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

Paris FC

A balanced-territory side, aggressive high-press out of possession, with a high-volume attack. Currently #6 of 18 on 14 points (4-2-2, 13:9). Tactical profile below is built from 8 matches of ball-by-ball event data, contextualised against the league.

balanced-territoryaggressive high-presshigh-volume attack
D
W
W
L
W
L
W
D
W
W
D
W
L
W
L
14
points
#6 of 18 · 8 games
13:9
goals
+4 GD
4-2-2
record
W-D-L
1.75
points/game
#6 in the league
1.57
xG for/game
rank #6
1.00
xG against/game
rank #1
#13
press rank
of 18 · most aggressive
46%
field tilt
#12
Paris FC
League average
Expected goals for / game
1.571.41
Expected goals against / game
1.001.41
Field tilt
46%50%
Pass retention
86%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 Paris FC and how good was the season?
Read. Paris FC currently sit #6 of 18 on 14 points (4W-2D-2L, 1.75 pts/game), scoring 13 and conceding 9 for a goal difference of +4. On the ball they are a balanced-territory team (46% field tilt, #12 in the league); off it they defend with a aggressive high-press (#13 of 18 for press intensity). Across the 8 matches of event data, they create 1.57 xG/game (#6) and concede 1.00 (#1), a net of +0.57 xG/game — the underlying performance matches the actual goal difference.

Season Context — the league table

The question
Where did Paris FC actually finish, and against whom?

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

#teamPWDLGFGAGDPts
3Lille8431116+515
4Monaco8431117+415
5Lyon8422137+614
6Paris FC8422139+414
7Lens84131612+413
8Marseille84041813+512
9Lorient833276+112

Match Momentum — when they create and concede danger

The question
Across a whole game, which periods does Paris FC 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+'+1.61
Vision AI Model Insight
Paris FC tend to come alive after the break — first-half net xG +1.47, second-half +3.12. Their strongest period is 46-60' (net +1.61 xG); their most dangerous period to face is 16-30' (net +0.26 xG against them).

xG Race — the season, match by match

Expected goals piling up over 8 matches: Paris FC 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 (+4.6 xG). Hover a point for that match.

0591412.68.0Troyes (A) 0-0 · xG 1.25 to 0.48Nice (H) 3-0 · xG 2.21 to 0.94Marseille (A) 3-2 · xG 2.87 to 1.25Lyon (H) 0-0 · xG 0.45 to 0.61Strasbourg (H) 2-1 · xG 1.06 to 0.28Lorient (A) 0-1 · xG 1.11 to 0.58Rennes (H) 4-3 · xG 3.21 to 3.19Lens (A) 1-2 · xG 0.39 to 0.63M1M2M3M4M5M6M7M8
Paris FCOpponents against Paris FC

Tactical DNA

The football question
In five genes, what is Paris FC'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.

58
Build-Up
possession control from the back
25
Progression
moving the ball upfield with threat
50
Creation
manufacturing chances
31
Press
winning the ball high & hard
94
Solidity
resisting the opponent's threat
Genetic identity
Paris FC are defined most by their Solidity DNA (94/100 · elite at resisting the opponent's threat), and least by their Progression DNA (25/100). 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).

Possess52Disrupt37Finish52Press22Defend25
Possess
Deep buildup
60%
Possession
55%
Field tilt
40%
Disrupt
Central progression ~proxy
80%
Dribble tendency
0%
Progressive passes
30%
Finish
Final-third patience
30%
Shot quality
70%
Open-play xG
55%
Press
Forward press ~proxy
25%
High regains
25%
Pressing efficiency ~proxy
15%
Defend
Def-mid press ~proxy
20%
High line ~proxy
20%
PAdj defensive actions
35%
Paris FC is defined most by Possess (52% in the league) and weakest on Press (22%). 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 Paris FC actually control, relative to the rest of the league?

Field tilt = share of final-third touches between the two sides across Paris FC'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; Paris FC 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/gParis FC40%52%63%74%0.20.91.52.1FIELD TILT %POINTS / GAME
Vision AI Model Insight
Paris FC post 46% field tilt, ranking #12 of 18 in the league — below the league mid-point, spending more of the game defending their own territory than dictating it, and their 1.75 points/game sits above the league-average return — a real disconnect: their territorial control isn't showing up on the scoreboard the way it should.

Where the time goes — how long each phase lasts

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

The share of Paris FC'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: 35.2% of the time on the ball35%Progression: 45.3% of the time on the ball45%Chance creation: 17.0% of the time on the ball17%Attacking transition: 2.4% of the time on the ball45%PROGRESSION
PhaseShare of the time
Build-up35.2%
Progression45.3%
Chance creation17.0%
Attacking transition2.4%
Vision AI Model Insight
Progression is where Paris FC spends most of its time on the ball (45% 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.

