 of 53.7 xG, 302 of 502 shots), mostly pass into the box / short pass. 112 of the 502 shots (22%) came within 15 seconds of winning the ball. 88 of the 502 shots came from set pieces; 51 goals from 53.7 xG (-2.7).
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
44CB · 13%
12Long Wide · 4%
38Long Central · 11%
96Wide → FB · 28%
151Central · 44%
Sequence success — final-third
1036final-third sequences this season
25%succeed — produce a shot
39%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
CB44 · 13%
Long Wide12 · 4%
Long Central38 · 11%
Wide → FB96 · 28%
Central151 · 44%
Who creates — volume × threat
55Honorat
27Stöger
26Reitz
21Scally
18Bolin
18Neuhaus
17Castrop
341key passes #15 of 21
11.8box entries/game #16 of 21
27.1final-3rd entries/game #15 of 21
1.31xG/game #12 of 21
51goals #12 of 21
-2.7finishing vs xG #14 of 21
◆ Vision AI Insight
Borussia Monchengladbach lean right — 43% of their key passes come from that flank and its half-space (145 of 341), most often from Honorat (42). They funnel chances into the c of the final third, most often via RW att → C att (60 key passes, most often from Honorat). Honorat is the primary source overall. They generate 1.31 xG a game and finish it wasteful — they under-perform their xG, led by Haris Tabakovic (13G), Kevin Diks (5G), Joe Scally (5G).
Crosses & Cutbacks — squad-wide
The question
How does Borussia Monchengladbach deliver into the box from wide areas, and what comes of it?
553 crosses: 186 found a team-mate (34%), 71 led to a shot (13%). 108 cut-backs: 47 found a team-mate (44%), 35 led to a shot (32%). 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?
553 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. 28% come from one territory, the byline right wing; 20 come from the half-spaces and 0 from the centre.
Territory
Crosses
Share
Led to a shot
byline · right wing
157
28%
22
advanced · right wing
129
23%
17
advanced · left wing
98
18%
13
byline · left wing
82
15%
8
deep · left wing
37
7%
4
deep · right wing
30
5%
2
byline · right half-space
7
1%
1
advanced · right half-space
5
1%
2
byline · left half-space
4
1%
2
advanced · left half-space
2
0%
0
deep · right half-space
1
0%
0
deep · left half-space
1
0%
0
Finishing3 sections
Shot Map
502 shots for 53.7 xG (1.31/game, #12 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.
Moderate attack. 12.2 shots/game at 0.107 xG/shot. Box entries 12/game (#16). Threat created 10.0 xT/game (#14).
Goal Sequences
The move behind each of Borussia Monchengladbach’s 53 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 — 53 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
38 · 75%
Set pieces
8 · 16%
Penalties
5 · 10%
By finish
Right foot
28 · 55%
Left foot
13 · 25%
Header
10 · 20%
Vision AI Model Insight
51 goals scored. 75% come in open play; set pieces add 16%. 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
1149
Failed forward pass
791
Failed take-on
347
Lost in the defensive third
1260
Their weakest zone is the right half-space in the attacking third: 35% of 564 pass attempts there are lost (198 times). Separately, 30% 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.
20
Pressing Index /100 · passive
Intensity6
PPDA, high-press share, high-zone actions
Height19
how high up the pitch they defend
Effectiveness19
high recoveries & high-regain share
Disruption40
opponent completion & progression allowed
Every defensive action & where it happens
Where they win the ball back — by lane
RW
19%
RHS
20%
C
23%
LHS
19%
LW
18%
By third (high / mid / low regain)
Def 55%
Mid 34%
Att 11%
Read
Ranked #19 of 21 for press intensity, this is a high press. Note the tension: 55% of their raw defensive actions still occur in their own third — a possession effect, not a passive one. Dominating the ball (17% high-press share) means that when they do defend, it is often deep. Of recoveries, 50% are won high up the pitch.
