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Team reportLigue 1 2026

Strasbourg

A territorially-dominated side, aggressive high-press out of possession, with a moderate attack. Currently #16 of 18 on 7 points (2-1-5, 12:18). Tactical profile below is built from 8 matches of ball-by-ball event data, contextualised against the league.

territorially-dominatedaggressive high-pressmoderate attack
W
L
W
L
D
W
W
L
W
W
D
L
L
L
L
7
points
#16 of 18 · 8 games
12:18
goals
-6 GD
2-1-5
record
W-D-L
0.88
points/game
#16 in the league
1.22
xG for/game
rank #13
1.69
xG against/game
rank #15
#17
press rank
of 18 · most aggressive
42%
field tilt
#16
Strasbourg
League average
Expected goals for / game
1.221.41
Expected goals against / game
1.691.41
Field tilt
42%50%
Pass retention
84%83%
Overview

The team

Who they are and how the season went.

Who Are They? — Identity

The question
In one read, what kind of team is Strasbourg and how good was the season?
Read. Strasbourg currently sit #16 of 18 on 7 points (2W-1D-5L, 0.88 pts/game), scoring 12 and conceding 18 for a goal difference of -6. 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 (#17 of 18 for press intensity). Across the 8 matches of event data, they create 1.22 xG/game (#13) and concede 1.69 (#15), a net of -0.47 xG/game — the underlying performance matches the actual goal difference.

Season Context — the league table

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

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

#teamPWDLGFGAGDPts
13Brest82331412+29
14Angers8224915-68
15Le Mans81431116-57
16Strasbourg82151218-67
17Troyes8125516-115
18Le Havre8026815-72

Match Momentum — when they create and concede danger

The question
Across a whole game, which periods does Strasbourg actually dominate for chances — and which periods hurt them?

Net xG (created minus conceded) in each 15-minute window, summed across the whole season's matches — above the line is time they dominate for chances, below is time it hurts them. Real on-clock shot timing.

dominant (net xG for)conceding (net xG against)
even0-15'16-30'31-45'46-60'61-75'76-90+'+0.36-1.87
Vision AI Model Insight
Strasbourg tend to fade after a strong start — first-half net xG -1.51, second-half -2.23. Their strongest period is 31-45' (net +0.36 xG); their most dangerous period to face is 76-90+' (net -1.87 xG against them).

xG Race — the season, match by match

Expected goals piling up over 8 matches: Strasbourg 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 (-3.7 xG). Hover a point for that match.

0510159.813.5Marseille (A) 0-4 · xG 0.26 to 1.93Lens (H) 2-1 · xG 1.64 to 2.58Troyes (A) 6-2 · xG 2.98 to 2.41Monaco (H) 1-1 · xG 0.80 to 0.86Paris FC (A) 1-2 · xG 0.28 to 1.06Nice (A) 1-2 · xG 0.87 to 1.07PSG (H) 1-3 · xG 2.08 to 2.27Rennes (A) 0-3 · xG 0.87 to 1.34M1M2M3M4M5M6M7M8
StrasbourgOpponents against Strasbourg

Tactical DNA

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

33
Build-Up
possession control from the back
3
Progression
moving the ball upfield with threat
24
Creation
manufacturing chances
8
Press
winning the ball high & hard
17
Solidity
resisting the opponent's threat
Genetic identity
Strasbourg are defined most by their Build-Up DNA (33/100 · limited at possession control from the back), and least by their Progression DNA (3/100). 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).

Possess72Disrupt50Finish65Press35Defend42
Possess
Deep buildup
95%
Possession
65%
Field tilt
55%
Disrupt
Central progression ~proxy
55%
Dribble tendency
80%
Progressive passes
15%
Finish
Final-third patience
50%
Shot quality
85%
Open-play xG
60%
Press
Forward press ~proxy
30%
High regains
50%
Pressing efficiency ~proxy
25%
Defend
Def-mid press ~proxy
45%
High line ~proxy
50%
PAdj defensive actions
30%
Strasbourg is defined most by Possess (72% in the league) and weakest on Press (35%). Percentiles vs the 20 teams in the league this season.
In possession

In possession

How they keep the ball, build up, progress, create and finish, and where their attacks die.

Keeping the ball6 sections

Field Tilt — Territorial Control

The question
How much of the pitch does Strasbourg actually control, relative to the rest of the league?

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

PSG: 74% tilt, 2.12 pts/gRennes: 50% tilt, 2.00 pts/gLille: 44% tilt, 1.88 pts/gMonaco: 52% tilt, 1.88 pts/gLyon: 49% tilt, 1.75 pts/gParis FC: 46% tilt, 1.75 pts/gLens: 55% tilt, 1.62 pts/gLorient: 40% tilt, 1.50 pts/gMarseille: 58% tilt, 1.50 pts/gNice: 53% tilt, 1.38 pts/gToulouse: 51% tilt, 1.25 pts/gAuxerre: 52% tilt, 1.25 pts/gBrest: 44% tilt, 1.12 pts/gAngers: 43% tilt, 1.00 pts/gStrasbourg: 42% tilt, 0.88 pts/gLe Mans: 41% tilt, 0.88 pts/gTroyes: 54% tilt, 0.62 pts/gLe Havre: 51% tilt, 0.25 pts/gStrasbourg40%52%63%74%0.20.91.52.1FIELD TILT %POINTS / GAME
Vision AI Model Insight
Strasbourg post 42% field tilt, ranking #16 of 18 in the league — below the league mid-point, spending more of the game defending their own territory than dictating it, and their 0.88 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 Strasbourg has the ball, how much of that time is spent building, progressing and creating?