Maxime Lopez ↔ Diego Coppola: 176 passes, -0.7 combined xTMaxime Lopez ↔ Pierre Lees-Melou: 172 passes, 0.5 combined xTKevin Trapp ↔ Diego Coppola: 145 passes, -0.3 combined xTMaxime Lopez ↔ Adama Camara: 144 passes, 1.1 combined xTHamari Traoré ↔ Diego Coppola: 143 passes, -0.8 combined xTMaxime Lopez ↔ Ilan Kebbal: 140 passes, 0.6 combined xTIlan Kebbal ↔ Adama Camara: 131 passes, 2.7 combined xTMaxime Lopez ↔ Samir Chergui: 99 passes, -0.4 combined xTPierre Lees-Melou ↔ Diego Coppola: 84 passes, -0.1 combined xTMoustapha Mbow ↔ Diego Coppola: 81 passes, -0.3 combined xTPierre Lees-Melou ↔ Pablo Pagis: 78 passes, 0.8 combined xTOtávio ↔ Diego Coppola: 72 passes, -0.3 combined xTHamari Traoré ↔ Luca Koleosho: 68 passes, 0.9 combined xTOtávio ↔ Moustapha Mbow: 68 passes, -0.5 combined xTDiego Coppola ↔ Samir Chergui: 66 passes, -0.2 combined xTHamari Traoré ↔ Pierre Lees-Melou: 62 passes, -0.2 combined xTSamir Chergui ↔ Adama Camara: 58 passes, -0.7 combined xTKevin Trapp ↔ Samir Chergui: 54 passes, -0.4 combined xTKevin Trapp ↔ Moustapha Mbow: 51 passes, 0.1 combined xTPierre Lees-Melou ↔ Otávio: 48 passes, 0.1 combined xTIlan Kebbal ↔ Samir Chergui: 48 passes, -0.1 combined xTPablo Pagis ↔ Luca Koleosho: 48 passes, 2.4 combined xTHamari Traoré ↔ Pablo Pagis: 40 passes, 0.4 combined xTPierre Lees-Melou ↔ Ilan Kebbal: 39 passes, 1.2 combined xTMaxime Lopez — isolate this player's connectionsLopezPierre Lees-Melou — isolate this player's connectionsLees-MelouIlan Kebbal — isolate this player's connectionsKebbalDiego Coppola — isolate this player's connectionsCoppolaLassine Sinayoko — isolate this player's connectionsSinayokoPablo Pagis — isolate this player's connectionsPagisAdama Camara — isolate this player's connectionsCamaraRudy Matondo — isolate this player's connectionsMatondoLuca Koleosho — isolate this player's connectionsKoleoshoKevin Trapp — isolate this player's connectionsTrappMoses Simon — isolate this player's connectionsSimonHamari Traoré — isolate this player's connectionsTraoréOtávio — isolate this player's connectionsOtávioSamir Chergui — isolate this player's connectionsCherguiPatrick Zabi — isolate this player's connectionsZabiMoustapha Mbow — isolate this player's connectionsMbow
width = passes between the paircolour = xT per pass: low → high

Top 10 combinations

Lopez ↔ Coppola
176p · -0.7xT
Lopez ↔ Lees-Melou
172p · 0.5xT
Trapp ↔ Coppola
145p · -0.3xT
Lopez ↔ Camara
144p · 1.1xT
Traoré ↔ Coppola
143p · -0.8xT
Lopez ↔ Kebbal
140p · 0.6xT
Kebbal ↔ Camara
131p · 2.7xT
Lopez ↔ Chergui
99p · -0.4xT
Lees-Melou ↔ Coppola
84p · -0.1xT
Mbow ↔ Coppola
81p · -0.3xT
Vision AI Model Insight
Busiest connection: Maxime Lopez ↔ Diego Coppola (176 passes). Most dangerous per pass: Pablo Pagis ↔ Luca Koleosho (0.05 xT/pass over 48 passes) — the combination that hurts opponents most when it fires, not just the one used most.

Touch Density — where Paris FC 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 Paris FC 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
4759
ACTIONS
4%7%13%6%5%7%10%14%10%8%4%3%2%3%4%PASSES
Carries
the ball moved with a player
854
ACTIONS
2%6%13%6%4%8%9%15%9%9%6%2%2%3%6%CARRIES
Vision AI Model Insight
Their single busiest zone is the centre of the middle third (14.3% of all actions). 67% of actions run through the inside lanes (centre + both half-spaces) versus 33% down the two wings — an inside-dominant, half-space build. By third: 34% defensive, 49% middle, 17% 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. Paris FC spread it evenly (no channel above 37%). The bars under the pitch put the league’s share beside theirs.

