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.
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.
#
team
P
W
D
L
GF
GA
GD
Pts
3
Lille
8
4
3
1
11
6
+5
15
4
Monaco
8
4
3
1
11
7
+4
15
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
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)
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.
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).
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.
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.
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.
Phase
Share of the time
Build-up
35.2%
Progression
45.3%
Chance creation
17.0%
Attacking transition
2.4%
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.
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
Carries
the ball moved with a player
854
ACTIONS
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.
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.
Channel
Entries
Per match
Share
League share
Left wing
88
11.0
27%
30%
Left half-space
44
5.5
13%
13%
Centre
60
7.5
18%
11%
Right half-space
42
5.2
13%
14%
Right wing
98
12.2
30%
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%
Long Wide31 · 2%
Long Central8 · 1%
Wide → FB478 · 34%
Central183 · 13%
◆ 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.
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. 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%
Long Wide59 · 3%
Long Central10 · 0%
Wide → FB920 · 44%
Central388 · 18%
◆ 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.
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
Kind of attack, then how the shot was set up
Shots
Goals
xG
xG / shot
Attacking transition33% of shots
35
7
3.78
0.11
Cross
1
1
0.13
0.13
Cut-back
2
1
0.22
0.11
Long ball
1
0
0.07
0.07
Pass into the box / short pass
21
5
2.18
0.10
Own creation (carry, dribble, rebound)
10
0
1.18
0.12
Settled attack50% of shots
53
2
5.47
0.10
Cross
3
0
0.28
0.09
Cut-back
4
0
0.34
0.09
Long ball
6
0
0.36
0.06
Pass into the box / short pass
25
1
2.83
0.11
Own creation (carry, dribble, rebound)
15
1
1.66
0.11
Set piece17% of shots
18
4
3.31
0.18
Corner
13
1
0.88
0.07
Free kick
2
0
0.05
0.03
Penalty
3
3
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
Ilan Kebbal
11
1.33
2
Lassine Sinayoko
14
1.23
3
Pablo Pagis
5
0.80
0
Moses Simon
3
0.69
0
Luca Koleosho
5
0.68
1
Who takes them
Player
Shots
xG
Goals
Lassine Sinayoko
26
4.91
6
Ilan Kebbal
17
1.50
1
Luca Koleosho
16
1.37
1
Maxime Lopez
6
1.09
2
Pablo Pagis
15
1.00
1
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%
Long Wide6 · 8%
Long Central2 · 3%
Wide → FB23 · 32%
Central36 · 51%
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.
Territory
Crosses
Share
Led to a shot
byline · left wing
17
27%
2
advanced · right wing
13
21%
0
advanced · left wing
12
19%
0
byline · right wing
10
16%
2
deep · right wing
5
8%
1
advanced · right half-space
3
5%
1
deep · left wing
2
3%
0
deep · right half-space
1
2%
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
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.
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
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#13/18
lower = more intense; rank is what matters
Defensive actions / game
158
#8/18
High-press share
23%
#8/18
def actions won high up
Defensive-action height (0–100)
38.4
#10/18
avg pitch height of actions
High recoveries / game
21.6
#14/18
ball won in mid+att third
High-recovery share
48%
#16/18
share of recoveries won high
Opponent completion allowed
85%
#16/18
lower = more disruptive
Opponent progressive allowed
7.1%
#3/18
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 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
Every opponent key pass against them (54 total)
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.
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
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)
Delivered into the box 88%Short 12%
Left side (11)
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)
Shot 5Goals 0
Left flag (7)
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 type
goals for/g
goals against/g
points/g
vs high-pressing opponents (2m)
2.00
2.00
1.50
vs mid-block opponents (2m)
3.50
1.50
3.00
vs low-block opponents (4m)
0.50
0.50
1.25
Behaviour — how Paris FC actually play against each
opponent type
retention
directness (long balls)
prog. passes/g
xT/g
box entries/g
vs high-pressing opponents
81%
11%
30
7.7
9.0
vs mid-block opponents
86%
8%
36
10.7
16.5
vs low-block opponents
88%
7%
43
12.0
10.2
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.]