The share of Strasbourg's time on the ball spent in each phase of play, in the order the phases happen. A phase is where the ball is on the pitch (own third, middle third, final third); a transition or a set piece skips the build-up and is counted on its own.

Build-up: 34.6% of the time on the ball35%Progression: 44.0% of the time on the ball44%Chance creation: 19.6% of the time on the ball20%Attacking transition: 1.8% of the time on the ball44%PROGRESSION
PhaseShare of the time
Build-up34.6%
Progression44.0%
Chance creation19.6%
Attacking transition1.8%
Vision AI Model Insight
Progression is where Strasbourg spends most of its time on the ball (44% 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.

Filip Jörgensen ↔ Jeyland Mitchell: 149 passes, -0.7 combined xTJeyland Mitchell ↔ Genesis Antwi: 137 passes, -0.6 combined xTPape Diop ↔ Jeyland Mitchell: 89 passes, -0.0 combined xTGessime Yassine ↔ Genesis Antwi: 67 passes, 0.9 combined xTFilip Jörgensen ↔ Karim Coulibaly: 66 passes, -0.4 combined xTPape Diop ↔ Genesis Antwi: 57 passes, -0.4 combined xTOso ↔ Karim Coulibaly: 52 passes, -0.5 combined xTPape Diop ↔ Oso: 51 passes, 0.0 combined xTFilip Jörgensen ↔ Pape Diop: 43 passes, -0.2 combined xTGiovanni Reyna ↔ Pape Diop: 42 passes, 0.6 combined xTPape Diop ↔ Diogo Sousa: 41 passes, 1.2 combined xTPape Diop ↔ Gessime Yassine: 38 passes, 0.0 combined xTDiogo Sousa ↔ Jeyland Mitchell: 38 passes, 0.7 combined xTJeyland Mitchell ↔ Karim Coulibaly: 38 passes, 0.1 combined xTGiovanni Reyna ↔ Oso: 36 passes, 0.3 combined xTDário Essugo ↔ Oso: 31 passes, 0.1 combined xTDiogo Sousa ↔ Gessime Yassine: 31 passes, 1.2 combined xTDiogo Sousa ↔ Genesis Antwi: 31 passes, 0.3 combined xTPape Diop ↔ Karim Coulibaly: 30 passes, -0.2 combined xTGiovanni Reyna ↔ Genesis Antwi: 26 passes, -0.1 combined xTDário Essugo ↔ Pape Diop: 25 passes, 0.1 combined xTGiovanni Reyna ↔ Gessime Yassine: 22 passes, 0.0 combined xTSam Amo-Ameyaw ↔ Genesis Antwi: 22 passes, 0.6 combined xTFilip Jörgensen ↔ Genesis Antwi: 20 passes, -0.1 combined xTGiovanni Reyna — isolate this player's connectionsReynaFilip Jörgensen — isolate this player's connectionsJörgensenPape Diop — isolate this player's connectionsDiopGessime Yassine — isolate this player's connectionsYassineGenesis Antwi — isolate this player's connectionsAntwiJacobo Ortega — isolate this player's connectionsOrtegaJeyland Mitchell — isolate this player's connectionsMitchellSam Amo-Ameyaw — isolate this player's connectionsAmo-AmeyawDiogo Sousa — isolate this player's connectionsSousaJunior Mwanga — isolate this player's connectionsMwangaOso — isolate this player's connectionsOsoSamir El Mourabet — isolate this player's connectionsMourabetDário Essugo — isolate this player's connectionsEssugoDilane Bakwa — isolate this player's connectionsBakwaConrad Harder — isolate this player's connectionsHarderKarim Coulibaly — isolate this player's connectionsCoulibaly
width = passes between the paircolour = xT per pass: low → high

Top 10 combinations

Jörgensen ↔ Mitchell
149p · -0.7xT
Mitchell ↔ Antwi
137p · -0.6xT
Diop ↔ Mitchell
89p · -0.0xT
Yassine ↔ Antwi
67p · 0.9xT
Jörgensen ↔ Coulibaly
66p · -0.4xT
Diop ↔ Antwi
57p · -0.4xT
Oso ↔ Coulibaly
52p · -0.5xT
Diop ↔ Oso
51p · 0.0xT
Jörgensen ↔ Diop
43p · -0.2xT
Reyna ↔ Diop
42p · 0.6xT
Vision AI Model Insight
Busiest connection: Filip Jörgensen ↔ Jeyland Mitchell (149 passes). Most dangerous per pass: Diogo Sousa ↔ Gessime Yassine (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 Strasbourg had the ball

Every touch across 72 matches. Brighter means more touches.

