Vision AI ← BACK TO SEARCHSeason2025/262026 · this seasonAll seasons
Building the full report
Combining every season — this one is larger, give it a moment.
Team reportLigue 1 2026

Marseille

A possession-dominant side, aggressive high-press out of possession, with a moderate attack. Currently #8 of 18 on 12 points (4-0-4, 18:13). Tactical profile below is built from 8 matches of ball-by-ball event data, contextualised against the league.

possession-dominantaggressive high-pressmoderate attack
L
W
L
D
L
W
W
W
L
L
L
L
W
W
W
12
points
#8 of 18 · 8 games
18:13
goals
+5 GD
4-0-4
record
W-D-L
1.50
points/game
#8 in the league
1.48
xG for/game
rank #8
1.91
xG against/game
rank #18
#3
press rank
of 18 · most aggressive
58%
field tilt
#2
Marseille
League average
Expected goals for / game
1.481.41
Expected goals against / game
1.911.41
Field tilt
58%50%
Pass retention
87%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 Marseille and how good was the season?
Read. Marseille currently sit #8 of 18 on 12 points (4W-0D-4L, 1.50 pts/game), scoring 18 and conceding 13 for a goal difference of +5. On the ball they are a possession-dominant team (58% field tilt, #2 in the league); off it they defend with a aggressive high-press (#3 of 18 for press intensity). Across the 8 matches of event data, they create 1.48 xG/game (#8) and concede 1.91 (#18), a net of -0.43 xG/game — the underlying performance diverges from the actual goal difference.

Season Context — the league table

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

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

#teamPWDLGFGAGDPts
5Lyon8422137+614
6Paris FC8422139+414
7Lens84131612+413
8Marseille84041813+512
9Lorient833276+112
10Nice8323810-211
11Toulouse82421211+110

Match Momentum — when they create and concede danger

The question
Across a whole game, which periods does Marseille 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+'+0.26-3.01
Vision AI Model Insight
Marseille tend to fade after a strong start — first-half net xG -0.28, second-half -3.15. Their strongest period is 16-30' (net +0.26 xG); their most dangerous period to face is 76-90+' (net -3.01 xG against them).

xG Race — the season, match by match

Expected goals piling up over 8 matches: Marseille 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 (-3.4 xG). Hover a point for that match.

05111611.815.3Strasbourg (H) 4-0 · xG 1.93 to 0.26Monaco (A) 0-2 · xG 1.39 to 1.03Paris FC (H) 2-3 · xG 1.25 to 2.87Rennes (A) 0-1 · xG 1.07 to 1.74PSG (H) 1-2 · xG 1.55 to 4.36Troyes (A) 2-0 · xG 1.17 to 0.92Angers (A) 5-2 · xG 2.51 to 1.71Le Havre (H) 4-3 · xG 0.97 to 2.35M1M2M3M4M5M6M7M8
MarseilleOpponents against Marseille

Tactical DNA

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

89
Build-Up
possession control from the back
56
Progression
moving the ball upfield with threat
50
Creation
manufacturing chances
86
Press
winning the ball high & hard
0
Solidity
resisting the opponent's threat
Genetic identity
Marseille are defined most by their Build-Up DNA (89/100 · elite at possession control from the back), and least by their Solidity DNA (0/100). 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).

Possess88Disrupt42Finish65Press72Defend70
Possess
Deep buildup
85%
Possession
90%
Field tilt
90%
Disrupt
Central progression ~proxy
90%
Dribble tendency
10%
Progressive passes
25%
Finish
Final-third patience
80%
Shot quality
35%
Open-play xG
80%
Press
Forward press ~proxy
70%
High regains
55%
Pressing efficiency ~proxy
90%
Defend
Def-mid press ~proxy
75%
High line ~proxy
85%
PAdj defensive actions
50%
Marseille is defined most by Possess (88% in the league) and weakest on Disrupt (42%). 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 Marseille actually control, relative to the rest of the league?

Field tilt = share of final-third touches between the two sides across Marseille'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; Marseille 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/gMarseille40%52%63%74%0.20.91.52.1FIELD TILT %POINTS / GAME
Vision AI Model Insight
Marseille post 58% field tilt, ranking #2 of 18 in the league — a genuinely territory-dominant side, and their 1.50 points/game sits above the league-average return — territory is genuinely converting into results.

Where the time goes — how long each phase lasts

The question
When Marseille has the ball, how much of that time is spent building, progressing and creating?

The share of Marseille'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: 24.7% of the time on the ball25%Progression: 48.6% of the time on the ball49%Chance creation: 25.0% of the time on the ball25%Attacking transition: 1.7% of the time on the ball49%PROGRESSION
PhaseShare of the time
Build-up24.7%
Progression48.6%
Chance creation25.0%
Attacking transition1.7%
Vision AI Model Insight
Progression is where Marseille spends most of its time on the ball (49% 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.

