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.
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.
#
team
P
W
D
L
GF
GA
GD
Pts
5
Lyon
8
4
2
2
13
7
+6
14
6
Paris FC
8
4
2
2
13
9
+4
14
7
Lens
8
4
1
3
16
12
+4
13
8
Marseille
8
4
0
4
18
13
+5
12
9
Lorient
8
3
3
2
7
6
+1
12
10
Nice
8
3
2
3
8
10
-2
11
11
Toulouse
8
2
4
2
12
11
+1
10
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)
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.
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).
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.
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.
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.
Phase
Share of the time
Build-up
24.7%
Progression
48.6%
Chance creation
25.0%
Attacking transition
1.7%
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.
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
Carries
the ball moved with a player
1299
ACTIONS
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.
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.
Channel
Entries
Per match
Share
League share
Left wing
144
18.0
35%
30%
Left half-space
59
7.4
14%
13%
Centre
35
4.4
8%
11%
Right half-space
54
6.8
13%
14%
Right wing
122
15.2
29%
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%
Long Wide55 · 5%
Long Central7 · 1%
Wide → FB474 · 43%
Central102 · 9%
◆ 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.
From channel
To channel
From third
To third
Outcome
Minutes
Channel to channel · click a cell
Third to third · click a cell
Progression4 sections
How They Progress — the mechanism
Not just how many progressive passes, but where they start, who plays them, and whether they actually penetrate. 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%
Long Wide61 · 3%
Long Central13 · 1%
Wide → FB1193 · 54%
Central301 · 14%
◆ 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.
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
Kind of attack, then how the shot was set up
Shots
Goals
xG
xG / shot
Attacking transition28% of shots
29
2
2.61
0.09
Cross
1
0
0.09
0.09
Cut-back
4
0
0.36
0.09
Long ball
4
0
0.26
0.07
Pass into the box / short pass
14
1
1.22
0.09
Own creation (carry, dribble, rebound)
6
1
0.68
0.11
Settled attack51% of shots
53
12
5.85
0.11
Cross
11
2
1.68
0.15
Cut-back
7
2
0.97
0.14
Long ball
1
0
0.03
0.03
Pass into the box / short pass
22
3
2.18
0.10
Own creation (carry, dribble, rebound)
12
5
0.99
0.08
Set piece20% of shots
21
4
3.37
0.16
Corner
17
2
0.96
0.06
Free kick
1
0
0.03
0.03
Penalty
3
2
2.38
0.79
Every attempt to create — and what came of it
Every pass into the box, every cut-back and every pass before a shot. Red led to a shot, gold to a goal; grey ones were tried but no shot came: solid found a team-mate, dashed lost the ball. Filter by outcome, type or player; tap an arrow for the detail.
Who sets the shots up
Player
Set-ups
xG created
Goals
Amine Gouiri
10
1.43
2
Amine Harit
12
1.05
1
Emerson
7
1.04
1
Timothy Weah
6
0.75
2
Pierre-Emile Højbjerg
6
0.51
0
Who takes them
Player
Shots
xG
Goals
Amine Gouiri
17
3.28
5
Himad Abdelli
8
1.43
3
Angel Gomes
10
1.31
1
Igor Paixão
15
1.25
2
Pierre-Emile Højbjerg
12
0.77
1
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%
Long Central5 · 7%
Wide → FB24 · 32%
Central40 · 54%
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.
Territory
Crosses
Share
Led to a shot
byline · right wing
24
26%
6
advanced · right wing
23
25%
0
byline · left wing
16
17%
2
advanced · left wing
12
13%
1
advanced · right half-space
4
4%
3
deep · right wing
3
3%
0
deep · left wing
3
3%
0
advanced · left half-space
3
3%
0
byline · right half-space
2
2%
0
byline · left half-space
2
2%
1
deep · left half-space
1
1%
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
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.
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
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#3/18
lower = more intense; rank is what matters
Defensive actions / game
146
#14/18
High-press share
25%
#4/18
def actions won high up
Defensive-action height (0–100)
39.4
#7/18
avg pitch height of actions
High recoveries / game
23.4
#10/18
ball won in mid+att third
High-recovery share
55%
#7/18
share of recoveries won high
Opponent completion allowed
83%
#7/18
lower = more disruptive
Opponent progressive allowed
8.7%
#12/18
of opp passes, lower = better
Transition timing
This source carries per-event timestamps, so genuine transition metrics — counter-attack shots, press→shot conversions, time-to-recover — are measured; see the dedicated Transitions section below.
How long they keep it — passes and seconds until the ball is won
The question
Once the opponent has the ball, how many passes and how many seconds pass before 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
Every opponent key pass against them (99 total)
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.
The tactical idea behind each phase of this team's play, written from the same numbers shown above — what they do in build-up, how they progress, where they create, and how they defend it.
How much of the goal threat is manufactured from dead balls — a route to goal that bypasses their open-play mechanism entirely.