Which Zones Do They Attack Through?

The question
Across the whole pitch, where does Strasbourg 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
3862
ACTIONS
5%6%14%6%5%7%10%11%10%8%4%3%3%3%4%PASSES
Carries
the ball moved with a player
1081
ACTIONS
4%6%13%7%4%8%10%9%10%10%6%3%2%2%5%CARRIES
Vision AI Model Insight
Their single busiest zone is the centre of the defensive third (13.7% 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: 36% defensive, 46% 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. Strasbourg leaned on their right (40%). The bars under the pitch put the league’s share beside theirs.

36%Left side24%Middle40%Right sideATTACKING →
Strasbourg
36%24%40%
League
38%22%40%

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. Strasbourg made 288 this season (36.0 a match); 29% came into the right wing. Longer, bolder arrows mean more entries. Entries by carrying the ball in count as well as completed passes; dribbles are not counted.

Left wing: 76 entries (26%)7626%LEFT WINGLeft half-space: 49 entries (17%)4917%LEFT HALF-SPACECentre: 47 entries (16%)4716%CENTRERight half-space: 33 entries (11%)3311%RIGHT HALF-SPACERight wing: 83 entries (29%)8329%RIGHT WINGATTACKING ↑
ChannelEntriesPer matchShareLeague share
Left wing769.526%30%
Left half-space496.117%13%
Centre475.916%11%
Right half-space334.111%14%
Right wing8310.429%32%
Build-up2 sections

Build-Up — the pass map

The football question
How does Strasbourg 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.

1408build-up passes attempted
83%completed
25%reach the middle third
1%go long into the final third

Pass types

430CB · 37%
64Long Wide · 5%
13Long Central · 1%
538Wide → FB · 46%
128Central · 11%

Sequence success — build-up

112build-up sequences this season
8%succeed — reach the next third or a shot
43%turned over with nothing to show
CB430 · 37%
left: 220 passes, most from Mitchell220right: 210 passes, most from Mitchell210
Long Wide64 · 5%
left: 47 passes, most from Jörgensen47right: 17 passes, most from Jörgensen17
Long Central13 · 1%
left: 7 passes, most from Jörgensen7right: 6 passes, most from Jörgensen6
Wide → FB538 · 46%
right: 281 passes, most from Jörgensen281left: 257 passes, most from Jörgensen257
Central128 · 11%
right: 66 passes, most from Jörgensen66left: 62 passes, most from Jörgensen62
◆ Vision AI Insight
Strasbourg build predominantly down the right, with Jörgensen as the fulcrum (302 build-up passes). Only 26% 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 (94×).

Pass Explorer

Every pass of Strasbourg’s last 8 matches as an arrow, theirs first and the opponents’ with other team, each side attacking left to right (solid = completed, dashed grey = failed). Eight families per team — click any map to open it wide, then filter by channel or third, from one to another (or click a cell of the flow grids), by outcome, half, minute and passer. Channels: right wing, right half-space, centre, left half-space, left wing.

Progression4 sections

How They Progress — the mechanism

Not just how many progressive passes, but where they start, who plays them, and whether they actually penetrate. 285 progressive passes across 8 matches (35.6/game).

Origin lane

Right wing
24%
Right half-space
14%
Central
21%
Left half-space
16%
Left wing
25%
Progression is left-dominant: 38% right side · 21% central · 41% left.

Who generates it

Filip Jörgensen
43 · 15%
Jeyland Mitchell
32 · 11%
Oso
32 · 11%
Sebastian Nanasi
29 · 10%
Gessime Yassine
28 · 10%
Top two — Filip Jörgensen & Jeyland Mitchell — account for 26% of all progression.
PROGRESSION
35.6 prog passes/game
mostly left
→
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. 26% 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 wingbalanced

Progression — the pass map

The football question
Once Strasbourg 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.

1752progression passes attempted
89%completed
14%break into the final third
14%go backward into the defensive third

Pass types

467CB · 30%
49Long Wide · 3%
4Long Central · 0%
814Wide → FB · 52%
217Central · 14%

Sequence success — progression

157progression sequences this season
20%succeed — reach the next third or a shot
39%turned over with nothing to show
CB467 · 30%
right: 243 passes, most from Mitchell243left: 224 passes, most from Mitchell224
Long Wide49 · 3%
right: 25 passes, most from Antwi25left: 24 passes, most from Oso24
Long Central4 · 0%
left: 3 passes, most from Yassine3
Wide → FB814 · 52%
left: 427 passes, most from Oso427right: 387 passes, most from Antwi387
Central217 · 14%
left: 111 passes, most from Diop111right: 106 passes, most from Diop106
◆ Vision AI Insight
Strasbourg progress predominantly down the left, with Diop the busiest midfield outlet (247 progression passes). Only 14% of passes that start in midfield actually break the final-third line — the rest (73%) 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 C mid → RHS mid (79×) — 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)
12%
Progressive
1%
Neutral / sideways
49%
Safe / backward
39%
12% of passes are progressive-or-better; the remaining 88% keep possession without advancing it. A control-first side — safe in build-up, reliant on a few players for the incision.