33%Left side29%Middle37%Right sideATTACKING →
Paris FC
33%29%37%
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. Paris FC made 332 this season (41.5 a match); 30% 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: 88 entries (27%)8827%LEFT WINGLeft half-space: 44 entries (13%)4413%LEFT HALF-SPACECentre: 60 entries (18%)6018%CENTRERight half-space: 42 entries (13%)4213%RIGHT HALF-SPACERight wing: 98 entries (30%)9830%RIGHT WINGATTACKING ↑
ChannelEntriesPer matchShareLeague share
Left wing8811.027%30%
Left half-space445.513%13%
Centre607.518%11%
Right half-space425.213%14%
Right wing9812.230%32%
Build-up2 sections

Build-Up — the pass map

The football question
How does Paris FC 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.

1661build-up passes attempted
86%completed
23%reach the middle third
1%go long into the final third

Pass types

724CB · 51%
31Long Wide · 2%
8Long Central · 1%
478Wide → FB · 34%
183Central · 13%

Sequence success — build-up

84build-up sequences this season
13%succeed — reach the next third or a shot
42%turned over with nothing to show
CB724 · 51%
left: 419 passes, most from Coppola419right: 305 passes, most from Coppola305
Long Wide31 · 2%
right: 17 passes, most from Trapp17left: 14 passes, most from Trapp14
Long Central8 · 1%
left: 6 passes, most from Trapp6right: 2 passes, most from Trapp2
Wide → FB478 · 34%
right: 274 passes, most from Camara274left: 204 passes, most from Trapp204
Central183 · 13%
left: 92 passes, most from Trapp92right: 91 passes, most from Lopez91
◆ Vision AI Insight
Paris FC build predominantly down the left, with Coppola as the fulcrum (317 build-up passes). Only 24% 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 (111×).

Pass Explorer

Every pass of Paris FC’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. 306 progressive passes across 8 matches (38.2/game).

Origin lane

Right wing
29%
Right half-space
14%
Central
16%
Left half-space
18%
Left wing
22%
Progression is right-dominant: 43% right side · 16% central · 41% left.

Who generates it

Ilan Kebbal
57 · 19%
Pablo Pagis
28 · 9%
Hamari Traoré
28 · 9%
Diego Coppola
26 · 8%
Maxime Lopez
26 · 8%
Top two — Ilan Kebbal & Pablo Pagis — account for 28% of all progression.
PROGRESSION
38.2 prog passes/game
mostly right
→
PENETRATION
31% reach final third
15% reach the box
→
THREAT
the other 69% is recycled
progress ≠ danger
What came of it. 31% of progressive passes reached the final third and 15% the box; the rest was recycled in front of the block. 28% of the progression ran through two players, mostly down the right side.

Which lanes actually create the threat

Share of passes played from each lane against its share of the threat (xT) they add. Efficient = more threat than volume, sterile = lots of the ball, little damage.

Per lane — volume vs threat
Right wingbalanced
Right half-spacebalanced
Centralsterile
Left half-spacebalanced
Left wingbalanced

Progression — the pass map

The football question
Once Paris FC 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.

2320progression passes attempted
91%completed
13%break into the final third
11%go backward into the defensive third

Pass types

728CB · 35%
59Long Wide · 3%
10Long Central · 0%
920Wide → FB · 44%
388Central · 18%

Sequence success — progression

104progression sequences this season
28%succeed — reach the next third or a shot
38%turned over with nothing to show
CB728 · 35%
left: 442 passes, most from Traoré442right: 286 passes, most from Coppola286
Long Wide59 · 3%
right: 44 passes, most from Lees-Melou44left: 15 passes, most from Pagis15
Long Central10 · 0%
right: 6 passes, most from Kebbal6left: 4 passes, most from Lopez4
Wide → FB920 · 44%
right: 500 passes, most from Lopez500left: 420 passes, most from Lees-Melou420
Central388 · 18%
left: 204 passes, most from Lopez204right: 184 passes, most from Lees-Melou184
◆ Vision AI Insight
Paris FC progress predominantly down the right, with Lopez the busiest midfield outlet (412 progression passes). Only 13% of passes that start in midfield actually break the final-third line — the rest (76%) is recycled back into midfield or (11%) played backward, the hallmark of a side that carries progression itself through midfield rather than just passing through it. Their most-repeated pattern is RHS mid → C mid (137×) — 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)
10%
Progressive
1%
Neutral / sideways
50%
Safe / backward
39%
11% of passes are progressive-or-better; the remaining 89% 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 Paris FC actually progress the ball with their feet, not a pass?