Emerson ↔ Nayef Aguerd: 142 passes, -0.5 combined xTJeffrey de Lange ↔ CJ Egan-Riley: 124 passes, -0.9 combined xTTimothy Weah ↔ CJ Egan-Riley: 119 passes, -1.3 combined xTNayef Aguerd ↔ CJ Egan-Riley: 112 passes, -0.6 combined xTPierre-Emile Højbjerg ↔ Emerson: 100 passes, 0.1 combined xTEmerson ↔ Igor Paixão: 90 passes, 1.6 combined xTNayef Aguerd ↔ Jeffrey de Lange: 86 passes, -0.0 combined xTAmine Harit ↔ Timothy Weah: 85 passes, 1.8 combined xTPierre-Emile Højbjerg ↔ Timothy Weah: 70 passes, 0.1 combined xTPierre-Emile Højbjerg ↔ Himad Abdelli: 70 passes, 0.5 combined xTPierre-Emile Højbjerg ↔ CJ Egan-Riley: 69 passes, 0.2 combined xTEmerson ↔ Himad Abdelli: 69 passes, 0.3 combined xTPierre-Emile Højbjerg ↔ Nayef Aguerd: 68 passes, 0.3 combined xTCJ Egan-Riley ↔ Himad Abdelli: 62 passes, -0.4 combined xTGeoffrey Kondogbia ↔ CJ Egan-Riley: 58 passes, -0.2 combined xTGeoffrey Kondogbia ↔ Jeffrey de Lange: 56 passes, -0.5 combined xTEmerson ↔ Amine Gouiri: 56 passes, 0.7 combined xTAmine Harit ↔ CJ Egan-Riley: 50 passes, 0.7 combined xTTimothy Weah ↔ Himad Abdelli: 50 passes, 0.8 combined xTPierre-Emile Højbjerg ↔ Angel Gomes: 49 passes, 0.4 combined xTPierre-Emile Højbjerg ↔ Amine Harit: 48 passes, 1.0 combined xTNayef Aguerd ↔ Himad Abdelli: 46 passes, -0.4 combined xTAmine Harit ↔ Himad Abdelli: 45 passes, 0.7 combined xTEmerson ↔ Tochukwu Nnadi: 44 passes, -0.1 combined xTEmerson — isolate this player's connectionsEmersonAmine Harit — isolate this player's connectionsHaritAmine Gouiri — isolate this player's connectionsGouiriNayef Aguerd — isolate this player's connectionsAguerdJeffrey de Lange — isolate this player's connectionsLangeHimad Abdelli — isolate this player's connectionsAbdelliKeyliane Abdallah — isolate this player's connectionsAbdallahCJ Egan-Riley — isolate this player's connectionsEgan-RileyIgor Paixão — isolate this player's connectionsPaixãoTimothy Weah — isolate this player's connectionsWeahAngel Gomes — isolate this player's connectionsGomesNeal Maupay — isolate this player's connectionsMaupayPierre-Emile Højbjerg — isolate this player's connectionsHøjbjergTochukwu Nnadi — isolate this player's connectionsNnadiGeoffrey Kondogbia — isolate this player's connectionsKondogbiaUlisses Garcia — isolate this player's connectionsGarcia
width = passes between the paircolour = xT per pass: low → high

Top 10 combinations

Emerson ↔ Aguerd
142p · -0.5xT
Lange ↔ Egan-Riley
124p · -0.9xT
Weah ↔ Egan-Riley
119p · -1.3xT
Aguerd ↔ Egan-Riley
112p · -0.6xT
Højbjerg ↔ Emerson
100p · 0.1xT
Emerson ↔ Paixão
90p · 1.6xT
Aguerd ↔ Lange
86p · -0.0xT
Harit ↔ Weah
85p · 1.8xT
Højbjerg ↔ Weah
70p · 0.1xT
Højbjerg ↔ Abdelli
70p · 0.5xT
Vision AI Model Insight
Busiest connection: Emerson ↔ Nayef Aguerd (142 passes). Most dangerous per pass: Pierre-Emile Højbjerg ↔ Amine Harit (0.02 xT/pass over 48 passes) — the combination that hurts opponents most when it fires, not just the one used most.

Touch Density — where Marseille 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 Marseille 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
4893
ACTIONS
3%5%9%5%4%7%10%10%12%10%6%4%2%4%7%PASSES
Carries
the ball moved with a player
1299
ACTIONS
2%6%7%7%3%9%10%11%10%10%7%3%2%3%7%CARRIES
Vision AI Model Insight
Their single busiest zone is the left half-space of the middle third (11.9% 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: 26% defensive, 50% middle, 24% 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. Marseille leaned on their left (45%). The bars under the pitch put the league’s share beside theirs.