Carries Map — progressive ball-carries, squad-wide

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

211 progressive carries this season across the whole squad (20% of 1081 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 64m. Most active carrier: Oso (31). Filter to one player below.

First (59)Second (55)Third (54)Fourth (43)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 Strasbourg'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.

98shots
9.8xG
0.10xG per shot
5big chances (0.3+ xG)
10goals
#14key passes / game in the league
#15box entries / game in the league
Attacking transition: 26 shots (27%)27%26Settled attack: 60 shots (61%)61%60Set piece: 12 shots (12%)12%1298SHOTS
Kind of attack, then how the shot was set upShotsGoalsxGxG / shot
Attacking transition 27% of shots2652.400.09
Cross210.120.06
Cut-back210.440.22
Long ball100.090.09
Pass into the box / short pass1621.410.09
Own creation (carry, dribble, rebound)510.340.07
Settled attack 61% of shots6055.590.09
Cross400.190.05
Cut-back1121.520.14
Long ball300.140.05
Pass into the box / short pass2932.780.10
Own creation (carry, dribble, rebound)1300.960.07
Set piece 12% of shots1201.790.15
Corner901.720.19
Free kick300.080.03

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

PlayerSet-upsxG createdGoals
Gessime Yassine91.372
Oso81.071
Sebastian Nanasi70.742
Diogo Sousa60.721
Giovanni Reyna80.470

Who takes them

PlayerShotsxGGoals
Diogo Sousa91.281
Gessime Yassine111.152
Jacobo Ortega71.101
Jeyland Mitchell41.100
Dário Essugo70.941
Vision AI Model Insight
The biggest source of Strasbourg's chances, by xG, is settled attack (5.6 of 9.8 xG, 60 of 98 shots), mostly pass into the box / short pass. 26 of the 98 shots (27%) came within 15 seconds of winning the ball. 12 of the 98 shots came from set pieces; 10 goals from 9.8 xG (+0.2).

Chance Creation — the pass map

The football question
How does Strasbourg 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%
3Long Wide · 4%
2Long Central · 3%
22Wide → FB · 31%
38Central · 54%

Sequence success — final-third

105final-third sequences this season
26%succeed — produce a shot
36%turned over with nothing to show

Danger Funnel — which service carries threat

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

0 key passes shown● low threat● mid● high threatarrow width = total xT, not count
CB5 · 7%
right: 3 passes, most from Høgsberg3left: 2 passes, most from Mitchell2
Long Wide3 · 4%
right: 2 passes, most from Nanasi2
Long Central2 · 3%
Wide → FB22 · 31%
left: 14 passes, most from Reyna14right: 8 passes, most from Sousa8
Central38 · 54%
left: 19 passes, most from Yassine19right: 19 passes, most from Reyna19

Who creates — volume × threat

10Reyna
9Yassine
7Diop
7Oso
7Nanasi
6Sousa
5Harder
70key passes #14 of 18
11.0box entries/game #15 of 18
27.8final-3rd entries/game #16 of 18
1.22xG/game #13 of 18
10goals #8 of 18
+0.2finishing vs xG #5 of 18
◆ Vision AI Insight
Strasbourg lean right — 39% of their key passes come from that flank and its half-space (27 of 70), most often from Yassine (9). They funnel chances into the c of the final third, most often via RHS att → C att (14 key passes, most often from Yassine). Reyna is the primary source overall. They generate 1.22 xG a game and finish it clinical — they beat their xG, led by Sam Amo-Ameyaw (2G), Giovanni Reyna (2G), Gessime Yassine (2G).

Crosses & Cutbacks — squad-wide

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

91 crosses: 32 found a team-mate (35%), 10 led to a shot (11%). 24 cut-backs: 14 found a team-mate (58%), 10 led to a shot (42%). 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 Strasbourg actually cross from?

91 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; 7 come from the half-spaces and 0 from the centre.

deep right wing: 4 crosses (4%)4%4deep right half-space: 0 crosses (0%)deep centre: 0 crosses (0%)deep left half-space: 0 crosses (0%)deep left wing: 4 crosses (4%)4%4advanced right wing: 20 crosses (22%)22%20advanced right half-space: 2 crosses (2%)2%2advanced centre: 0 crosses (0%)advanced left half-space: 1 crosses (1%)1%1advanced left wing: 9 crosses (10%)10%9byline right wing: 28 crosses (31%)31%28byline right half-space: 2 crosses (2%)2%2byline centre: 0 crosses (0%)byline left half-space: 2 crosses (2%)2%2byline left wing: 19 crosses (21%)21%19
TerritoryCrossesShareLed to a shot
byline · right wing2831%2
advanced · right wing2022%4
byline · left wing1921%0
advanced · left wing910%1
deep · right wing44%1
deep · left wing44%0
advanced · right half-space22%1
byline · right half-space22%0
byline · left half-space22%1
advanced · left half-space11%0
Finishing3 sections

Shot Map

98 shots for 9.8 xG (1.22/game, #13 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 0.100 xG/shot. Box entries 11/game (#15). Threat created 9.4 xT/game (#17).

