Combining every season — this one is larger, give it a moment.
Team reportLigue 1 2026
Lille
A territorially-dominated side, aggressive high-press out of possession, with a moderate attack. Currently #3 of 18 on 15 points (4-3-1, 11:6). 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 Lille and how good was the season?
Read. Lille currently sit #3 of 18 on 15 points (4W-3D-1L, 1.88 pts/game), scoring 11 and conceding 6 for a goal difference of +5.
On the ball they are a territorially-dominated team (44% field tilt, #14 in the league); off it they defend with a aggressive high-press (#15 of 18 for press intensity).
Across the 8 matches of event data, they create 1.10 xG/game (#15) and concede 1.52 (#12), a net of -0.42 xG/game — the underlying performance diverges from the actual goal difference.
Season Context — the league table
The question
Where did Lille actually finish, and against whom?
The authoritative final table (full season), centred on Lille.
#
team
P
W
D
L
GF
GA
GD
Pts
1
PSG
8
5
2
1
16
9
+7
17
2
Rennes
8
5
1
2
18
14
+4
16
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
Match Momentum — when they create and concede danger
The question
Across a whole game, which periods does Lille 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
Lille tend to fade after a strong start — first-half net xG -0.22, second-half -3.13. Their strongest period is 16-30' (net +0.34 xG); their most dangerous period to face is 61-75' (net -1.38 xG against them).
xG Race — the season, match by match
Expected goals piling up over 8 matches: Lille 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.
LilleOpponents against Lille
Tactical DNA
The football question
In five genes, what is Lille'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.
42
Build-Up
possession control from the back
28
Progression
moving the ball upfield with threat
30
Creation
manufacturing chances
19
Press
winning the ball high & hard
33
Solidity
resisting the opponent's threat
Genetic identity
Lille are defined most by their Build-Up DNA (42/100 · average at possession control from the back), and least by their Press DNA (19/100). 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
65%
Possession
85%
Field tilt
85%
Disrupt
Central progression ~proxy
50%
Dribble tendency
30%
Progressive passes
85%
Finish
Final-third patience
90%
Shot quality
30%
Open-play xG
50%
Press
Forward press ~proxy
75%
High regains
65%
Pressing efficiency ~proxy
75%
Defend
Def-mid press ~proxy
70%
High line ~proxy
70%
PAdj defensive actions
65%
Lille is defined most by Possess (78% in the league) and weakest on Disrupt (55%). 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 Lille actually control, relative to the rest of the league?
Field tilt = share of final-third touches between the two sides across Lille'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; Lille highlighted. Hover any dot for the team.
Vision AI Model Insight
Lille post 44% field tilt, ranking #14 of 18 in the league — below the league mid-point, spending more of the game defending their own territory than dictating it, and their 1.88 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 Lille has the ball, how much of that time is spent building, progressing and creating?
The share of Lille'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
30.2%
Progression
48.3%
Chance creation
19.1%
Attacking transition
2.4%
Vision AI Model Insight
Progression is where Lille spends most of its time on the ball (48% 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
Perraud ↔ Ribeiro
128p · -0.2xT
Ngoy ↔ Ribeiro
115p · -0.2xT
Özer ↔ Ribeiro
110p · -0.1xT
Özer ↔ Ngoy
86p · -0.3xT
Ngoy ↔ Santos
75p · 0.0xT
Bentaleb ↔ Ribeiro
65p · -0.3xT
Bentaleb ↔ Ngoy
63p · -0.1xT
Bentaleb ↔ Perraud
55p · 0.5xT
Bentaleb ↔ Haraldsson
43p · 1.4xT
André ↔ Perraud
42p · 0.2xT
Vision AI Model Insight
Busiest connection: Romain Perraud ↔ Alexsandro Ribeiro (128 passes). Most dangerous per pass: Romain Perraud ↔ Dilane Bakwa (0.04 xT/pass over 27 passes) — the combination that hurts opponents most when it fires, not just the one used most.
Touch Density — where Lille 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 Lille 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
3985
ACTIONS
Carries
the ball moved with a player
992
ACTIONS
Vision AI Model Insight
Their single busiest zone is the centre of the defensive third (12.8% of all actions). 63% of actions run through the inside lanes (centre + both half-spaces) versus 37% down the two wings — an inside-dominant, half-space build. By third: 32% defensive, 49% middle, 18% attacking.
Attack Sides
Which channel they attacked through this season: the share of every action made in the opponent’s half, down the left, through the middle and down the right. Lille leaned on their left (41%). The bars under the pitch put the league’s share beside theirs.
Lille
41%22%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. Lille made 308 this season (38.5 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
91
11.4
30%
30%
Left half-space
41
5.1
13%
13%
Centre
40
5.0
13%
11%
Right half-space
44
5.5
14%
14%
Right wing
92
11.5
30%
32%
Build-up2 sections
Build-Up — the pass map
The football question
How does Lille 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.
1282build-up passes attempted
83%completed
25%reach the middle third
2%go long into the final third
Pass types
534CB · 50%
52Long Wide · 5%
10Long Central · 1%
359Wide → FB · 34%
105Central · 10%
Sequence success — build-up
68build-up sequences this season
10%succeed — reach the next third or a shot
43%turned over with nothing to show
CB534 · 50%
Long Wide52 · 5%
Long Central10 · 1%
Wide → FB359 · 34%
Central105 · 10%
◆ Vision AI Insight
Lille build predominantly down the left, with Özer as the fulcrum (202 build-up passes). Only 27% 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 (80×).