165 progressive carries this season across the whole squad (19% of 854 tracked carries total), grouped into up to 4 spatial patterns by where each carry starts and ends together — not just its origin zone. Median 14m, longest 45m. Most active carrier: Kebbal (25). Filter to one player below.

First (55)Second (43)Third (34)Fourth (33)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 Paris FC'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.

106shots
12.6xG
0.12xG per shot
9big chances (0.3+ xG)
13goals
#13key passes / game in the league
#13box entries / game in the league
Attacking transition: 35 shots (33%)33%35Settled attack: 53 shots (50%)50%53Set piece: 18 shots (17%)17%18106SHOTS
Kind of attack, then how the shot was set upShotsGoalsxGxG / shot
Attacking transition 33% of shots3573.780.11
Cross110.130.13
Cut-back210.220.11
Long ball100.070.07
Pass into the box / short pass2152.180.10
Own creation (carry, dribble, rebound)1001.180.12
Settled attack 50% of shots5325.470.10
Cross300.280.09
Cut-back400.340.09
Long ball600.360.06
Pass into the box / short pass2512.830.11
Own creation (carry, dribble, rebound)1511.660.11
Set piece 17% of shots1843.310.18
Corner1310.880.07
Free kick200.050.03
Penalty332.380.79

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
Ilan Kebbal111.332
Lassine Sinayoko141.233
Pablo Pagis50.800
Moses Simon30.690
Luca Koleosho50.681

Who takes them

PlayerShotsxGGoals
Lassine Sinayoko264.916
Ilan Kebbal171.501
Luca Koleosho161.371
Maxime Lopez61.092
Pablo Pagis151.001
Vision AI Model Insight
The biggest source of Paris FC's chances, by xG, is settled attack (5.5 of 12.6 xG, 53 of 106 shots), mostly pass into the box / short pass. 35 of the 106 shots (33%) came within 15 seconds of winning the ball. 18 of the 106 shots came from set pieces; 13 goals from 12.6 xG (+0.4).

Chance Creation — the pass map

The football question
How does Paris FC 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

4CB · 6%
6Long Wide · 8%
2Long Central · 3%
23Wide → FB · 32%
36Central · 51%

Sequence success — final-third

79final-third sequences this season
29%succeed — produce a shot
34%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
CB4 · 6%
left: 3 passes, most from Chergui3
Long Wide6 · 8%
left: 3 passes, most from Camara3right: 3 passes, most from Sinayoko3
Long Central2 · 3%
right: 2 passes, most from Kebbal2
Wide → FB23 · 32%
right: 14 passes, most from Camara14left: 9 passes, most from Sinayoko9
Central36 · 51%
left: 23 passes, most from Sinayoko23right: 13 passes, most from Kebbal13

Who creates — volume × threat

13Sinayoko
12Kebbal
9Pagis
8Camara
7Lopez
6Koleosho
4Lees-Melou
71key passes #13 of 18
11.5box entries/game #13 of 18
35.2final-3rd entries/game #9 of 18
1.57xG/game #6 of 18
13goals #6 of 18
+0.4finishing vs xG #10 of 18
◆ Vision AI Insight
Paris FC lean right — 41% of their key passes come from that flank and its half-space (29 of 71), most often from Lopez (6). They funnel chances into the c of the final third, most often via RHS att → C att (9 key passes, most often from Kebbal). Sinayoko is the primary source overall. They generate 1.57 xG a game and finish it clinical — they beat their xG, led by Lassine Sinayoko (6G), Maxime Lopez (2G), Vincent Marchetti (1G).

Crosses & Cutbacks — squad-wide

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

63 crosses: 16 found a team-mate (25%), 6 led to a shot (10%). 23 cut-backs: 12 found a team-mate (52%), 6 led to a shot (26%). 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 Paris FC actually cross from?