45%Left side20%Middle35%Right sideATTACKING →
Marseille
45%20%35%
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. Marseille made 414 this season (51.8 a match); 35% came into the left wing. Longer, bolder arrows mean more entries. Entries by carrying the ball in count as well as completed passes; dribbles are not counted.

Left wing: 144 entries (35%)14435%LEFT WINGLeft half-space: 59 entries (14%)5914%LEFT HALF-SPACECentre: 35 entries (8%)358%CENTRERight half-space: 54 entries (13%)5413%RIGHT HALF-SPACERight wing: 122 entries (29%)12229%RIGHT WINGATTACKING ↑
ChannelEntriesPer matchShareLeague share
Left wing14418.035%30%
Left half-space597.414%13%
Centre354.48%11%
Right half-space546.813%14%
Right wing12215.229%32%
Build-up2 sections

Build-Up — the pass map

The football question
How does Marseille 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.

1270build-up passes attempted
87%completed
25%reach the middle third
1%go long into the final third

Pass types

467CB · 42%
55Long Wide · 5%
7Long Central · 1%
474Wide → FB · 43%
102Central · 9%

Sequence success — build-up

96build-up sequences this season
15%succeed — reach the next third or a shot
33%turned over with nothing to show
CB467 · 42%
left: 246 passes, most from Aguerd246right: 221 passes, most from Egan-Riley221
Long Wide55 · 5%
left: 34 passes, most from Lange34right: 21 passes, most from Lange21
Long Central7 · 1%
right: 4 passes, most from Lange4left: 3 passes, most from Lange3
Wide → FB474 · 43%
left: 272 passes, most from Lange272right: 202 passes, most from Lange202
Central102 · 9%
right: 52 passes, most from Lange52left: 50 passes, most from Lange50
◆ Vision AI Insight
Marseille build predominantly down the left, with Lange as the fulcrum (254 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 → LHS def (103×).

Pass Explorer

Every pass of Marseille’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. 343 progressive passes across 8 matches (42.9/game).

Origin lane

Right wing
31%
Right half-space
10%
Central
8%
Left half-space
13%
Left wing
38%
Progression is left-dominant: 41% right side · 8% central · 51% left.

Who generates it

Emerson
59 · 17%
Timothy Weah
44 · 13%
Amine Harit
35 · 10%
Nayef Aguerd
26 · 8%
Igor Paixão
24 · 7%
Top two — Emerson & Timothy Weah — account for 30% of all progression.
PROGRESSION
42.9 prog passes/game
mostly left
→
PENETRATION
20% reach final third
19% reach the box
→
THREAT
the other 80% is recycled
progress ≠ danger
What came of it. 20% of progressive passes reached the final third and 19% the box; the rest was recycled in front of the block. 30% 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 wingefficient
Right half-spacebalanced
Centralsterile
Left half-spacebalanced
Left wingbalanced

Progression — the pass map

The football question
Once Marseille 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.

2452progression passes attempted
90%completed
14%break into the final third
10%go backward into the defensive third

Pass types

634CB · 29%
61Long Wide · 3%
13Long Central · 1%
1193Wide → FB · 54%
301Central · 14%

Sequence success — progression

187progression sequences this season
30%succeed — reach the next third or a shot
40%turned over with nothing to show
CB634 · 29%
left: 358 passes, most from Aguerd358right: 276 passes, most from Egan-Riley276
Long Wide61 · 3%
right: 32 passes, most from Weah32left: 29 passes, most from Emerson29
Long Central13 · 1%
left: 7 passes, most from Harit7right: 6 passes, most from Weah6
Wide → FB1193 · 54%
left: 634 passes, most from Emerson634right: 559 passes, most from Weah559
Central301 · 14%
right: 156 passes, most from Abdelli156left: 145 passes, most from Højbjerg145
◆ Vision AI Insight
Marseille progress predominantly down the left, with Egan-Riley the busiest midfield outlet (289 progression passes). Only 14% of passes that start in midfield actually break the final-third line — the rest (77%) 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 (139×) — 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
48%
Safe / backward
40%
13% of passes are progressive-or-better; the remaining 87% 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 Marseille actually progress the ball with their feet, not a pass?

240 progressive carries this season across the whole squad (18% of 1299 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 40m. Most active carrier: Weah (27). Filter to one player below.

First (65)Second (62)Third (58)Fourth (55)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 Marseille'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.

