Combining every season — this one is larger, give it a moment.
Team reportBundesliga 2026
Stuttgart
A possession-dominant side, aggressive high-press out of possession, with a high-volume attack. Currently #15 of 18 on 6 points (2-0-5, 10:13). 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 Stuttgart and how good was the season?
Read. Stuttgart currently sit #15 of 18 on 6 points (2W-0D-5L, 0.86 pts/game), scoring 10 and conceding 13 for a goal difference of -3.
On the ball they are a possession-dominant team (56% field tilt, #6 in the league); off it they defend with a aggressive high-press (#3 of 18 for press intensity).
Across the 7 matches of event data, they create 1.75 xG/game (#7) and concede 2.08 (#15), a net of -0.32 xG/game — the underlying performance matches the actual goal difference.
Season Context — the league table
The question
Where did Stuttgart actually finish, and against whom?
The authoritative final table (full season), centred on Stuttgart.
#
team
P
W
D
L
GF
GA
GD
Pts
12
Koln
7
2
2
3
8
11
-3
8
13
Paderborn
7
2
2
3
6
9
-3
8
14
Eintracht Frankfurt
7
2
2
3
10
17
-7
8
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 Stuttgart 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
Stuttgart tend to fade after a strong start — first-half net xG +0.84, second-half -3.11. Their strongest period is 0-15' (net +2.03 xG); their most dangerous period to face is 46-60' (net -1.61 xG against them).
xG Race — the season, match by match
Expected goals piling up over 7 matches: Stuttgart 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 (-2.3 xG). Hover a point for that match.
StuttgartOpponents against Stuttgart
Tactical DNA
The football question
In five genes, what is Stuttgart'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.
69
Build-Up
possession control from the back
78
Progression
moving the ball upfield with threat
63
Creation
manufacturing chances
75
Press
winning the ball high & hard
17
Solidity
resisting the opponent's threat
Genetic identity
Stuttgart are defined most by their Progression DNA (78/100 · strong at moving the ball upfield with threat), and least by their Solidity DNA (17/100). 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
43%
Possession
86%
Field tilt
86%
Disrupt
Central progression ~proxy
67%
Dribble tendency
0%
Progressive passes
81%
Finish
Final-third patience
62%
Shot quality
57%
Open-play xG
81%
Press
Forward press ~proxy
81%
High regains
81%
Pressing efficiency ~proxy
95%
Defend
Def-mid press ~proxy
86%
High line ~proxy
67%
PAdj defensive actions
76%
Stuttgart is defined most by Press (86% in the league) and weakest on Disrupt (49%). 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 Stuttgart actually control, relative to the rest of the league?
Field tilt = share of final-third touches between the two sides across Stuttgart'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; Stuttgart highlighted. Hover any dot for the team.
Vision AI Model Insight
Stuttgart post 56% field tilt, ranking #6 of 18 in the league — comfortably above the league mid-point for territorial control, and their 0.86 points/game sits below the league-average return — a real disconnect: their territorial control isn't showing up on the scoreboard the way it should.
Where the time goes — how long each phase lasts
The question
When Stuttgart has the ball, how much of that time is spent building, progressing and creating?
The share of Stuttgart'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
23.2%
Progression
50.5%
Chance creation
23.7%
Attacking transition
2.5%
Vision AI Model Insight
Progression is where Stuttgart spends most of its time on the ball (51% 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
Mittelstädt ↔ Stiller
125p · 0.5xT
Vagnoman ↔ Jeltsch
119p · -0.8xT
Mittelstädt ↔ Führich
91p · 0.9xT
Mittelstädt ↔ Hendriks
73p · -0.0xT
Chabot ↔ Stiller
73p · 0.0xT
Stiller ↔ Jeltsch
73p · 0.1xT
Chabot ↔ Jeltsch
65p · -0.3xT
Prömel ↔ Stiller
64p · 0.8xT
Führich ↔ Stiller
64p · 1.0xT
Stiller ↔ Hendriks
64p · 0.2xT
Vision AI Model Insight
Busiest connection: Maximilian Mittelstädt ↔ Angelo Stiller (125 passes). Most dangerous per pass: Deniz Undav ↔ Angelo Stiller (0.04 xT/pass over 31 passes) — the combination that hurts opponents most when it fires, not just the one used most.