63 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. 27% come from one territory, the byline left wing; 4 come from the half-spaces and 0 from the centre.

deep right wing: 5 crosses (8%)8%5deep right half-space: 1 crosses (2%)2%1deep centre: 0 crosses (0%)deep left half-space: 0 crosses (0%)deep left wing: 2 crosses (3%)3%2advanced right wing: 13 crosses (21%)21%13advanced right half-space: 3 crosses (5%)5%3advanced centre: 0 crosses (0%)advanced left half-space: 0 crosses (0%)advanced left wing: 12 crosses (19%)19%12byline right wing: 10 crosses (16%)16%10byline right half-space: 0 crosses (0%)byline centre: 0 crosses (0%)byline left half-space: 0 crosses (0%)byline left wing: 17 crosses (27%)27%17
TerritoryCrossesShareLed to a shot
byline · left wing1727%2
advanced · right wing1321%0
advanced · left wing1219%0
byline · right wing1016%2
deep · right wing58%1
advanced · right half-space35%1
deep · left wing23%0
deep · right half-space12%0
Finishing3 sections

Shot Map

106 shots for 12.6 xG (1.57/game, #6 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.

High-volume attack. 13.2 shots/game at 0.118 xG/shot. Box entries 12/game (#13). Threat created 10.6 xT/game (#11).

Goal Sequences

The move behind each of Paris FC’s 13 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 — 13 goals traced.

How Do They Score?

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

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

By situation

Open play
9 · 69%
Set pieces
1 · 8%
Penalties
3 · 23%

By finish

Right foot
8 · 62%
Left foot
4 · 31%
Header
1 · 8%
Vision AI Model Insight
13 goals scored. 69% come in open play; set pieces add 8%. 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

55 of 209 pass attempts lost here (26%).26%55/209Camara31 of 313 pass attempts lost here (10%).10%31/313Lopez74 of 610 pass attempts lost here (12%).12%74/610Trapp22 of 299 pass attempts lost here (7%).7%22/299Coppola55 of 230 pass attempts lost here (24%).24%55/230Otávio47 of 338 pass attempts lost here (14%).14%47/338Kebbal45 of 463 pass attempts lost here (10%).10%45/463Camara43 of 670 pass attempts lost here (6%).6%43/670Lees-Melou34 of 466 pass attempts lost here (7%).7%34/466Lees-Melou46 of 383 pass attempts lost here (12%).12%46/383Pagis43 of 205 pass attempts lost here (21%).21%43/205Kebbal41 of 159 pass attempts lost here (26%).26%41/159Lopez26 of 117 pass attempts lost here (22%).22%26/117Sinayoko36 of 120 pass attempts lost here (30%).30%36/120Koleosho54 of 177 pass attempts lost here (31%).31%54/177Koleoshoattacking →

How they lose it

Incomplete pass — attacking third
200
Failed forward pass
138
Failed take-on
63
Lost in the defensive third
237
Their weakest zone is the left wing in the attacking third: 31% of 177 pass attempts there are lost (54 times). Separately, 31% 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 Paris FC 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.

35
Pressing Index
/100 · passive
Intensity39
PPDA, high-press share, high-zone actions
Height44
how high up the pitch they defend
Effectiveness17
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
20%
RHS
19%
C
25%
LHS
20%
LW
16%
By third (high / mid / low regain)
Def 50%
Mid 35%
Att 15%
Read
Ranked #13 of 18 for press intensity, this is a high press. 50% of defensive actions happen in the middle & attacking thirds, at an average height of 38. Of recoveries, 48% are won high up the pitch.

Every pressing metric, ranked in the league

metricvaluerankmeaning
PPDA (opp passes per def action)—#13/18lower = more intense; rank is what matters
Defensive actions / game158#8/18
High-press share23%#8/18def actions won high up
Defensive-action height (0–100)38.4#10/18avg pitch height of actions
High recoveries / game21.6#14/18ball won in mid+att third
High-recovery share48%#16/18share of recoveries won high
Opponent completion allowed85%#16/18lower = more disruptive
Opponent progressive allowed7.1%#3/18of 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 Paris FC win it back with a defensive action?

Counted over the 296 opposition possessions that Paris FC 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 Paris FC step in. Paris FC win it after 8.1 opponent passes in the first half-hour and after 5.2 after the hour mark (sooner).

PASSES THEY GET
6.4passes
median 5 · before Paris FC win it
TIME THEY KEEP IT
19sec
median 12s · 37.0 wins per game
How many passes the opponent got in before the ball was won
32%
24%
20%
24%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
8.3
24s
15–30′
7.9
23s
30–45+′
6.7
20s
45–60′
5.7
17s
60–75′
5.1
14s
75–90+′
5.2
16s
average passesaverage seconds