Every pressing metric, ranked in the league
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#19/21
lower = more intense; rank is what matters
Defensive actions / game
146
#11/21
High-press share
17%
#19/21
def actions won high up
Defensive-action height (0–100)
34.9
#17/21
avg pitch height of actions
High recoveries / game
20.1
#16/21
ball won in mid+att third
High-recovery share
50%
#18/21
share of recoveries won high
Opponent completion allowed
83%
#20/21
lower = more disruptive
Opponent progressive allowed
9.0%
#5/21
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 1267 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 6.2 opponent passes in the first half-hour and after 4.9 after the hour mark (sooner).
PASSES THEY GET
5.6passes
median 3 · before Borussia Monchengladbach win it
TIME THEY KEEP IT
16sec
median 8s · 30.9 wins per game
How many passes the opponent got in before the ball was won
38%
29%
14%
19%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
6.6
19s
15–30′
5.8
16s
30–45+′
6.3
18s
45–60′
5.2
14s
60–75′
5.5
16s
75–90+′
4.4
13s
average passesaverage seconds
Phases of play
Every moment of the match read twice — what Borussia Monchengladbach were doing with the
ball, and what the opposition were doing about it at that exact moment. Built from the
real action sequence in time order, so one possession is split across the phases it
genuinely passed through rather than labelled once by where it began.
6,168phase segments analysed
8.0smedian time before the opposition engage
2.0their passes per defensive action when pressed
#13of 18 for share of defending spent pressing high
How Borussia Monchengladbach defend when the opponent has it.
High press 6.0%Mid block 10.4%Low block 54.0%Unopposed 29.5%
What opponents do against Borussia Monchengladbach
The mirror image, and the part no other section can show: whether teams come
out to press Borussia Monchengladbach or sit in and invite them on. A side that is always faced with a low
block is playing a different sport from one that is pressed every week.
High press 9.5%Mid block 13.7%Low block 44.4%Unopposed 32.4%
Which phases actually produce
Phase
Segments
Avg length
Avg actions
Ends in shot
Turned over
Build-up
2,178
5.1s
3.1
0.1%
50.0%
Progression
2,618
6.6s
4.0
0.5%
50.6%
Chance creation
1,271
5.5s
3.2
28.0%
51.5%
Attacking transition
101
7.2s
4.5
3.0%
32.7%
Most common routes through the phases
How a possession actually travelled, in order. The chain is the tactical signature — a move that climbs all three thirds is a different thing from one that never leaves the first.
Route
Times
Ends in shot
build_up
886
0.2%
progression
653
0.3%
build_up → progression
424
0.0%
chance_creation
286
41.3%
progression → chance_creation
262
27.9%
build_up → progression → chance_creation
163
33.7%
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) 6,469 passes
nothing unusual in this zone
Width of use+0.1321.27
Wide-channel share+0.260.32
Side bias-0.22-0.68
Pass length-0.3621.42
Long-ball share-0.120.22
Forward gain per pass-0.2310.48
Backward-pass share+0.370.22
Deep-pass share-0.140.27
Completion+0.070.80
Seconds between passes-0.303.15
Side used
left 26% · central 45% · right 29%
Progression (middle third) 9,317 passes
nothing unusual in this zone
Width of use-0.2625.02
Wide-channel share-0.220.39
Side bias-0.13-0.47
Pass length-0.6316.34
Long-ball share-0.430.08
Forward gain per pass-0.333.11
Backward-pass share+0.370.35
Deep-pass share-0.200.15
Completion-0.100.84
Seconds between passes+0.113.24
Side used
left 34% · central 32% · right 35%
Final third 3,526 passes
nothing unusual in this zone
Width of use-0.2830.92
Wide-channel share-0.230.57
Side bias-0.34-2.89
Pass length+0.0215.51
Long-ball share+0.320.11
Forward gain per pass+0.381.55
Backward-pass share-0.360.36
Deep-pass share+0.070.09
Completion-0.390.66
Seconds between passes-0.173.27
Side used
left 38% · central 18% · right 44%
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%
#19 of 21 · reaches final third in ≤3 passes
Patient build-up share26%
#9 of 21 · spells of 6+ passes
Tempo (possession length)16.3s
#12 of 21 · shorter = faster tempo
Passes per possession4.5
#8 of 21
Counter-attack shots /game0.90
#18 of 21 · shot within 12s of a regain in own half
Press→shot /game2.78
#18 of 21 · shot within 8s of a high regain
Speed to final third after regain12.4s
#14 of 21 · lower = faster break
Sequence success — attacking transition
91attacking transition sequences this season
19%succeed — produce a shot
57%turned over with nothing to show
Sequence success — defensive transition
314defensive transition sequences this season
62%succeed — reach the next third or a shot
8%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 (#19 of 21), against 26% built patiently over 6+ passes (#9 of 21). Winning the ball back leads to a shot inside 12 seconds 0.90 times a game (#18), and a high regain converts to a shot within 8 seconds 2.78 times a game (#18 of 21) — 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? 2024 opponent progressive passes and 606 shots faced (63.4 xG against) across the season.