103shots
11.8xG
0.11xG per shot
6big chances (0.3+ xG)
18goals
#7key passes / game in the league
#10box entries / game in the league
Attacking transition: 29 shots (28%)28%29Settled attack: 53 shots (51%)51%53Set piece: 21 shots (20%)20%21103SHOTS
Kind of attack, then how the shot was set upShotsGoalsxGxG / shot
Attacking transition 28% of shots2922.610.09
Cross100.090.09
Cut-back400.360.09
Long ball400.260.07
Pass into the box / short pass1411.220.09
Own creation (carry, dribble, rebound)610.680.11
Settled attack 51% of shots53125.850.11
Cross1121.680.15
Cut-back720.970.14
Long ball100.030.03
Pass into the box / short pass2232.180.10
Own creation (carry, dribble, rebound)1250.990.08
Set piece 20% of shots2143.370.16
Corner1720.960.06
Free kick100.030.03
Penalty322.380.79

Every attempt to create — and what came of it

Every pass into the box, every cut-back and every pass before a shot. Red led to a shot, gold to a goal; grey ones were tried but no shot came: solid found a team-mate, dashed lost the ball. Filter by outcome, type or player; tap an arrow for the detail.

Who sets the shots up

PlayerSet-upsxG createdGoals
Amine Gouiri101.432
Amine Harit121.051
Emerson71.041
Timothy Weah60.752
Pierre-Emile Højbjerg60.510

Who takes them

PlayerShotsxGGoals
Amine Gouiri173.285
Himad Abdelli81.433
Angel Gomes101.311
Igor Paixão151.252
Pierre-Emile Højbjerg120.771
Vision AI Model Insight
The biggest source of Marseille's chances, by xG, is settled attack (5.9 of 11.8 xG, 53 of 103 shots), mostly pass into the box / short pass. 29 of the 103 shots (28%) came within 15 seconds of winning the ball. 21 of the 103 shots came from set pieces; 18 goals from 11.8 xG (+6.2).

Chance Creation — the pass map

The football question
How does Marseille 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

5Long Wide · 7%
5Long Central · 7%
24Wide → FB · 32%
40Central · 54%

Sequence success — final-third

142final-third sequences this season
27%succeed — produce a shot
35%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
Long Wide5 · 7%
right: 3 passes, most from Harit3left: 2 passes, most from Emerson2
Long Central5 · 7%
right: 3 passes, most from Emerson3left: 2 passes, most from Emerson2
Wide → FB24 · 32%
left: 14 passes, most from Paixão14right: 10 passes, most from Højbjerg10
Central40 · 54%
left: 24 passes, most from Harit24right: 16 passes, most from Gouiri16

Who creates — volume × threat

15Harit
11Emerson
10Gouiri
7Weah
7Abdelli
6Højbjerg
6Paixão
74key passes #7 of 18
12.9box entries/game #10 of 18
39.4final-3rd entries/game #3 of 18
1.48xG/game #8 of 18
18goals #1 of 18
+6.2finishing vs xG #1 of 18
◆ Vision AI Insight
Marseille lean left — 43% of their key passes come from that flank and its half-space (32 of 74), most often from Emerson (10). They funnel chances into the c of the final third, most often via LHS att → C att (14 key passes, most often from Abdelli). Harit is the primary source overall. They generate 1.48 xG a game and finish it clinical — they beat their xG, led by Amine Gouiri (5G), Himad Abdelli (3G), Amine Harit (2G).

Crosses & Cutbacks — squad-wide

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

93 crosses: 38 found a team-mate (41%), 13 led to a shot (14%). 41 cut-backs: 18 found a team-mate (44%), 12 led to a shot (29%). 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 Marseille actually cross from?

93 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. 26% come from one territory, the byline right wing; 12 come from the half-spaces and 0 from the centre.

deep right wing: 3 crosses (3%)3%3deep right half-space: 0 crosses (0%)deep centre: 0 crosses (0%)deep left half-space: 1 crosses (1%)1%1deep left wing: 3 crosses (3%)3%3advanced right wing: 23 crosses (25%)25%23advanced right half-space: 4 crosses (4%)4%4advanced centre: 0 crosses (0%)advanced left half-space: 3 crosses (3%)3%3advanced left wing: 12 crosses (13%)13%12byline right wing: 24 crosses (26%)26%24byline right half-space: 2 crosses (2%)2%2byline centre: 0 crosses (0%)byline left half-space: 2 crosses (2%)2%2byline left wing: 16 crosses (17%)17%16
TerritoryCrossesShareLed to a shot
byline · right wing2426%6
advanced · right wing2325%0
byline · left wing1617%2
advanced · left wing1213%1
advanced · right half-space44%3
deep · right wing33%0
deep · left wing33%0
advanced · left half-space33%0
byline · right half-space22%0
byline · left half-space22%1
deep · left half-space11%0
Finishing3 sections

Shot Map

103 shots for 11.8 xG (1.48/game, #8 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.

Moderate attack. 12.9 shots/game at 0.115 xG/shot. Box entries 13/game (#10). Threat created 11.6 xT/game (#5).