Attitude in each third

Every other style number here says how much Paris FC 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 Paris FC 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) 1,661 passes

shorter passing, kept on the floor, playing short

Width of use-0.1720.65
Wide-channel share-0.420.28
Side bias+0.241.06
Pass length-1.3917.68
Long-ball share-1.180.11
Forward gain per pass-1.007.11
Backward-pass share+0.390.22
Deep-pass share-1.060.19
Completion+0.740.86
Seconds between passes-0.952.75
Side used

left 29% · central 44% · right 27%

Progression (middle third) 2,320 passes

quicker, shorter passing, narrow, central

Width of use-1.2422.95
Wide-channel share-0.990.33
Side bias+0.210.88
Pass length-1.2915.43
Long-ball share-0.890.07
Forward gain per pass-1.012.07
Backward-pass share+0.560.36
Deep-pass share-1.030.11
Completion+0.930.90
Seconds between passes-1.582.50
Side used

left 32% · central 38% · right 30%

Final third 778 passes

narrow, central, played inside, circulating

Width of use-1.0629.01
Wide-channel share-0.950.51
Side bias-0.30-2.60
Pass length-0.4514.77
Long-ball share-0.570.07
Forward gain per pass-0.87-0.12
Backward-pass share+0.680.43
Deep-pass share-0.420.07
Completion+0.630.75
Seconds between passes-0.203.25
Side used

left 36% · central 21% · right 43%

Reference measured across roughly 3,500 match-teams in this competition, per third, so the comparison is with Paris FC'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 Paris FC 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 share14%
#18 of 18 · reaches final third in ≤3 passes
Patient build-up share33%
#6 of 18 · spells of 6+ passes
Tempo (possession length)19.3s
#12 of 18 · shorter = faster tempo
Passes per possession5.3
#4 of 18
Counter-attack shots /game1.36
#2 of 18 · shot within 12s of a regain in own half
Press→shot /game2.69
#13 of 18 · shot within 8s of a high regain
Speed to final third after regain13.6s
#14 of 18 · lower = faster break

Sequence success — attacking transition

13attacking transition sequences this season
46%succeed — produce a shot
46%turned over with nothing to show

Sequence success — defensive transition

35defensive transition sequences this season
66%succeed — reach the next third or a shot
17%turned over with nothing to show
Vision AI Model Insight
Paris FC are a patient build-up side: 14% of possessions reach the final third in 3 passes or fewer (#18 of 18), against 33% built patiently over 6+ passes (#6 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 2.69 times a game (#13 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 Paris FC? 325 opponent progressive passes and 78 shots faced (8.0 xG against) across the season.

Opponent threat by lane (attacking into Paris FC)

Right wing
36%
Right half-space
16%
Central
11%
Left half-space
12%
Left wing
25%
Where the danger came from. Opponents generated most of their threat through the right wing (36% of the danger conceded).

How Do They Concede?

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

Every goal Paris FC conceded across the loaded matches — the opponent's shots in Paris FC'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 (54 total)

4%4%26%20%13%17%17%Jonathan ClaussMelvin BardSofiane DiopGauthier HeinXavier MandzaGauthier HeinJonathan ClaussElye WahiSofiane DiopJonathan ClaussAntoine MendyGauthier HeinDjibril CoulibalyAmine HaritEmersonAmine HaritKeyliane AbdallahPierre-Emile HøjbjergAmine HaritTimothy WeahMerwan IfnaouiHugo PicardMerwan IfnaouiMerwan IfnaouiKandet DiawaraNoah DonkorHugo PicardIgor PaixãoAmine HaritIgor PaixãoTimothy WeahAdrien ThomassonBryan ReynoldsEstéban LepaulValentin RongierEstéban LepaulEstéban LepaulMousa Al TamariFlorian SotocaSaud AbdulhamidThorgan HazardFlorian ThauvinFlorian ThauvinGessime YassineOsoDiogo SousaDembo SyllaArsène KouassiMamadou KonéDembo SyllaJean-Victor MakengoPavel SulcLoïs OpendaKaïl Boudache← own goal

By situation

Open play
5 · 62%
Set pieces
0 · 0%
Penalties
3 · 38%
Vision AI Model Insight
8 goals conceded. 62% come in open play; set pieces account for only 0%, 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 wing of the attacking third (26% 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
9 g · 9.2 xG
Set pieces
1 g · 0.9 xG
Penalties
3 g
8% of goals come from set pieces (1 of 13), worth 0.9 xG from 15 shots. Set pieces are a secondary threat; the danger is in open play.

Where do Paris FC's corners go?

28 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 (17)

18%35%24%12%
Delivered into the box 88%Short 12%

Left side (11)

18%9%9%9%55%
Delivered into the box 100%Short 0%

Where opponents attack Paris FC from set pieces

12 shots from opponent corners against Paris FC, 0 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 (5)

20%20%40%20%
Shot 5Goals 0

Left flag (7)

14%14%57%14%
Shot 7Goals 0

Tactical Strengths & Weaknesses

Strengths (top-third vs league)
  • xG conceded /game — 1.00 (94th pct)
  • pass retention % — 86.30 (83th pct)
  • xG created /game — 1.57 (67th pct)
Weaknesses (bottom-third vs league)
  • progression /game — 38.25 (11th pct)
  • box entries /game — 11.50 (22th pct)
  • press intensity (PPDA) — 6.41 (28th pct)
  • field tilt % — 45.94 (33th 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.