Opponent threat by lane (attacking into Borussia Monchengladbach)
Right wing
27%
Right half-space
14%
Central
16%
Left half-space
16%
Left wing
27%
Where the danger came from. Opponents generated most of their threat through the left wing (27% 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 (446 total)
By situation
Open play
58 · 79%
Set pieces
10 · 14%
Penalties
5 · 7%
Vision AI Model Insight
73 goals conceded. 79% come in open play; set pieces account for only 14%, so dead-ball defending is not the main issue. 15% are headers — aerial defending is not where the damage comes from. Their supply line runs mainly through the left half-space of the attacking third (20% of key passes faced) — that is the zone to shut off.
Context
Shape and tactics
Set pieces, strengths and weaknesses, and who they resemble.
How much of the goal threat is manufactured from dead balls — a route to goal that bypasses their open-play mechanism entirely.
Open play
38 g · 39.8 xG
Set pieces
8 g · 6.8 xG
Penalties
5 g
16% of goals come from set pieces (8 of 51), worth 6.8 xG from 79 shots. Set pieces are a secondary threat; the danger is in open play.
Where do Borussia Monchengladbach's corners go?
169 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 (99)
Delivered into the box 99%Short 1%
Left side (70)
Delivered into the box 96%Short 4%
Where opponents attack Borussia Monchengladbach from set pieces
92 shots from opponent corners against Borussia Monchengladbach, 9 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 (38)
Shot 38Goals 1
Left flag (54)
Shot 54Goals 8
Tactical Strengths & Weaknesses
Strengths (top-third vs league)
none stand out
Weaknesses (bottom-third vs league)
press intensity (PPDA) — 7.64 (10th pct)
progression /game — 40.41 (14th pct)
field tilt % — 41.97 (24th pct)
box entries /game — 11.76 (24th pct)
xG conceded /game — 1.55 (29th pct)
Style Archetype & Similar Teams
Team archetype: Direct / counter high-press. Their closest stylistic matches in the league (cosine similarity on style metrics) — sides that play a similar way.
Koln
94%
Hamburg
91%
Wolfsburg
76%
St. Pauli
68%
Heidenheim
66%
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 (13m)
1.54
2.69
0.77
vs mid-block opponents (13m)
1.15
1.54
0.92
vs low-block opponents (15m)
1.20
1.33
1.27
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
75%
17%
38
8.6
9.5
vs mid-block opponents
79%
14%
37
9.5
10.7
vs low-block opponents
83%
11%
46
11.7
14.6
Vision AI Model Insight
Facing a high press, retention drops to 75% vs 83% against a low block (8pt lower under pressure), and directness (long-ball share) sits at 17% when pressed vs 11% against a low block — a real style switch: they go noticeably more direct when pressed rather than trying to play through it.
Scouting Summary — the team in brief
In brief. Borussia Monchengladbach are territorially-dominated with a aggressive high-press.