Goal Sequences

The move behind each of Marseille’s 18 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 — 18 goals traced.

How Do They Score?

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

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

By situation

Open play
14 · 78%
Set pieces
2 · 11%
Penalties
2 · 11%

By finish

Right foot
13 · 72%
Left foot
2 · 11%
Header
3 · 17%
Vision AI Model Insight
18 goals scored. 78% come in open play; set pieces add 11%. 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

24 of 132 pass attempts lost here (18%).18%24/132Weah26 of 233 pass attempts lost here (11%).11%26/233Lange54 of 456 pass attempts lost here (12%).12%54/456Lange18 of 254 pass attempts lost here (7%).7%18/254Aguerd43 of 195 pass attempts lost here (22%).22%43/195Emerson39 of 349 pass attempts lost here (11%).11%39/349Weah35 of 489 pass attempts lost here (7%).7%35/489Egan-Riley49 of 508 pass attempts lost here (10%).10%49/508Højbjerg55 of 604 pass attempts lost here (9%).9%55/604Højbjerg72 of 502 pass attempts lost here (14%).14%72/502Emerson56 of 307 pass attempts lost here (18%).18%56/307Harit53 of 197 pass attempts lost here (27%).27%53/197Harit20 of 103 pass attempts lost here (19%).19%20/103Gouiri51 of 211 pass attempts lost here (24%).24%51/211Emerson58 of 353 pass attempts lost here (16%).16%58/353Emersonattacking →

How they lose it

Incomplete pass — attacking third
238
Failed forward pass
136
Failed take-on
83
Lost in the defensive third
165
Their weakest zone is the right half-space in the attacking third: 27% of 197 pass attempts there are lost (53 times). Separately, 36% 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 Marseille 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.

57
Pressing Index
/100 · average
Intensity65
PPDA, high-press share, high-zone actions
Height61
how high up the pitch they defend
Effectiveness53
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
18%
RHS
19%
C
20%
LHS
23%
LW
20%
By third (high / mid / low regain)
Def 46%
Mid 37%
Att 16%
Read
Ranked #3 of 18 for press intensity, this is a high press. 54% of defensive actions happen in the middle & attacking thirds, at an average height of 39. Of recoveries, 55% are won high up the pitch.

Every pressing metric, ranked in the league

metricvaluerankmeaning
PPDA (opp passes per def action)—#3/18lower = more intense; rank is what matters
Defensive actions / game146#14/18
High-press share25%#4/18def actions won high up
Defensive-action height (0–100)39.4#7/18avg pitch height of actions
High recoveries / game23.4#10/18ball won in mid+att third
High-recovery share55%#7/18share of recoveries won high
Opponent completion allowed83%#7/18lower = more disruptive
Opponent progressive allowed8.7%#12/18of 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 Marseille win it back with a defensive action?

Counted over the 283 opposition possessions that Marseille 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 Marseille step in. Marseille win it after 5.6 opponent passes in the first half-hour and after 4.3 after the hour mark (sooner).

PASSES THEY GET
5.1passes
median 3 · before Marseille win it
TIME THEY KEEP IT
15sec
median 7s · 35.4 wins per game
How many passes the opponent got in before the ball was won
41%
27%
19%
14%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
6.2
19s
15–30′
4.8
14s
30–45+′
5.3
16s
45–60′
5.2
16s
60–75′
3.7
8s
75–90+′
4.7
13s
average passesaverage seconds

Attitude in each third

Every other style number here says how much Marseille 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 Marseille 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) 1,270 passes

safer

Width of use+0.0021.01
Wide-channel share-0.440.28
Side bias+0.662.63
Pass length-0.7320.05
Long-ball share-0.840.15
Forward gain per pass-0.808.00
Backward-pass share+0.520.22
Deep-pass share-0.820.21
Completion+0.880.87
Seconds between passes-0.333.12
Side used

left 32% · central 43% · right 25%

Progression (middle third) 2,452 passes

left-sided, playing short

Width of use-0.2625.02
Wide-channel share-0.400.37
Side bias+1.084.34
Pass length-0.3816.69
Long-ball share-0.690.07
Forward gain per pass-0.592.71
Backward-pass share+0.180.34
Deep-pass share-0.880.12
Completion+0.770.89
Seconds between passes-0.443.00
Side used

left 42% · central 29% · right 28%

Final third 1,171 passes

safer, circulating, kept on the floor

Width of use-0.0331.54
Wide-channel share+0.060.59
Side bias+0.382.24
Pass length-0.7314.34
Long-ball share-0.860.06
Forward gain per pass-0.90-0.16
Backward-pass share+0.810.44
Deep-pass share-0.580.07
Completion+1.120.79
Seconds between passes-0.033.38
Side used

left 46% · central 13% · right 41%

Reference measured across roughly 3,500 match-teams in this competition, per third, so the comparison is with Marseille'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 Marseille 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 share17%
#9 of 18 · reaches final third in ≤3 passes
Patient build-up share36%
#3 of 18 · spells of 6+ passes
Tempo (possession length)22.7s
#17 of 18 · shorter = faster tempo
Passes per possession6.1
#2 of 18
Counter-attack shots /game1.36
#2 of 18 · shot within 12s of a regain in own half
Press→shot /game3.43
#6 of 18 · shot within 8s of a high regain
Speed to final third after regain13.5s
#13 of 18 · lower = faster break