Lyon
81%
Lorient
50%
Strasbourg
36%
Lille
26%
Lens
6%

Performance vs Opponent Styles

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

Results

opponent typegoals for/ggoals against/gpoints/g
vs high-pressing opponents (2m)2.002.001.50
vs mid-block opponents (2m)3.501.503.00
vs low-block opponents (4m)0.500.501.25

Behaviour — how Paris FC actually play against each

opponent typeretentiondirectness (long balls)prog. passes/gxT/gbox entries/g
vs high-pressing opponents81%11%307.79.0
vs mid-block opponents86%8%3610.716.5
vs low-block opponents88%7%4312.010.2
Vision AI Model Insight
Facing a high press, retention drops to 81% vs 88% against a low block (7pt lower under pressure), and directness (long-ball share) sits at 11% 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. Paris FC are balanced-territory with a aggressive high-press. Their attack creates 1.57 xG a game and they concede 1.00 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 Paris FC’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.

Paris FC
across 8 matches this season
Progressive passesat Lorient · 0-1 · 2026-10-1063avg 38
Final-third entriesat Lorient · 0-1 · 2026-10-1079avg 42
Pass accuracyat Lens · 1-2 · 2026-10-2480%avg 86%
Defensive actionsvs Lyon · 0-0 · 2026-09-12129avg 158
Field tiltat Lorient · 0-1 · 2026-10-1076%avg 46%
Expected goalsvs Rennes · 4-3 · 2026-10-183.21avg 1.57
Passesat Marseille · 3-2 · 2026-09-06439avg 595
PPDAat Lens · 1-2 · 2026-10-249.5avg 6.4

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 Paris FC’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).

+14.5on-ball value, 8 matches
Where the value came from
Passes4759 actions+4.73
Carries854 actions+0.73
Dribbles110 actions+0.33
Shots106 actions+6.57
Ball wins526 actions+2.14
Most on-ball value (OBV)ActionsOBVper 100
Pablo Pagis363+2.36+0.65
Ilan Kebbal573+2.34+0.41
Lassine Sinayoko244+2.03+0.83
Maxime Lopez831+1.74+0.21
Luca Koleosho313+1.02+0.33
Adama Camara471+0.96+0.20
Pierre Lees-Melou650+0.95+0.15
Moses Simon112+0.88+0.78
123456
Biggest single movesOBV
1  Lassine Sinayoko · shot+0.774
2  Lassine Sinayoko · shot+0.745
3  Lassine Sinayoko · shot+0.703
4  Maxime Lopez · shot+0.688
5  Pablo Pagis · pass+0.538
6  Ilan Kebbal · pass+0.506

Set Pieces by Player

Who delivers Paris FC’s dead balls and who finishes them this season. 28 corners taken, most by Pablo Pagis (15); 15 shots from corners and free kicks, 1 goals, led by Lassine Sinayoko (1 goals, 0.42 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
Lassine Sinayoko——510.42—3/3
Pablo Pagis1514 (93%)2—0.052—
Ilan Kebbal1210 (83%)2—0.07——
Luca Koleosho——2—0.08——
Rudy Matondo——1—0.07——
Otávio——1—0.12——
Samir Chergui——1—0.05——
Adama Camara——1—0.06——
Maxime Lopez11 (100%)——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
Lassine SinayokoST7247.5Finishing 84%Ball-Carrying 74%Box Threat 65%Aerial Threat 0%
Ilan KebbalW6227.2Ball-Carrying 70%Box Threat 63%Chance Creation 56%Wide Delivery 34%
Kevin TrappGK6696.6Shot-Stopping 66%
Diego CoppolaCB7675.8Progression 52%Aerial & Box Defending 48%Ball-Winning 34%Ball-Carrying 25%
Pablo PagisCM5905.4Successful Dribble 64%Chance Creation 52%Progression 50%Ball-Carrying 50%
Maxime LopezCM6885.0Progression 89%Chance Creation 60%Ball-Carrying 54%Successful Dribble 18%
Adama CamaraFB7033.3Ball-Winning 52%Wide Delivery 44%Ball-Carrying 4%
Luca KoleoshoW5212.9Box Threat 71%Ball-Carrying 39%Chance Creation 28%Wide Delivery 6%
Pierre Lees-MelouCM7482.6Successful Dribble 34%Progression 31%Ball-Carrying 24%Chance Creation 16%