Goal Sequences

The move behind each of Strasbourg’s 12 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 — 12 goals traced.

How Do They Score?

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

Every goal Strasbourg 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
4 · 40%
Left foot
6 · 60%
Header
0 · 0%
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

56 of 210 pass attempts lost here (27%).27%56/210Antwi32 of 239 pass attempts lost here (13%).13%32/239Mitchell78 of 532 pass attempts lost here (15%).15%78/532Jörgensen19 of 216 pass attempts lost here (9%).9%19/216Jörgensen50 of 211 pass attempts lost here (24%).24%50/211Oso29 of 279 pass attempts lost here (10%).10%29/279Yassine52 of 375 pass attempts lost here (14%).14%52/375Mitchell40 of 413 pass attempts lost here (10%).10%40/413Diop45 of 372 pass attempts lost here (12%).12%45/372Oso35 of 313 pass attempts lost here (11%).11%35/313Oso51 of 172 pass attempts lost here (30%).30%51/172Yassine38 of 134 pass attempts lost here (28%).28%38/134Yassine19 of 102 pass attempts lost here (19%).19%19/102Ortega27 of 129 pass attempts lost here (21%).21%27/129Nanasi39 of 165 pass attempts lost here (24%).24%39/165Osoattacking →

How they lose it

Incomplete pass — attacking third
174
Failed forward pass
115
Failed take-on
99
Lost in the defensive third
235
Their weakest zone is the right wing in the attacking third: 30% of 172 pass attempts there are lost (51 times). Separately, 29% of all losses happen in the attacking third — a fair share happen deeper, which invites transitions.
Out of possession

Out of possession

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

Pressing2 sections

Pressing — the full picture

The football question
How hard does Strasbourg 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.

17
Pressing Index
/100 · passive
Intensity11
PPDA, high-press share, high-zone actions
Height17
how high up the pitch they defend
Effectiveness11
high recoveries & high-regain share
Disruption33
opponent completion & progression allowed

Every defensive action & where it happens

Where they win the ball back — by lane

RW
23%
RHS
21%
C
18%
LHS
21%
LW
16%
By third (high / mid / low regain)
Def 51%
Mid 35%
Att 13%
Read
Ranked #17 of 18 for press intensity, this is a high press. Note the tension: 51% of their raw defensive actions still occur in their own third — a possession effect, not a passive one. Dominating the ball (21% high-press share) means that when they do defend, it is often deep. Of recoveries, 47% are won high up the pitch.

Every pressing metric, ranked in the league

metricvaluerankmeaning
PPDA (opp passes per def action)—#17/18lower = more intense; rank is what matters
Defensive actions / game142#17/18
High-press share21%#15/18def actions won high up
Defensive-action height (0–100)36.7#15/18avg pitch height of actions
High recoveries / game20.4#15/18ball won in mid+att third
High-recovery share47%#17/18share of recoveries won high
Opponent completion allowed87%#17/18lower = more disruptive
Opponent progressive allowed8.3%#7/18of opp passes, lower = better
Transition timing
This source carries per-event timestamps, so genuine transition metrics — counter-attack shots, press→shot conversions, time-to-recover — are measured; see the dedicated Transitions section below.

How long they keep it — passes and seconds until the ball is won

The question
Once the opponent has the ball, how many passes and how many seconds pass before Strasbourg win it back with a defensive action?

Counted over the 275 opposition possessions that Strasbourg 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 Strasbourg step in. Strasbourg win it after 7.0 opponent passes in the first half-hour and after 6.6 after the hour mark (sooner).

PASSES THEY GET
6.6passes
median 4 · before Strasbourg win it
TIME THEY KEEP IT
19sec
median 11s · 34.4 wins per game
How many passes the opponent got in before the ball was won
37%
20%
20%
24%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
6.1
18s
15–30′
7.9
25s
30–45+′
6.5
18s
45–60′
6.0
17s
60–75′
6.5
20s
75–90+′
6.6
18s
average passesaverage seconds

Attitude in each third

Every other style number here says how much Strasbourg 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 Strasbourg 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) 1,408 passes

nothing unusual in this zone

Width of use+0.0721.14
Wide-channel share+0.150.31
Side bias-0.05-0.02
Pass length-0.7619.95
Long-ball share-0.540.18
Forward gain per pass-0.519.24
Backward-pass share+0.370.22
Deep-pass share-0.580.23
Completion+0.360.82
Seconds between passes+0.293.50
Side used

left 28% · central 44% · right 28%

Progression (middle third) 1,752 passes

more recycling, circulating

Width of use-0.7124.07
Wide-channel share-0.750.35
Side bias+0.160.70
Pass length-0.6616.31
Long-ball share-0.580.08
Forward gain per pass-0.872.29
Backward-pass share+1.080.38
Deep-pass share-0.750.12
Completion+0.550.88
Seconds between passes+0.693.50
Side used