Sequence success — attacking transition

18attacking transition sequences this season
28%succeed — produce a shot
33%turned over with nothing to show

Sequence success — defensive transition

41defensive transition sequences this season
76%succeed — reach the next third or a shot
2%turned over with nothing to show
Vision AI Model Insight
Marseille are a patient build-up side: 17% of possessions reach the final third in 3 passes or fewer (#9 of 18), against 36% built patiently over 6+ passes (#3 of 18). Winning the ball back leads to a shot inside 12 seconds 1.36 times a game (#2), and a high regain converts to a shot within 8 seconds 3.43 times a game (#6 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 Marseille? 321 opponent progressive passes and 132 shots faced (15.3 xG against) across the season.

Opponent threat by lane (attacking into Marseille)

Right wing
33%
Right half-space
12%
Central
13%
Left half-space
11%
Left wing
31%
Where the danger came from. Opponents generated most of their threat through the right wing (33% of the danger conceded).

How Do They Concede?

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

Every goal Marseille conceded across the loaded matches — the opponent's shots in Marseille'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 (99 total)

1%2%3%5%4%3%2%16%12%17%19%15%Eric DierMamadou CoulibalyMamadou CoulibalyFlávio NazinhoAleksandr GolovinStanis IdumboVandersonLamine CamaraMamadou CoulibalyLuca KoleoshoPablo PagisPierre Lees-MelouPablo PagisIlan KebbalLassine SinayokoIlan KebbalRudy MatondoMaxime LopezPatrick ZabiOusmane DembéléKhvicha KvaratskheliaKhvicha KvaratskheliaKhvicha KvaratskheliaDésiré DouéOusmane DembéléVitinhaOusmane DembéléMarquinhosVitinhaDésiré DouéDésiré DouéOusmane DembéléNuno MendesOusmane DembéléNuno MendesFerran TorresNuno MendesDésiré DouéDésiré DouéNuno MendesNuno MendesVitinhaNuno MendesNuno MendesAntoine MilleJérémy Le DouaronLucas MaronnierIron GomisLucas MaronnierSamir CherguiPablo PagisLuca KoleoshoPablo PagisIlan KebbalDjibril OuziadAyumu SekoMbwana SamattaEnzo KoffiRassoul NdiayeMbwana SamattaSota NakamuraDjibril OuziadAyumu SekoLamine N'DiayeRayan FofanaKaito MizutaKaito MizutaSimon EbonogDaren MosengoAntoine MilleLucas MaronnierRenaud RipartHugo PicardLucas MaronnierPaolo GozziSamir El MourabetLucas HøgsbergSebastian NanasiLucas HøgsbergAdrien ThomassonEstéban LepaulAdrien ThomassonValentin RongierLudovic BlasAdrien ThomassonMahdi CamaraAmine SbaïJim AllevinahAmine SbaïJim AllevinahJim AllevinahJoseph KaluluAmine SbaïAboubakar NagidaMahdi CamaraValentin RongierMousa Al TamariAdrien ThomassonBoulaye Dia← own goal

By situation

Open play
9 · 69%
Set pieces
1 · 8%
Penalties
3 · 23%
Vision AI Model Insight
13 goals conceded. 69% come in open play; set pieces account for only 8%, so dead-ball defending is not the main issue. 46% are headers — a real weakness in the air. Their supply line runs mainly through the left half-space of the attacking third (19% of key passes faced) — that is the zone to shut off.
Context

Shape and tactics

Set pieces, strengths and weaknesses, and who they resemble.

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

Set-Piece Threat

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

Open play
14 g · 8.5 xG
Set pieces
2 g · 1.0 xG
Penalties
2 g
11% of goals come from set pieces (2 of 18), worth 1.0 xG from 18 shots. Set pieces are a secondary threat; the danger is in open play.

Where do Marseille's corners go?