Touch Density — where Stuttgart 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 Stuttgart 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
4050
ACTIONS
Carries
the ball moved with a player
744
ACTIONS
Vision AI Model Insight
Their single busiest zone is the left half-space of the middle third (12.3% of all actions). 65% of actions run through the inside lanes (centre + both half-spaces) versus 35% down the two wings — an inside-dominant, half-space build. By third: 27% defensive, 51% middle, 22% 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. Stuttgart leaned on their left (45%). The bars under the pitch put the league’s share beside theirs.
Stuttgart
45%27%28%
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. Stuttgart made 350 this season (50.0 a match); 31% 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
109
15.6
31%
28%
Left half-space
71
10.1
20%
17%
Centre
69
9.9
20%
14%
Right half-space
45
6.4
13%
15%
Right wing
56
8.0
16%
26%
Build-up2 sections
Build-Up — the pass map
The football question
How does Stuttgart 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.
1095build-up passes attempted
81%completed
30%reach the middle third
2%go long into the final third
Pass types
344CB · 39%
68Long Wide · 8%
9Long Central · 1%
346Wide → FB · 39%
124Central · 14%
Sequence success — build-up
129build-up sequences this season
15%succeed — reach the next third or a shot
40%turned over with nothing to show
CB344 · 39%
Long Wide68 · 8%
Long Central9 · 1%
Wide → FB346 · 39%
Central124 · 14%
◆ Vision AI Insight
Stuttgart build predominantly down the right, with Jeltsch as the fulcrum (134 build-up passes). Only 32% of their build-up passes actually advance a third — the rest is lateral circulation, the hallmark of a possession-controlling side that circulates patiently in midfield before progressing. Their single most-repeated pattern is C def → LHS def (60×).
Pass Explorer
Every pass of Stuttgart’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. 407 progressive passes across 7 matches (58.1/game).
Origin lane
Right wing
21%
Right half-space
13%
Central
15%
Left half-space
17%
Left wing
35%
Progression is left-dominant: 33% right side · 15% central · 52% left.
Who generates it
Maximilian Mittelstädt
64 · 16%
Chris Führich
45 · 11%
Angelo Stiller
42 · 10%
Josha Vagnoman
38 · 9%
Bilal El Khannouss
37 · 9%
Top two — Maximilian Mittelstädt & Chris Führich — account for 27% of all progression.
PROGRESSION
58.1 prog passes/game
mostly left
→
PENETRATION
27% reach final third
22% reach the box
→
THREAT
the other 73% is recycled
progress ≠ danger
What came of it.27% of progressive passes reached the final third and 22% the box; the rest was recycled in front of the block. 27% of the progression ran through two players, mostly down the left side.
Which lanes actually create the threat
Share of passes played from each lane against its share of the threat (xT) they add. Efficient = more threat than volume, sterile = lots of the ball, little damage.
Per lane — volume vs threat
Right wingbalanced
Right half-spacebalanced
Centralbalanced
Left half-spacebalanced
Left wingefficient
Progression — the pass map
The football question
Once Stuttgart 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.
2028progression passes attempted
86%completed
16%break into the final third
10%go backward into the defensive third
Pass types
540CB · 31%
37Long Wide · 2%
13Long Central · 1%
868Wide → FB · 50%
289Central · 17%
Sequence success — progression
244progression sequences this season
24%succeed — reach the next third or a shot
42%turned over with nothing to show
CB540 · 31%
Long Wide37 · 2%
Long Central13 · 1%
Wide → FB868 · 50%
Central289 · 17%
◆ Vision AI Insight
Stuttgart progress predominantly down the left, with Stiller the busiest midfield outlet (265 progression passes). Only 16% of passes that start in midfield actually break the final-third line — the rest (74%) is recycled back into midfield or (10%) played backward, the hallmark of a side that carries progression itself through midfield rather than just passing through it. Their most-repeated pattern is LHS mid → LW mid (118×) — 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
2%
Neutral / sideways
50%
Safe / backward
35%
16% of passes are progressive-or-better; the remaining 84% keep possession without advancing it. A control-first side — safe in build-up, reliant on a few players for the incision.