Top Creators

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

playerposminkey P /90box entriesxT
Ilan KebbalM6221.742310.0
Lassine SinayokoF7241.62132.9
Pablo PagisM5901.37146.9
Adama CamaraD7031.02135.0
Maxime LopezM6880.92810.5
Luca KoleoshoM5211.0474.6
Pierre Lees-MelouM7480.4838.0
Hamari TraoréD4290.4237.2

Wide Delivery Leaders

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

playerposmincrosses (completed)cutbacks (completed)
Ilan KebbalW62210 (40%)7 (14%)
Luca KoleoshoW5215 (20%)4 (25%)
Adama CamaraFB7031 (0%)5 (80%)
Moses SimonW19906 (50%)

Defensive Volume Leaders

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

playerposmindef actionstacklesinterceptrecoveries
Diego CoppolaD7671528834
Pierre Lees-MelouM74813627941
Ilan KebbalM62212613447
Adama CamaraD70311118532
Lassine SinayokoF7249611813
Maxime LopezM68884141037
OtávioD326847715
Pablo PagisM5907711319

Full Squad Data

Every player with 100+ minutes (16 players).

playerposappsmingoalsxGkey PprogdefxT
Diego CoppolaD876700.01261529.5
Pierre Lees-MelouM874800.34231368.0
Lassine SinayokoF872464.9136962.9
Adama CamaraD870300.38251115.0
Maxime LopezM868821.17268410.5
Kevin TrappG766900.0014223.9
Ilan KebbalM862211.5125712610.0
Pablo PagisM859011.0928776.9
Luca KoleoshoM752111.4625674.6
Samir CherguiD543410.215613.9
Hamari TraoréD642900.0228617.2
OtávioD532600.1015844.3
Moustapha MbowD530800.006453.0
Rudy MatondoM824800.235471.3
Moses SimonM619900.334240.7
Patrick ZabiM612300.925230.7

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.

Diego Coppola
Stopper / marker · Defender
minutes767
goals · xG0 · 0.0
prog passes /903.1
key passes /900.12
def actions /9017.8
pass accuracy91%
Pierre Lees-Melou
Ball-winner / destroyer · Midfielder
minutes748
goals · xG0 · 0.3
prog passes /902.8
key passes /900.48
def actions /9016.4
pass accuracy89%
Lassine Sinayoko
Creative forward · Forward
minutes724
goals · xG6 · 4.9
prog passes /900.7
key passes /901.62
def actions /9011.9
pass accuracy76%
Adama Camara
Stopper / marker · Defender
minutes703
goals · xG0 · 0.3
prog passes /903.2
key passes /901.02
def actions /9014.2
pass accuracy85%
Maxime Lopez
Ball-winner / destroyer · Midfielder
minutes688
goals · xG2 · 1.1
prog passes /903.4
key passes /900.92
def actions /9011.0
pass accuracy92%
Kevin Trapp
Goalkeeper · Goalkeeper
minutes669
goals · xG0 · 0.0
prog passes /901.9
key passes /900.00
def actions /903.0
pass accuracy80%
Ilan Kebbal
Deep-lying playmaker · Midfielder
minutes622
goals · xG1 · 1.5
prog passes /908.2
key passes /901.74
def actions /9018.2
pass accuracy79%
Pablo Pagis
Ball-winner / destroyer · Midfielder
minutes590
goals · xG1 · 1.0
prog passes /904.3
key passes /901.37
def actions /9011.7
pass accuracy77%
Luca Koleosho
Ball-winner / destroyer · Midfielder
minutes521
goals · xG1 · 1.4
prog passes /904.3
key passes /901.04
def actions /9011.6
pass accuracy74%

Recruitment Intelligence — profiles to sign

The question
If you were sporting director, which player profiles would move the needle most?

Each recruitment target is derived directly from the team's weakest areas vs the league — the gap, why it shows in the data, and the profile that closes it.

Need — bottom-third: progression /game (11th pct)
Sign a progressive passer / ball-carrier to break lines. The data flags this because progression is limited (38.25, 11th percentile vs the league).
Need — bottom-third: box entries /game (22th pct)
Sign a penetrative wide player or runner to create box entries. The data flags this because few entries into the box (11.50, 22th percentile vs the league).
Need — bottom-third: press intensity (PPDA) (28th pct)
Sign a high-energy presser to lift defensive intensity. The data flags this because the press is passive (6.41, 28th percentile vs the league).
Vision AI · building report