35 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 (20)

5%20%20%20%25%10%
Delivered into the box 100%Short 0%

Left side (15)

33%33%13%
Delivered into the box 80%Short 20%

Where opponents attack Marseille from set pieces

9 shots from opponent corners against Marseille, 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 (4)

50%50%
Shot 4Goals 0

Left flag (5)

40%20%40%
Shot 5Goals 1

Tactical Strengths & Weaknesses

Strengths (top-third vs league)
  • field tilt % — 58.34 (89th pct)
  • pass retention % — 86.65 (89th pct)
  • press intensity (PPDA) — 4.81 (83th pct)
  • threat created /game — 11.56 (72th pct)
Weaknesses (bottom-third vs league)
  • xG conceded /game — 1.91 (0th 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.

Lens
39%
PSG
39%
Toulouse
39%
Rennes
37%
Monaco
15%

Performance vs Opponent Styles

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

Results

opponent typegoals for/ggoals against/gpoints/g
vs high-pressing opponents (2m)2.502.501.50
vs mid-block opponents (2m)2.501.501.50
vs low-block opponents (4m)2.001.251.50

Behaviour — how Marseille actually play against each

opponent typeretentiondirectness (long balls)prog. passes/gxT/gbox entries/g
vs high-pressing opponents82%13%429.612.5
vs mid-block opponents84%10%4811.915.5
vs low-block opponents89%7%4112.411.8
Vision AI Model Insight
Facing a high press, retention drops to 82% vs 89% against a low block (7pt lower under pressure), and directness (long-ball share) sits at 13% when pressed vs 7% 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. Marseille are possession-dominant with a aggressive high-press. Their attack creates 1.48 xG a game and they concede 1.91 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 Marseille’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.

Marseille
across 8 matches this season
PPDAvs PSG · 1-2 · 2026-09-207.2avg 4.8
Expected goalsat Angers · 5-2 · 2026-10-182.51avg 1.48
Field tiltvs PSG · 1-2 · 2026-09-2031%avg 58%
Threat from passingvs PSG · 1-2 · 2026-09-202.78avg 4.73
Passesvs PSG · 1-2 · 2026-09-20433avg 612
Final-third entriesvs PSG · 1-2 · 2026-09-2031avg 52
Pass accuracyvs PSG · 1-2 · 2026-09-2079%avg 86%
Progressive passesat Troyes · 2-0 · 2026-10-1133avg 43

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 Marseille’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).

+14.2on-ball value, 8 matches
Where the value came from
Passes4893 actions+4.30
Carries1299 actions+0.28
Dribbles141 actions+0.04
Shots103 actions+6.68
Ball wins484 actions+2.88
Most on-ball value (OBV)ActionsOBVper 100
Amine Gouiri306+2.44+0.80
Himad Abdelli581+1.85+0.32
Angel Gomes339+1.19+0.35
Timothy Weah593+1.19+0.20
Emerson719+1.15+0.16
Igor Paixão359+1.10+0.31
Pierre-Emile Højbjerg675+0.99+0.15
Nayef Aguerd596+0.83+0.14
123456
Biggest single movesOBV
1  Amine Gouiri · shot+0.841
2  Himad Abdelli · shot+0.802
3  Amine Gouiri · shot+0.786
4  Angel Gomes · pass+0.531
5  Amine Gouiri · pass+0.424
6  Igor Paixão · shot+0.394

Set Pieces by Player

Who delivers Marseille’s dead balls and who finishes them this season. 35 corners taken, most by Amine Harit (14); 18 shots from corners and free kicks, 2 goals, led by Nouhoum Kamissoko (1 goals, 0.14 xG). “Into the box” counts corners that landed inside the penalty area; free-kick takers are the players who shot from one, because this feed does not mark who delivers the others.

PlayerCorners takenInto the boxSet-piece shotsGoalsxGFree-kick shotsPenalties
Amine Harit1414 (100%)1—0.03——
Igor Paixão63 (50%)2—0.051—
Emerson77 (100%)1—0.03——
Amine Gouiri——1—0.05—1/2
Angel Gomes43 (75%)1—0.02——
Keyliane Abdallah44 (100%)1—0.03——
Nayef Aguerd——2—0.08——
Pierre-Emile Højbjerg——2—0.08——
Timothy Weah——2—0.04——
Himad Abdelli——1—0.08—1/1
CJ Egan-Riley——110.10——
Derek Cornelius——1—0.05——
Ulisses Garcia——1—0.22——
Nouhoum Kamissoko——110.14——