Where does Stuttgart actually progress the ball with their feet, not a pass?
153 progressive carries this season across the whole squad (21% of 744 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 43m. Most active carrier: Jeltsch (24). Filter to one player below.
First (54)Second (39)Third (31)Fourth (29)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 Stuttgart'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.
118shots
12.3xG
0.10xG per shot
7big chances (0.3+ xG)
10goals
#4key passes / game in the league
#6box entries / game in the league
Kind of attack, then how the shot was set up
Shots
Goals
xG
xG / shot
Attacking transition14% of shots
16
1
1.95
0.12
Cross
1
0
0.09
0.09
Cut-back
2
0
0.18
0.09
Pass into the box / short pass
10
1
1.31
0.13
Own creation (carry, dribble, rebound)
3
0
0.38
0.13
Settled attack64% of shots
76
9
8.26
0.11
Cross
16
2
2.54
0.16
Cut-back
8
1
1.19
0.15
Long ball
2
0
0.08
0.04
Pass into the box / short pass
34
3
2.99
0.09
Own creation (carry, dribble, rebound)
16
3
1.45
0.09
Set piece22% of shots
26
0
2.06
0.08
Corner
25
0
2.02
0.08
Free kick
1
0
0.03
0.03
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
Chris Führich
10
1.37
0
Bilal El Khannouss
8
1.19
0
Ramon Hendriks
4
0.92
0
Ermedin Demirovic
4
0.78
0
Jamie Leweling
5
0.77
1
Who takes them
Player
Shots
xG
Goals
Ermedin Demirovic
21
2.98
3
Dzenan Pejcinovic
15
1.91
0
Deniz Undav
13
1.45
0
Josha Vagnoman
9
0.90
2
Grischa Prömel
11
0.82
1
Vision AI Model Insight
The biggest source of Stuttgart's chances, by xG, is settled attack (8.3 of 12.3 xG, 76 of 118 shots), mostly pass into the box / short pass. 16 of the 118 shots (14%) came within 15 seconds of winning the ball. 26 of the 118 shots came from set pieces; 10 goals from 12.3 xG (-2.3).
Chance Creation — the pass map
The football question
How does Stuttgart 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
6CB · 7%
15Long Central · 17%
19Wide → FB · 22%
48Central · 55%
Sequence success — final-third
146final-third sequences this season
27%succeed — produce a shot
40%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
CB6 · 7%
Long Central15 · 17%
Wide → FB19 · 22%
Central48 · 55%
Who creates — volume × threat
16Mittelstädt
14Führich
10Khannouss
9Stiller
7Vagnoman
7Undav
5Leweling
88key passes #4 of 18
17.6box entries/game #6 of 18
43.0final-3rd entries/game #4 of 18
1.75xG/game #7 of 18
10goals #12 of 18
-2.3finishing vs xG #16 of 18
◆ Vision AI Insight
Stuttgart lean left — 48% of their key passes come from that flank and its half-space (42 of 88), most often from Führich (12). They funnel chances into the c of the final third, most often via LW att → C att (18 key passes, most often from Mittelstädt). Mittelstädt is the primary source overall. They generate 1.75 xG a game and finish it wasteful — they under-perform their xG, led by Ermedin Demirovic (3G), Josha Vagnoman (2G), Maximilian Mittelstädt (1G).
Crosses & Cutbacks — squad-wide
The question
How does Stuttgart deliver into the box from wide areas, and what comes of it?
106 crosses: 42 found a team-mate (40%), 16 led to a shot (15%). 31 cut-backs: 16 found a team-mate (52%), 10 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 Stuttgart actually cross from?
106 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. 32% come from one territory, the byline left wing; 11 come from the half-spaces and 0 from the centre.