Their attack creates 1.31 xG a game and they concede 1.55 a game.
[Event-data based — no tracking, so off-ball structure and marking scheme need video.]
For each of ten match numbers, the one match this season that departed furthest from Borussia Monchengladbach’s own average, the eight biggest swings first. Bar right of centre means more than usual, left means less. It shows how much the way they play moves from game to game, and which opponent moved it.
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 12 matches added up: where the value came from, who produced it, and the six biggest single moves. OBV only measures what a player does with the ball: it shows who added the most on-ball value, not who was the most important player (defending off the ball, positioning and pressing are not in it).
+17.7on-ball value, 12 matches
Where the value came from
Passes5492 actions+6.54
Carries1138 actions+0.12
Dribbles157 actions-0.25
Shots163 actions+7.75
Ball wins673 actions+3.49
Most on-ball value (OBV)
Actions
OBV
per 100
Hugo Bolin
365
+2.28
+0.63
Joe Scally
541
+1.88
+0.35
Kevin Diks
649
+1.46
+0.23
Shuto Machino
112
+1.43
+1.28
Franck Honorat
283
+1.38
+0.49
Philipp Sander
970
+1.32
+0.14
Rocco Reitz
259
+1.26
+0.49
Wael Mohya
273
+1.21
+0.44
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. 169 corners taken, most by Franck Honorat (47); 79 shots from corners and free kicks, 8 goals, led by Haris Tabakovic (4 goals, 1.88 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
47
41 (87%)
3
—
0.07
—
—
Rocco Reitz
26
26 (100%)
10
—
0.53
1
—
Kevin Stöger
39
34 (87%)
3
1
0.13
1
0/1
Kevin Diks
—
—
9
—
0.67
1
4/5
Haris Tabakovic
—
—
10
4
1.88
—
0/2
Philipp Sander
—
—
9
1
0.43
1
—
Luca Netz
17
15 (88%)
2
—
0.05
—
—
Hugo Bolin
6
5 (83%)
5
1
0.57
—
—
Robin Hack
14
13 (93%)
—
—
0.00
—
—
Yannik Engelhardt
—
—
4
1
0.96
—
—
Shuto Machino
—
—
3
—
0.29
—
1/1
Florian Neuhaus
2
2 (100%)
3
—
0.21
—
—
Lukas Ullrich
6
6 (100%)
1
—
0.02
—
—
Jens Castrop
—
—
3
—
0.16
—
—
Top Performers
Ranked by Vision AI Score — the same tactical behavioural evaluation each player's own report page shows (min 300 minutes). Coloured chips are that player's strongest capability blocks.
Chance creation leaders — key passes /90 and box entries.
player
pos
min
key P /90
box entries
xT
Franck Honorat
M
2274
2.18
74
25.5
Kevin Stöger
M
1568
1.55
48
27.1
Philipp Sander
D
3215
0.45
37
41.0
Florian Neuhaus
M
1246
1.30
22
15.4
Joe Scally
M
3282
0.58
19
22.7
Lukas Ullrich
M
1903
0.71
25
17.7
Jens Castrop
M
1727
0.89
22
10.9
Shuto Machino
F
1431
0.88
25
8.5
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
2274
40 (30%)
34 (35%)
Defensive Volume Leaders
Who does the defensive work — tackles, interceptions, recoveries.
player
pos
min
def actions
tackles
intercept
recoveries
Philipp Sander
D
3215
654
83
62
197
Nico Elvedi
D
3144
611
43
41
104
Kevin Diks
D
3171
540
50
27
145
Joe Scally
M
3282
477
62
18
131
Haris Tabakovic
F
2474
329
10
0
43
Lukas Ullrich
M
1903
283
40
34
71
Jens Castrop
M
1727
250
58
18
84
Kevin Stöger
M
1568
198
38
15
68
Full Squad Data
Every player with 100+ minutes (29 players).