left 34% · central 33% · right 33%

Final third 702 passes

narrow, central

Width of use-1.0529.01
Wide-channel share-0.710.53
Side bias-0.04-0.73
Pass length-0.4214.82
Long-ball share-0.130.09
Forward gain per pass+0.051.11
Backward-pass share+0.050.39
Deep-pass share+0.120.09
Completion+0.730.76
Seconds between passes+0.143.50
Side used

left 39% · central 20% · right 41%

Reference measured across roughly 3,500 match-teams in this competition, per third, so the comparison is with Strasbourg'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 Strasbourg 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%
#14 of 18 · reaches final third in ≤3 passes
Patient build-up share36%
#2 of 18 · spells of 6+ passes
Tempo (possession length)21.1s
#15 of 18 · shorter = faster tempo
Passes per possession5.6
#3 of 18
Counter-attack shots /game0.95
#11 of 18 · shot within 12s of a regain in own half
Press→shot /game2.79
#12 of 18 · shot within 8s of a high regain
Speed to final third after regain15.3s
#18 of 18 · lower = faster break

Sequence success — attacking transition

8attacking transition sequences this season
0%succeed — produce a shot
88%turned over with nothing to show

Sequence success — defensive transition

31defensive transition sequences this season
58%succeed — reach the next third or a shot
13%turned over with nothing to show
Vision AI Model Insight
Strasbourg are a patient build-up side: 15% of possessions reach the final third in 3 passes or fewer (#14 of 18), against 36% built patiently over 6+ passes (#2 of 18). Winning the ball back leads to a shot inside 12 seconds 0.95 times a game (#11), and a high regain converts to a shot within 8 seconds 2.79 times a game (#12 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 Strasbourg? 378 opponent progressive passes and 131 shots faced (13.5 xG against) across the season.

Opponent threat by lane (attacking into Strasbourg)

Right wing
38%
Right half-space
11%
Central
10%
Left half-space
11%
Left wing
29%
Where the danger came from. Opponents generated most of their threat through the right wing (38% of the danger conceded).

How Do They Concede?

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

Every goal Strasbourg conceded across the loaded matches — the opponent's shots in Strasbourg's own matches — plus every individual opponent key pass faced, each drawn as its own real arrow (not zone-clustered) with the % share of each zone behind it.

Where the goals go in

← opponent shot at own goal

Every opponent key pass against them (95 total)

2%1%2%1%1%3%17%20%20%17%16%Anis OuzenadjiHugo PicardLucas MaronnierHugo PicardHugo PicardMerwan IfnaouiDermane KarimMerwan IfnaouiRenaud RipartAdrien MonfrayLucas MaronnierAntoine MilleAntoine MilleLucas MaronnierAnis OuzenadjiTimothé NkadaKandet DiawaraAntoine MilleAnis OuzenadjiAnis OuzenadjiKandet DiawaraAbdallah SimaFlorian ThauvinFlorian ThauvinMatthieu UdolMichal SkórasMatthieu UdolFlorian ThauvinFlorian ThauvinFlorian ThauvinAndrija BulatovicMezian MesloubRuben AguilarAdama CamaraPierre Lees-MelouMaxime LopezMaxime LopezIlan KebbalLassine SinayokoLuca KoleoshoLassine SinayokoMaxime LopezMaxime LopezPablo PagisJonathan ClaussJonathan ClaussHicham BoudaouiGauthier HeinGauthier HeinMohammed AmouraJonathan ClaussSofiane DiopMohamed AbdelmonemAmine HaritAngel GomesAmine GouiriPierre-Emile HøjbjergAngel GomesTimothy WeahParis BrunnerParis BrunnerAleksandr GolovinVandersonAleksandr GolovinStanis IdumboMatthis AblineLamine CamaraDro FernándezOllie WatkinsKhvicha KvaratskheliaKhvicha KvaratskheliaVitinhaVitinhaOusmane DembéléOusmane DembéléVitinhaIllia ZabarnyiAchraf HakimiMatvey SafonovFerran TorresBoulaye DiaBoulaye DiaValentin RongierPrzemyslaw FrankowskiAboubakar NagidaValentin RongierMousa Al TamariSebastian SzymanskiPrzemyslaw FrankowskiDjaoui CisséLudovic BlasLudovic BlasMatthis AblineMatthis AblineAleksandr Golovin← own goal

By situation

Open play
14 · 82%
Set pieces
1 · 6%
Penalties
2 · 12%
Vision AI Model Insight
17 goals conceded. 82% come in open play; set pieces account for only 6%, so dead-ball defending is not the main issue. 0% 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 (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.

Premium
Tactical Phase Read
The tactical idea behind each phase of this team's play, written from the same numbers shown above — what they do in build-up, how they progress, where they create, and how they defend it.
Subscribe →

Set-Piece Threat

How much of the goal threat is manufactured from dead balls — a route to goal that bypasses their open-play mechanism entirely.

Open play
10 g · 8.0 xG
Set pieces
0 g · 1.8 xG
Penalties
0 g
0% of goals come from set pieces (0 of 10), worth 1.8 xG from 12 shots. Set pieces are a secondary threat; the danger is in open play.