Territory
Crosses
Share
Led to a shot
byline · left wing
34
32%
6
advanced · left wing
28
26%
2
byline · right wing
18
17%
5
advanced · right wing
10
9%
1
deep · right wing
4
4%
0
advanced · left half-space
3
3%
0
byline · right half-space
3
3%
1
byline · left half-space
3
3%
1
deep · right half-space
1
1%
0
deep · left wing
1
1%
0
advanced · right half-space
1
1%
0
Finishing3 sections
Shot Map
118 shots for 12.3 xG (1.75/game, #7 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.
High-volume attack. 16.9 shots/game at 0.104 xG/shot. Box entries 18/game (#6). Threat created 13.4 xT/game (#4).
Goal Sequences
The move behind each of Stuttgart’s 10 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 — 10 goals traced.
How Do They Score?
The question
Where do the goals actually come from — open play, dead balls, which foot?
Every goal Stuttgart scored across the loaded matches, broken down by how it was created and finished.
By situation
Open play
10 · 100%
Set pieces
0 · 0%
Penalties
0 · 0%
By finish
Right foot
5 · 50%
Left foot
3 · 30%
Header
2 · 20%
Vision AI Model Insight
10 goals scored. 100% come in open play; set pieces add 0%. 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
250
Failed forward pass
185
Failed take-on
50
Lost in the defensive third
204
Their weakest zone is the right half-space in the attacking third: 31% of 135 pass attempts there are lost (42 times). Separately, 34% 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 Stuttgart 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.
50
Pressing Index /100 · average
Intensity61
PPDA, high-press share, high-zone actions
Height67
how high up the pitch they defend
Effectiveness31
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
22%
C
23%
LHS
24%
LW
14%
By third (high / mid / low regain)
Def 45%
Mid 41%
Att 14%
Read
Ranked #3 of 18 for press intensity, this is a high press. 55% of defensive actions happen in the middle & attacking thirds, at an average height of 39. Of recoveries, 53% are won high up the pitch.
Every pressing metric, ranked in the league
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#3/18
lower = more intense; rank is what matters
Defensive actions / game
164
#14/18
High-press share
23%
#9/18
def actions won high up
Defensive-action height (0–100)
39.0
#6/18
avg pitch height of actions
High recoveries / game
20.0
#14/18
ball won in mid+att third
High-recovery share
53%
#10/18
share of recoveries won high
Opponent completion allowed
76%
#3/18
lower = more disruptive
Opponent progressive allowed
10.9%
#16/18
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 Stuttgart win it back with a defensive action?
Counted over the 178 opposition possessions that Stuttgart 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 Stuttgart step in. Stuttgart win it after 3.3 opponent passes in the first half-hour and after 3.2 after the hour mark (about the same).
PASSES THEY GET
3.8passes
median 3 · before Stuttgart win it
TIME THEY KEEP IT
10sec
median 5s · 25.4 wins per game
How many passes the opponent got in before the ball was won
49%
30%
12%
8%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
3.2
8s
15–30′
3.4
9s
30–45+′
5.0
13s
45–60′
5.1
16s
60–75′
3.9
10s
75–90+′
2.7
7s
average passesaverage seconds
Attitude in each third
Every other style number here says how much Stuttgart 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 Stuttgart are genuinely unusual, and a verdict is only written where something clears
0.85 sd — a side that is ordinary in a third is told so rather than given a label
for average play.
Build-up (own third) 1,095 passes
nothing unusual in this zone
Width of use-0.4120.16
Wide-channel share-0.320.29
Side bias-0.29-0.93
Pass length+0.0022.71
Long-ball share-0.120.22
Forward gain per pass-0.0511.26
Backward-pass share-0.220.19
Deep-pass share-0.040.28
Completion+0.200.81
Seconds between passes-0.782.86
Side used
left 24% · central 48% · right 28%
Progression (middle third) 2,028 passes
left-sided
Width of use-0.3824.76
Wide-channel share-0.660.36
Side bias+0.913.65
Pass length-0.3516.74
Long-ball share-0.800.07
Forward gain per pass-0.293.16
Backward-pass share-0.520.31
Deep-pass share-0.620.13
Completion+0.090.85
Seconds between passes-0.443.00
Side used
left 40% · central 31% · right 29%
Final third 927 passes
narrow, central, played inside, left-sided
Width of use-1.2928.43
Wide-channel share-1.190.49
Side bias+0.986.47
Pass length+0.2315.83
Long-ball share-0.100.09
Forward gain per pass+0.321.47
Backward-pass share-0.350.36
Deep-pass share+0.060.09
Completion+0.270.72
Seconds between passes-0.343.14
Side used
left 48% · central 24% · right 28%
Reference measured across roughly 3,500 match-teams
in this competition, per third, so the comparison is with Stuttgart'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 Stuttgart win or lose the ball, what actually happens in the seconds after?