player
pos
apps
min
goals
xG
key P
prog
def
xT
Moritz Nicolas
G
41
3910
0
0.0
2
217
94
45.9
Joe Scally
M
39
3282
5
2.5
21
111
477
22.7
Philipp Sander
D
37
3215
2
1.8
16
163
654
41.0
Kevin Diks
D
36
3171
5
5.7
9
136
540
40.7
Nico Elvedi
D
33
3144
0
0.5
12
79
611
30.2
Haris Tabakovic
F
32
2474
13
12.1
13
8
329
3.8
Franck Honorat
M
32
2274
2
1.3
55
144
196
25.5
Lukas Ullrich
M
29
1903
0
1.7
15
82
283
17.7
Jens Castrop
M
26
1727
3
3.2
17
53
250
10.9
Kevin Stöger
M
32
1568
1
2.4
27
105
198
27.1
Shuto Machino
F
37
1431
5
4.5
14
37
174
8.5
Florian Neuhaus
M
21
1246
1
1.9
18
50
158
15.4
Hugo Bolin
M
20
1164
3
2.7
18
39
134
8.8
Luca Netz
D
15
1009
0
0.1
10
46
117
8.7
Wael Mohya
M
22
983
4
2.4
9
22
117
4.2
Fabio Chiarodia
D
18
887
0
0.7
1
24
147
8.7
Robin Hack
M
15
625
1
1.3
13
39
74
8.1
Giovanni Reyna
M
19
606
1
0.6
12
25
74
7.6
Kota Takai
D
8
381
0
0.2
2
17
80
3.9
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
Tim Kleindienst
F
6
285
0
0.4
1
6
52
1.2
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
Marvin Friedrich
D
6
192
0
0.0
0
7
46
2.3
Grant-Leon Ranos
M
8
164
1
0.4
0
2
21
0.7
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.
Moritz Nicolas
Goalkeeper · Goalkeeper
minutes3910
goals · xG0 · 0.0
prog passes /905.0
key passes /900.05
def actions /902.2
pass accuracy73%
Joe Scally
Ball-winner / destroyer · Midfielder
minutes3282
goals · xG5 · 2.5
prog passes /903.0
key passes /900.58
def actions /9013.1
pass accuracy80%
Philipp Sander
Ball-playing defender · Defender
minutes3215
goals · xG2 · 1.8
prog passes /904.6
key passes /900.45
def actions /9018.3
pass accuracy84%
Kevin Diks
Stopper / marker · Defender
minutes3171
goals · xG5 · 5.7
prog passes /903.9
key passes /900.26
def actions /9015.3
pass accuracy89%
Nico Elvedi
Stopper / marker · Defender
minutes3144
goals · xG0 · 0.5
prog passes /902.3
key passes /900.34
def actions /9017.5
pass accuracy92%
Haris Tabakovic
Poacher / finisher · Forward
minutes2474
goals · xG13 · 12.1
prog passes /900.3
key passes /900.47
def actions /9012.0
pass accuracy67%
Franck Honorat
Advanced creator · Attacking mid
minutes2274
goals · xG2 · 1.3
prog passes /905.7
key passes /902.18
def actions /907.8
pass accuracy67%
Lukas Ullrich
Ball-winner / destroyer · Midfielder
minutes1903
goals · xG0 · 1.7
prog passes /903.9
key passes /900.71
def actions /9013.4
pass accuracy75%
Jens Castrop
Ball-winner / destroyer · Midfielder
minutes1727
goals · xG3 · 3.2
prog passes /902.8
key passes /900.89
def actions /9013.0
pass accuracy74%
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) (10th pct)
Sign a high-energy presser to lift defensive intensity. The data flags this because the press is passive (7.64, 10th percentile vs the league).
Need — bottom-third: progression /game (14th pct)
Sign a progressive passer / ball-carrier to break lines. The data flags this because progression is limited (40.41, 14th percentile vs the league).
Need — bottom-third: field tilt % (24th pct)
Sign a possession-controlling midfielder to win the territorial battle. The data flags this because they get pinned back (41.97, 24th percentile vs the league).
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