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
Nayef AguerdCB5517.5Ball-Carrying 93%Progression 67%Ball-Winning 62%Aerial & Box Defending 30%
EmersonFB7537.2Ball-Carrying 84%Ball-Winning 43%Wide Delivery 38%
Pierre-Emile HøjbjergCM5616.7Ball-Carrying 78%Progression 74%Successful Dribble 67%Chance Creation 48%
Himad AbdelliCM6365.9Ball-Carrying 76%Successful Dribble 69%Progression 51%Chance Creation 39%
Amine GouiriST7055.5Finishing 74%Ball-Carrying 60%Box Threat 47%Aerial Threat 0%
Amine HaritAM6575.4Ball-Carrying 73%Chance Creation 30%Box Threat 3%
Timothy WeahFB6754.5Ball-Carrying 52%Wide Delivery 49%Ball-Winning 34%
Igor PaixãoW5564.3Box Threat 41%Ball-Carrying 16%Chance Creation 14%Wide Delivery 11%
Angel GomesCM4984.1Successful Dribble 63%Progression 51%Ball-Carrying 45%Chance Creation 42%
Jeffrey de LangeGK7724.0Shot-Stopping 40%

Top Creators

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

playerposminkey P /90box entriesxT
Amine HaritM6572.05208.0
EmersonD7531.311210.6
Amine GouiriF7051.28102.8
Timothy WeahD6750.93129.9
Himad AbdelliM6360.99117.3
Igor PaixãoM5560.9783.9
Pierre-Emile HøjbjergM5610.9688.8
Angel GomesM4980.7274.8

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)
Timothy WeahFB6757 (57%)8 (25%)
EmersonFB7533 (33%)9 (33%)
Keyliane AbdallahW2294 (75%)6 (17%)
Igor PaixãoW5562 (50%)5 (40%)

Defensive Volume Leaders

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

playerposmindef actionstacklesinterceptrecoveries
CJ Egan-RileyD7491339931
Nayef AguerdD5511165820
Timothy WeahD67510217824
Himad AbdelliM6369710446
Pierre-Emile HøjbjergM5619381133
Igor PaixãoM556909522
EmersonD7538981125
Amine HaritM6577410628

Full Squad Data

Every player with 100+ minutes (17 players).

playerposappsmingoalsxGkey PprogdefxT
Jeffrey de LangeG877200.0022135.9
EmersonD875300.211598910.6
CJ Egan-RileyD874910.10201338.7
Amine GouiriF870553.31012672.8
Timothy WeahD767510.57441029.9
Amine HaritM865720.51535748.0
Himad AbdelliM863631.4719977.3
Pierre-Emile HøjbjergM656110.8622938.8
Igor PaixãoM755621.3624903.9
Nayef AguerdD855100.30261168.9
Angel GomesM849811.3417604.8
Tochukwu NnadiM525800.1110462.5
Keyliane AbdallahM822910.7313232.3
Geoffrey KondogbiaD420900.007442.8
Neal MaupayF719600.421280.8
Nouhoum KamissokoM317010.113451.6
Kelyann BezahafD211000.005191.5

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.

Jeffrey de Lange
Goalkeeper · Goalkeeper
minutes772
goals · xG0 · 0.0
prog passes /902.6
key passes /900.00
def actions /901.5
pass accuracy82%
Emerson
Ball-playing defender · Defender
minutes753
goals · xG0 · 0.2
prog passes /907.1
key passes /901.31
def actions /9010.6
pass accuracy82%
CJ Egan-Riley
Stopper / marker · Defender
minutes749
goals · xG1 · 0.1
prog passes /902.4
key passes /900.00
def actions /9016.0
pass accuracy94%
Amine Gouiri
Poacher / finisher · Forward
minutes705
goals · xG5 · 3.3
prog passes /901.5
key passes /901.28
def actions /908.6
pass accuracy84%
Timothy Weah
Ball-playing defender · Defender
minutes675
goals · xG1 · 0.5
prog passes /905.9
key passes /900.93
def actions /9013.6
pass accuracy86%
Amine Harit
Advanced creator · Attacking mid
minutes657
goals · xG2 · 0.5
prog passes /904.8
key passes /902.05
def actions /9010.1
pass accuracy80%
Himad Abdelli
Ball-winner / destroyer · Midfielder
minutes636
goals · xG3 · 1.4
prog passes /902.7
key passes /900.99
def actions /9013.7
pass accuracy91%
Pierre-Emile Højbjerg
Ball-winner / destroyer · Midfielder
minutes561
goals · xG1 · 0.8
prog passes /903.5
key passes /900.96
def actions /9014.9
pass accuracy89%
Igor Paixão
Ball-winner / destroyer · Midfielder
minutes556
goals · xG2 · 1.3
prog passes /903.9
key passes /900.97
def actions /9014.6
pass accuracy79%

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 (0th pct)
Sign a defensive organiser / shot-suppressing centre-back. The data flags this because too much xG is conceded (1.91, 0th percentile vs the league).
Need — below-average: progression /game (39th pct)
Sign a progressive passer / ball-carrier to break lines. The data flags this because progression is limited (42.88, 39th percentile vs the league).
Need — below-average: box entries /game (44th pct)
Sign a penetrative wide player or runner to create box entries. The data flags this because few entries into the box (12.88, 44th percentile vs the league).
Vision AI · building report