Reconstructed from the full timestamped event stream: real possession spells (not zone-clustered), and shots that follow a regain within a fixed window. This phase of play needs ordered, per-event timestamps — which every match here carries.
Direct play share16%
#16 of 18 · reaches final third in ≤3 passes
Patient build-up share31%
#3 of 18 · spells of 6+ passes
Tempo (possession length)18.4s
#15 of 18 · shorter = faster tempo
Passes per possession5.0
#3 of 18
Counter-attack shots /game1.24
#7 of 18 · shot within 12s of a regain in own half
Press→shot /game3.37
#9 of 18 · shot within 8s of a high regain
Speed to final third after regain11.7s
#9 of 18 · lower = faster break
Sequence success — attacking transition
8attacking transition sequences this season
12%succeed — produce a shot
50%turned over with nothing to show
Sequence success — defensive transition
37defensive transition sequences this season
59%succeed — reach the next third or a shot
8%turned over with nothing to show
Vision AI Model Insight
Stuttgart are a patient build-up side: 16% of possessions reach the final third in 3 passes or fewer (#16 of 18), against 31% built patiently over 6+ passes (#3 of 18). Winning the ball back leads to a shot inside 12 seconds 1.24 times a game (#7), and a high regain converts to a shot within 8 seconds 3.37 times a game (#9 of 18) — a modest but real source of chances.
Defending the box2 sections
Vulnerability Map — how opponents hurt them
The report flipped: where do opponents create their threat against Stuttgart? 333 opponent progressive passes and 125 shots faced (14.5 xG against) across the season.
Opponent threat by lane (attacking into Stuttgart)
Right wing
25%
Right half-space
11%
Central
22%
Left half-space
17%
Left wing
25%
Where the danger came from. Opponents generated most of their threat through the left wing (25% of the danger conceded).
How Do They Concede?
The question
Where do the goals against Stuttgart actually come from — open play, dead balls, and who supplies them?
Every goal Stuttgart conceded across the loaded matches — the opponent's shots in Stuttgart'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 (94 total)
By situation
Open play
10 · 91%
Set pieces
1 · 9%
Penalties
0 · 0%
Vision AI Model Insight
11 goals conceded. 91% come in open play; set pieces account for only 9%, so dead-ball defending is not the main issue. 18% are headers — aerial defending is not where the damage comes from. Their supply line runs mainly through the right half-space of the attacking third (22% 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
10 g · 10.2 xG
Set pieces
0 g · 2.1 xG
Penalties
0 g
0% of goals come from set pieces (0 of 10), worth 2.1 xG from 26 shots. Set pieces are a secondary threat; the danger is in open play.
Where do Stuttgart's corners go?
46 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 (21)
Delivered into the box 100%Short 0%
Left side (25)
Delivered into the box 96%Short 4%
Where opponents attack Stuttgart from set pieces
26 shots from opponent corners against Stuttgart, 1 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 (11)
Shot 11Goals 0
Left flag (15)
Shot 15Goals 1
Tactical Strengths & Weaknesses
Strengths (top-third vs league)
press intensity (PPDA) — 4.14 (83th pct)
progression /game — 58.14 (78th pct)
threat created /game — 13.38 (78th pct)
pass retention % — 81.85 (72th pct)
field tilt % — 55.56 (67th pct)
Weaknesses (bottom-third vs league)
xG conceded /game — 2.08 (17th pct)
Style Archetype & Similar Teams
Team archetype: High-press possession side. Their closest stylistic matches in the league (cosine similarity on style metrics) — sides that play a similar way.