Where do Strasbourg's corners go?

19 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 (13)

38%8%8%23%15%
Delivered into the box 92%Short 8%

Left side (6)

17%17%17%33%17%
Delivered into the box 100%Short 0%

Where opponents attack Strasbourg from set pieces

29 shots from opponent corners against Strasbourg, 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 (17)

24%12%47%12%6%
Shot 17Goals 0

Left flag (12)

17%25%33%25%
Shot 12Goals 1

Tactical Strengths & Weaknesses

Strengths (top-third vs league)
  • none stand out
Weaknesses (bottom-third vs league)
  • progression /game — 35.62 (0th pct)
  • press intensity (PPDA) — 7.39 (6th pct)
  • threat created /game — 9.39 (6th pct)
  • field tilt % — 42.08 (11th pct)
  • xG conceded /game — 1.69 (17th 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.

Lille
81%
Angers
70%
Lorient
58%
Lyon
54%
Paris FC
36%

Performance vs Opponent Styles

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

Results

opponent typegoals for/ggoals against/gpoints/g
vs high-pressing opponents (2m)0.503.500.00
vs mid-block opponents (2m)0.502.500.00
vs low-block opponents (4m)2.501.501.75

Behaviour — how Strasbourg actually play against each

opponent typeretentiondirectness (long balls)prog. passes/gxT/gbox entries/g
vs high-pressing opponents81%16%266.57.5
vs mid-block opponents83%11%369.46.5
vs low-block opponents86%10%4010.815.0
Vision AI Model Insight
Facing a high press, retention drops to 81% vs 86% against a low block (5pt lower under pressure), and directness (long-ball share) sits at 16% 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. Strasbourg are territorially-dominated with a aggressive high-press. Their attack creates 1.22 xG a game and they concede 1.69 a game. [Event-data based — no tracking, so off-ball structure and marking scheme need video.]
Squad

Players

Who drives the numbers.

Against Their Own Norm

For each of ten match numbers, the one match this season that departed furthest from Strasbourg’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.

Strasbourg
across 8 matches this season
Defensive actionsat Troyes · 6-2 · 2026-09-06178avg 142
Final-third entriesat Nice · 1-2 · 2026-10-1162avg 36
Expected goalsat Troyes · 6-2 · 2026-09-062.98avg 1.22
Passesvs PSG · 1-3 · 2026-10-17279avg 483
Progressive passesat Marseille · 0-4 · 2026-08-2119avg 36
Progressive carriesat Nice · 1-2 · 2026-10-1167avg 46
Threat from passingvs Lens · 2-1 · 2026-08-297.31avg 4.61
PPDAat Marseille · 0-4 · 2026-08-2111.7avg 7.4

On-Ball Value — who moved the game

Every pass, carry, take-on, shot and ball win is worth the change it made to the chance of scoring minus the chance of conceding in the next 10 seconds. This is Strasbourg’s last 8 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).

+12.8on-ball value, 8 matches
Where the value came from
Passes3862 actions+4.66
Carries1081 actions+0.53
Dribbles168 actions+0.31
Shots98 actions+4.48
Ball wins519 actions+2.78
Most on-ball value (OBV)ActionsOBVper 100
Gessime Yassine415+2.07+0.50
Sebastian Nanasi213+1.53+0.72
Oso483+1.20+0.25
Diogo Sousa303+1.02+0.34
Jeyland Mitchell630+0.88+0.14
Filip Jörgensen516+0.81+0.16
Dário Essugo174+0.78+0.45
Pape Diop583+0.76+0.13
123456
Biggest single movesOBV
1  Jeyland Mitchell · shot+0.375
2  Sebastian Nanasi · pass+0.362
3  Diogo Sousa · shot+0.262
4  Jacobo Ortega · shot+0.261
5  Sam Amo-Ameyaw · shot+0.225
6  Gessime Yassine · pass+0.222

Set Pieces by Player

Who delivers Strasbourg’s dead balls and who finishes them this season. 19 corners taken, most by Giovanni Reyna (5); 12 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.

PlayerCorners takenInto the boxSet-piece shotsGoalsxGFree-kick shotsPenalties
Pape Diop——3—0.092—
Jeyland Mitchell——3—1.07——
Giovanni Reyna55 (100%)1—0.05——
Oso43 (75%)1—0.021—
Yaya Diémé10 (0%)1—0.06——
Samir El Mourabet33 (100%)——0.00——
Diogo Sousa31 (33%)——0.00——
Dário Essugo——1—0.41——
Guéla Doué——1—0.06——
Karim Coulibaly——1—0.04——
Sebastian Nanasi21 (50%)——0.00——
Gessime Yassine10 (0%)——0.00——

Top Performers

Ranked by Vision AI Score — the same tactical behavioural evaluation each player's own report page shows (min 216 minutes). Coloured chips are that player's strongest capability blocks.