Open play
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)
Delivered into the box 100%Short 0%
Left side (15)
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)
Shot 4Goals 0
Left flag (5)
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 type
goals for/g
goals against/g
points/g
vs high-pressing opponents (2m)
2.50
2.50
1.50
vs mid-block opponents (2m)
2.50
1.50
1.50
vs low-block opponents (4m)
2.00
1.25
1.50
Behaviour — how Marseille actually play against each
opponent type
retention
directness (long balls)
prog. passes/g
xT/g
box entries/g
vs high-pressing opponents
82%
13%
42
9.6
12.5
vs mid-block opponents
84%
10%
48
11.9
15.5
vs low-block opponents
89%
7%
41
12.4
11.8
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.]
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.
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)
Actions
OBV
per 100
Amine Gouiri
306
+2.44
+0.80
Himad Abdelli
581
+1.85
+0.32
Angel Gomes
339
+1.19
+0.35
Timothy Weah
593
+1.19
+0.20
Emerson
719
+1.15
+0.16
Igor Paixão
359
+1.10
+0.31
Pierre-Emile Højbjerg
675
+0.99
+0.15
Nayef Aguerd
596
+0.83
+0.14
Biggest single moves
OBV
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.
Player
Corners taken
Into the box
Set-piece shots
Goals
xG
Free-kick shots
Penalties
Amine Harit
14
14 (100%)
1
—
0.03
—
—
Igor Paixão
6
3 (50%)
2
—
0.05
1
—
Emerson
7
7 (100%)
1
—
0.03
—
—
Amine Gouiri
—
—
1
—
0.05
—
1/2
Angel Gomes
4
3 (75%)
1
—
0.02
—
—
Keyliane Abdallah
4
4 (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
—
—
1
1
0.10
—
—
Derek Cornelius
—
—
1
—
0.05
—
—
Ulisses Garcia
—
—
1
—
0.22
—
—
Nouhoum Kamissoko
—
—
1
1
0.14
—
—
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.
Chance creation leaders — key passes /90 and box entries.
player
pos
min
key P /90
box entries
xT
Amine Harit
M
657
2.05
20
8.0
Emerson
D
753
1.31
12
10.6
Amine Gouiri
F
705
1.28
10
2.8
Timothy Weah
D
675
0.93
12
9.9
Himad Abdelli
M
636
0.99
11
7.3
Igor Paixão
M
556
0.97
8
3.9
Pierre-Emile Højbjerg
M
561
0.96
8
8.8
Angel Gomes
M
498
0.72
7
4.8
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.
player
pos
min
crosses (completed)
cutbacks (completed)
Timothy Weah
FB
675
7 (57%)
8 (25%)
Emerson
FB
753
3 (33%)
9 (33%)
Keyliane Abdallah
W
229
4 (75%)
6 (17%)
Igor Paixão
W
556
2 (50%)
5 (40%)
Defensive Volume Leaders
Who does the defensive work — tackles, interceptions, recoveries.
player
pos
min
def actions
tackles
intercept
recoveries
CJ Egan-Riley
D
749
133
9
9
31
Nayef Aguerd
D
551
116
5
8
20
Timothy Weah
D
675
102
17
8
24
Himad Abdelli
M
636
97
10
4
46
Pierre-Emile Højbjerg
M
561
93
8
11
33
Igor Paixão
M
556
90
9
5
22
Emerson
D
753
89
8
11
25
Amine Harit
M
657
74
10
6
28
Full Squad Data
Every player with 100+ minutes (17 players).
player
pos
apps
min
goals
xG
key P
prog
def
xT
Jeffrey de Lange
G
8
772
0
0.0
0
22
13
5.9
Emerson
D
8
753
0
0.2
11
59
89
10.6
CJ Egan-Riley
D
8
749
1
0.1
0
20
133
8.7
Amine Gouiri
F
8
705
5
3.3
10
12
67
2.8
Timothy Weah
D
7
675
1
0.5
7
44
102
9.9
Amine Harit
M
8
657
2
0.5
15
35
74
8.0
Himad Abdelli
M
8
636
3
1.4
7
19
97
7.3
Pierre-Emile Højbjerg
M
6
561
1
0.8
6
22
93
8.8
Igor Paixão
M
7
556
2
1.3
6
24
90
3.9
Nayef Aguerd
D
8
551
0
0.3
0
26
116
8.9
Angel Gomes
M
8
498
1
1.3
4
17
60
4.8
Tochukwu Nnadi
M
5
258
0
0.1
1
10
46
2.5
Keyliane Abdallah
M
8
229
1
0.7
3
13
23
2.3
Geoffrey Kondogbia
D
4
209
0
0.0
0
7
44
2.8
Neal Maupay
F
7
196
0
0.4
2
1
28
0.8
Nouhoum Kamissoko
M
3
170
1
0.1
1
3
45
1.6
Kelyann Bezahaf
D
2
110
0
0.0
0
5
19
1.5
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).