Hoffenheim
66%
Mainz
59%
Bayern
59%
Leverkusen
41%
Borussia Dortmund
33%
Performance vs Opponent Styles
The question
Not just how Stuttgart 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 Stuttgart's own passing behaviour to the result.
Results
opponent type
goals for/g
goals against/g
points/g
vs high-pressing opponents (2m)
1.50
2.50
1.50
vs mid-block opponents (2m)
0.50
1.50
0.00
vs low-block opponents (3m)
2.00
1.67
1.00
Behaviour — how Stuttgart actually play against each
opponent type
retention
directness (long balls)
prog. passes/g
xT/g
box entries/g
vs high-pressing opponents
76%
14%
46
10.8
15.0
vs mid-block opponents
81%
12%
54
12.6
14.0
vs low-block opponents
85%
10%
69
15.6
21.7
Vision AI Model Insight
Facing a high press, retention drops to 76% vs 85% against a low block (10pt lower under pressure), and directness (long-ball share) sits at 14% when pressed vs 10% 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. Stuttgart are possession-dominant with a aggressive high-press.
Their attack creates 1.75 xG a game and they concede 2.08 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 Stuttgart’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).
+11.4on-ball value, 7 matches
Where the value came from
Passes4050 actions+3.38
Carries744 actions-0.10
Dribbles94 actions+0.54
Shots118 actions+5.40
Ball wins375 actions+2.19
Most on-ball value (OBV)
Actions
OBV
per 100
Bilal El Khannouss
282
+1.81
+0.64
Ermedin Demirovic
151
+1.68
+1.11
Deniz Undav
197
+1.35
+0.68
Ramon Hendriks
437
+1.07
+0.25
Dzenan Pejcinovic
167
+1.02
+0.61
Maximilian Mittelstädt
561
+0.82
+0.15
Angelo Stiller
626
+0.66
+0.11
Josha Vagnoman
408
+0.64
+0.16
Biggest single moves
OBV
1 Dzenan Pejcinovic · shot
+0.484
2 Ramon Hendriks · pass
+0.449
3 Tiago Tomás · shot
+0.401
4 Bilal El Khannouss · pass
+0.397
5 Josha Vagnoman · pass
+0.361
6 Deniz Undav · take-on
+0.353
Set Pieces by Player
Who delivers Stuttgart’s dead balls and who finishes them this season. 46 corners taken, most by Maximilian Mittelstädt (22); 26 shots from corners and free kicks, 0 goals. “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
Maximilian Mittelstädt
22
21 (95%)
1
—
0.02
—
—
Finn Jeltsch
—
—
6
—
0.57
—
—
Jeff Chabot
—
—
5
—
0.64
—
—
Bilal El Khannouss
5
3 (60%)
3
—
0.13
—
—
Chris Führich
13
12 (92%)
—
—
0.00
—
—
Angelo Stiller
4
2 (50%)
2
—
0.06
1
—
Dzenan Pejcinovic
—
—
2
—
0.10
—
—
Ermedin Demirovic
—
—
2
—
0.13
—
—
Leo Sauer
—
—
1
—
0.09
—
—
Ramon Hendriks
—
—
1
—
0.12
—
—
Grischa Prömel
—
—
1
—
0.09
—
—
Deniz Undav
—
—
1
—
0.05
—
—
Josha Vagnoman
—
—
1
—
0.04
—
—
Jamie Leweling
1
0 (0%)
—
—
0.00
—
—
Top Performers
Ranked by Vision AI Score — the same tactical behavioural evaluation each player's own report page shows (min 189 minutes). Coloured chips are that player's strongest capability blocks.