playerposminVision AI Scorecapability blocks
Pape DiopDM6096.5Ball-Winning 69%Progression 56%
Jeyland MitchellCB6495.8Ball-Winning 74%Progression 65%Ball-Carrying 54%Aerial & Box Defending 22%
OsoFB5704.3Ball-Carrying 52%Ball-Winning 38%Wide Delivery 24%
Giovanni ReynaCM5214.0Successful Dribble 81%Ball-Carrying 62%Progression 56%Chance Creation 49%
Gessime YassineW6853.8Ball-Carrying 50%Chance Creation 30%Wide Delivery 28%Box Threat 18%
Diogo SousaCM5333.0Chance Creation 48%Progression 45%Successful Dribble 16%Ball-Carrying 14%
Filip JörgensenGK7642.5Shot-Stopping 25%
Genesis AntwiFB7450.9Ball-Winning 50%Ball-Carrying 7%Wide Delivery 0%

Top Creators

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

playerposminkey P /90box entriesxT
Gessime YassineF6851.18175.7
Sebastian NanasiF3761.68145.9
OsoD5701.11117.7
Diogo SousaM5331.01125.1
Giovanni ReynaM5211.7373.7
Jacobo OrtegaF3770.9540.6
Pape DiopM6091.0316.9
Sam Amo-AmeyawF2760.6551.8

Wide Delivery Leaders

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

playerposmincrosses (completed)cutbacks (completed)
Gessime YassineW6859 (44%)5 (60%)
OsoFB5709 (22%)3 (33%)
Genesis AntwiFB7452 (0%)6 (50%)
Sebastian NanasiW3762 (100%)4 (75%)
Sam Amo-AmeyawW2763 (33%)1 (100%)

Defensive Volume Leaders

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

playerposmindef actionstacklesinterceptrecoveries
Gessime YassineF68512519642
Jeyland MitchellD649120171326
Pape DiopM60911922748
Genesis AntwiD74510416836
OsoD5709110832
Giovanni ReynaM521686326
Diogo SousaM533665522
Dário EssugoM326545613

Full Squad Data

Every player with 100+ minutes (19 players).

playerposappsmingoalsxGkey PprogdefxT
Filip JörgensenG876400.0043269.5
Genesis AntwiD874500.02171044.6
Gessime YassineF868521.29281255.7
Jeyland MitchellD764901.12321208.9
Pape DiopM860900.37211196.9
OsoD657000.4732917.7
Diogo SousaM753311.3620665.1
Giovanni ReynaM852120.71016683.7
Jacobo OrtegaF837711.141390.6
Sebastian NanasiF537600.6729315.9
Karim CoulibalyD632900.118432.4
Dário EssugoM632610.944542.2
Conrad HarderF630410.953310.5
Sam Amo-AmeyawF727620.526311.8
Ismaël DoukouréD326000.004441.8
Samir El MourabetM624600.215381.7
Lucas HøgsbergD421500.029272.8
Andrew OmobamideleD219400.101241.5
Fabio BaldéM410700.11180.5

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.

Filip Jörgensen
Goalkeeper · Goalkeeper
minutes764
goals · xG0 · 0.0
prog passes /905.1
key passes /900.00
def actions /903.1
pass accuracy80%
Genesis Antwi
Stopper / marker · Defender
minutes745
goals · xG0 · 0.0
prog passes /902.1
key passes /900.24
def actions /9012.6
pass accuracy87%
Gessime Yassine
Ball-winner / destroyer · Midfielder
minutes685
goals · xG2 · 1.2
prog passes /903.7
key passes /901.18
def actions /9016.4
pass accuracy76%
Jeyland Mitchell
Stopper / marker · Defender
minutes649
goals · xG0 · 1.1
prog passes /904.4
key passes /900.28
def actions /9016.6
pass accuracy86%
Pape Diop
Ball-winner / destroyer · Midfielder
minutes609
goals · xG0 · 0.3
prog passes /903.1
key passes /901.03
def actions /9017.6
pass accuracy91%
Oso
Ball-playing defender · Defender
minutes570
goals · xG0 · 0.4
prog passes /905.1
key passes /901.11
def actions /9014.4
pass accuracy82%
Diogo Sousa
Ball-winner / destroyer · Midfielder
minutes533
goals · xG1 · 1.3
prog passes /903.4
key passes /901.01
def actions /9011.1
pass accuracy77%
Giovanni Reyna
Ball-winner / destroyer · Midfielder
minutes521
goals · xG2 · 0.7
prog passes /902.8
key passes /901.73
def actions /9011.7
pass accuracy87%
Jacobo Ortega
Ball-winner / destroyer · Midfielder
minutes377
goals · xG1 · 1.1
prog passes /900.2
key passes /900.95
def actions /909.3
pass accuracy66%

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: progression /game (0th pct)
Sign a progressive passer / ball-carrier to break lines. The data flags this because progression is limited (35.62, 0th percentile vs the league).
Need — bottom-third: press intensity (PPDA) (6th pct)
Sign a high-energy presser to lift defensive intensity. The data flags this because the press is passive (7.39, 6th percentile vs the league).
Need — bottom-third: threat created /game (6th pct)
Sign a threat-generating creator between the lines. The data flags this because little threat is manufactured (9.39, 6th percentile vs the league).
Vision AI · building report