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.
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)
Actions
OBV
per 100
Pablo Pagis
363
+2.36
+0.65
Ilan Kebbal
573
+2.34
+0.41
Lassine Sinayoko
244
+2.03
+0.83
Maxime Lopez
831
+1.74
+0.21
Luca Koleosho
313
+1.02
+0.33
Adama Camara
471
+0.96
+0.20
Pierre Lees-Melou
650
+0.95
+0.15
Moses Simon
112
+0.88
+0.78
Biggest single moves
OBV
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.
Player
Corners taken
Into the box
Set-piece shots
Goals
xG
Free-kick shots
Penalties
Lassine Sinayoko
—
—
5
1
0.42
—
3/3
Pablo Pagis
15
14 (93%)
2
—
0.05
2
—
Ilan Kebbal
12
10 (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 Lopez
1
1 (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.
Chance creation leaders — key passes /90 and box entries.
player
pos
min
key P /90
box entries
xT
Ilan Kebbal
M
622
1.74
23
10.0
Lassine Sinayoko
F
724
1.62
13
2.9
Pablo Pagis
M
590
1.37
14
6.9
Adama Camara
D
703
1.02
13
5.0
Maxime Lopez
M
688
0.92
8
10.5
Luca Koleosho
M
521
1.04
7
4.6
Pierre Lees-Melou
M
748
0.48
3
8.0
Hamari Traoré
D
429
0.42
3
7.2
Wide Delivery Leaders
Crosses and cutbacks — wingers and full-backs only (by real average position, not the raw D/M position code), ranked by combined volume.
player
pos
min
crosses (completed)
cutbacks (completed)
Ilan Kebbal
W
622
10 (40%)
7 (14%)
Luca Koleosho
W
521
5 (20%)
4 (25%)
Adama Camara
FB
703
1 (0%)
5 (80%)
Moses Simon
W
199
0
6 (50%)
Defensive Volume Leaders
Who does the defensive work — tackles, interceptions, recoveries.
player
pos
min
def actions
tackles
intercept
recoveries
Diego Coppola
D
767
152
8
8
34
Pierre Lees-Melou
M
748
136
27
9
41
Ilan Kebbal
M
622
126
13
4
47
Adama Camara
D
703
111
18
5
32
Lassine Sinayoko
F
724
96
11
8
13
Maxime Lopez
M
688
84
14
10
37
Otávio
D
326
84
7
7
15
Pablo Pagis
M
590
77
11
3
19
Full Squad Data
Every player with 100+ minutes (16 players).
player
pos
apps
min
goals
xG
key P
prog
def
xT
Diego Coppola
D
8
767
0
0.0
1
26
152
9.5
Pierre Lees-Melou
M
8
748
0
0.3
4
23
136
8.0
Lassine Sinayoko
F
8
724
6
4.9
13
6
96
2.9
Adama Camara
D
8
703
0
0.3
8
25
111
5.0
Maxime Lopez
M
8
688
2
1.1
7
26
84
10.5
Kevin Trapp
G
7
669
0
0.0
0
14
22
3.9
Ilan Kebbal
M
8
622
1
1.5
12
57
126
10.0
Pablo Pagis
M
8
590
1
1.0
9
28
77
6.9
Luca Koleosho
M
7
521
1
1.4
6
25
67
4.6
Samir Chergui
D
5
434
1
0.2
1
5
61
3.9
Hamari Traoré
D
6
429
0
0.0
2
28
61
7.2
Otávio
D
5
326
0
0.1
0
15
84
4.3
Moustapha Mbow
D
5
308
0
0.0
0
6
45
3.0
Rudy Matondo
M
8
248
0
0.2
3
5
47
1.3
Moses Simon
M
6
199
0
0.3
3
4
24
0.7
Patrick Zabi
M
6
123
0
0.9
2
5
23
0.7
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).