Chance creation leaders — key passes /90 and box entries.
player
pos
min
key P /90
box entries
xT
Maximilian Mittelstädt
D
547
2.63
20
12.2
Chris Führich
M
401
3.14
21
7.3
Bilal El Khannouss
M
336
2.68
20
8.2
Angelo Stiller
M
585
1.38
9
11.1
Josha Vagnoman
M
548
1.15
8
7.0
Deniz Undav
M
453
1.39
8
4.1
Jamie Leweling
M
296
1.52
9
2.3
Ramon Hendriks
D
467
0.58
6
7.2
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)
Chris Führich
W
401
10 (30%)
12 (67%)
Maximilian Mittelstädt
FB
547
9 (11%)
7 (57%)
Defensive Volume Leaders
Who does the defensive work — tackles, interceptions, recoveries.
player
pos
min
def actions
tackles
intercept
recoveries
Jeff Chabot
D
524
155
12
7
19
Maximilian Mittelstädt
D
547
118
15
11
23
Finn Jeltsch
D
581
109
8
3
23
Ramon Hendriks
D
467
85
5
4
14
Josha Vagnoman
M
548
83
4
5
26
Angelo Stiller
M
585
79
3
6
25
Ermedin Demirovic
F
461
67
1
3
10
Grischa Prömel
M
434
63
2
2
12
Full Squad Data
Every player with 100+ minutes (17 players).
player
pos
apps
min
goals
xG
key P
prog
def
xT
Angelo Stiller
M
7
585
0
0.2
9
42
79
11.1
Finn Jeltsch
D
7
581
0
0.7
3
18
109
6.2
Josha Vagnoman
M
6
548
2
0.9
7
38
83
7.0
Maximilian Mittelstädt
D
6
547
1
0.3
16
64
118
12.2
Jeff Chabot
D
7
524
1
0.8
0
19
155
6.8
Ramon Hendriks
D
6
467
0
0.1
3
29
85
7.2
Ermedin Demirovic
F
7
461
3
3.0
2
3
67
1.4
Deniz Undav
M
7
453
0
1.4
7
17
61
4.1
Grischa Prömel
M
7
434
1
0.8
2
18
63
4.1
Chris Führich
M
6
401
1
0.3
14
45
49
7.3
Fabian Bredlow
G
4
377
0
0.0
0
24
23
5.5
Dzenan Pejcinovic
F
7
369
0
1.9
5
4
44
1.7
Bilal El Khannouss
M
7
336
1
0.6
10
37
47
8.2
Jamie Leweling
M
7
296
0
0.2
5
14
31
2.3
Dennis Seimen
G
3
280
0
0.0
0
14
13
3.1
Atakan Karazor
M
3
222
0
0.1
3
8
48
2.6
Leo Sauer
M
6
122
0
0.4
2
5
15
1.0
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.
Angelo Stiller
Deep-lying playmaker · Midfielder
minutes585
goals · xG0 · 0.2
prog passes /906.5
key passes /901.38
def actions /9012.2
pass accuracy89%
Finn Jeltsch
Stopper / marker · Defender
minutes581
goals · xG0 · 0.7
prog passes /902.8
key passes /900.46
def actions /9016.9
pass accuracy90%
Josha Vagnoman
Deep-lying playmaker · Midfielder
minutes548
goals · xG2 · 0.9
prog passes /906.2
key passes /901.15
def actions /9013.6
pass accuracy84%
Maximilian Mittelstädt
Advanced creator · Attacking mid
minutes547
goals · xG1 · 0.3
prog passes /9010.5
key passes /902.63
def actions /9019.4
pass accuracy79%
Jeff Chabot
Stopper / marker · Defender
minutes524
goals · xG1 · 0.8
prog passes /903.3
key passes /900.00
def actions /9026.6
pass accuracy91%
Ramon Hendriks
Ball-playing defender · Defender
minutes467
goals · xG0 · 0.1
prog passes /905.6
key passes /900.58
def actions /9016.4
pass accuracy88%
Ermedin Demirovic
Poacher / finisher · Forward
minutes461
goals · xG3 · 3.0
prog passes /900.6
key passes /900.39
def actions /9013.1
pass accuracy66%
Deniz Undav
Ball-winner / destroyer · Midfielder
minutes453
goals · xG0 · 1.4
prog passes /903.4
key passes /901.39
def actions /9012.1
pass accuracy72%
Grischa Prömel
Ball-winner / destroyer · Midfielder
minutes434
goals · xG1 · 0.8
prog passes /903.7
key passes /900.41
def actions /9013.1
pass accuracy83%
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 (17th pct)
Sign a defensive organiser / shot-suppressing centre-back. The data flags this because too much xG is conceded (2.08, 17th percentile vs the league).