Pass Explorer
Every pass of Lille’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. 328 progressive passes across 8 matches (41.0/game).
Origin lane
Right wing
28%
Right half-space
14%
Central
18%
Left half-space
10%
Left wing
30%
Progression is right-dominant: 42% right side · 18% central · 40% left.
Who generates it
Romain Perraud
44 · 13%
Berke Özer
36 · 11%
Nabil Bentaleb
34 · 10%
Dilane Bakwa
32 · 10%
Ethan Mbappé
30 · 9%
Top two — Romain Perraud & Berke Özer — account for 24% of all progression.
PROGRESSION
41.0 prog passes/game
mostly right
→
PENETRATION
28% reach final third
20% reach the box
→
THREAT
the other 72% is recycled
progress ≠ danger
What came of it.28% of progressive passes reached the final third and 20% the box; the rest was recycled in front of the block. 24% 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 wingefficient
Right half-spacebalanced
Centralbalanced
Left half-spacesterile
Left wingbalanced
Progression — the pass map
The football question
Once Lille 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.
1949progression passes attempted
89%completed
13%break into the final third
13%go backward into the defensive third
Pass types
598CB · 35%
62Long Wide · 4%
4Long Central · 0%
816Wide → FB · 47%
247Central · 14%
Sequence success — progression
102progression sequences this season
25%succeed — reach the next third or a shot
38%turned over with nothing to show
CB598 · 35%
Long Wide62 · 4%
Long Central4 · 0%
Wide → FB816 · 47%
Central247 · 14%
◆ Vision AI Insight
Lille progress predominantly down the left, with Ngoy the busiest midfield outlet (220 progression passes). Only 13% of passes that start in midfield actually break the final-third line — the rest (74%) is recycled back into midfield or (13%) 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 (104×) — 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)
13%
Progressive
1%
Neutral / sideways
50%
Safe / backward
37%
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 Lille actually progress the ball with their feet, not a pass?
200 progressive carries this season across the whole squad (20% of 982 tracked carries total), grouped into up to 4 spatial patterns by where each carry starts and ends together — not just its origin zone. Median 13m, longest 46m. Most active carrier: Ngoy (35). Filter to one player below.
First (71)Second (53)Third (47)Fourth (29)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 Lille'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.
90shots
8.8xG
0.10xG per shot
3big chances (0.3+ xG)
11goals
#15key passes / game in the league
#7box entries / game in the league
Kind of attack, then how the shot was set up
Shots
Goals
xG
xG / shot
Attacking transition21% of shots
19
5
2.11
0.11
Long ball
1
0
0.14
0.14
Pass into the box / short pass
10
4
1.07
0.11
Own creation (carry, dribble, rebound)
8
1
0.90
0.11
Settled attack62% of shots
56
5
4.98
0.09
Cross
8
0
0.61
0.08
Cut-back
6
2
0.69
0.11
Long ball
5
2
0.30
0.06
Pass into the box / short pass
30
1
2.95
0.10
Own creation (carry, dribble, rebound)
7
0
0.43
0.06
Set piece17% of shots
15
1
1.71
0.11
Corner
11
0
0.84
0.08
Free kick
3
0
0.08
0.03
Penalty
1
1
0.79
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
Hákon Haraldsson
11
1.10
1
Ethan Mbappé
5
0.62
0
Basar Önal
2
0.59
0
Dilane Bakwa
6
0.53
1
Tiago Santos
5
0.51
0
Who takes them
Player
Shots
xG
Goals
Olivier Giroud
7
1.50
2
Ayase Ueda
9
1.42
2
Ethan Mbappé
11
1.23
2
Dilane Bakwa
7
0.66
2
Hákon Haraldsson
8
0.61
1
Vision AI Model Insight
The biggest source of Lille's chances, by xG, is settled attack (5.0 of 8.8 xG, 56 of 90 shots), mostly pass into the box / short pass. 19 of the 90 shots (21%) came within 15 seconds of winning the ball. 15 of the 90 shots came from set pieces; 11 goals from 8.8 xG (+2.2).
Chance Creation — the pass map
The football question
How does Lille consistently create dangerous chances — from which zones, and who creates them?
Key passes (the pass immediately before a shot) grouped into 5 real categories: CB, Wide→FB, Central, Long Wide, Long Central. Each gets its own panel below, so nothing competes for attention. Arrow width = volume, labelled with the exact count; hover for the top creator.
Pass types
5CB · 7%
6Long Wide · 9%
8Long Central · 12%
27Wide → FB · 40%
21Central · 31%
Sequence success — final-third
49final-third sequences this season
22%succeed — produce a shot
31%turned over with nothing to show
Danger Funnel — which service carries threat
Every key pass pooled by where it was played FROM, then drawn with its arrow width set by total xT rather than by count. A zone that produces a lot of cheap balls shrinks; one that produces a few decisive ones dominates. Where this disagrees with the volume map, the volume map is the misleading one.
0 key passes shown● low threat● mid● high threatarrow width = total xT, not count
CB5 · 7%
Long Wide6 · 9%
Long Central8 · 12%
Wide → FB27 · 40%
Central21 · 31%
Who creates — volume × threat
12Haraldsson
8Bakwa
7Bentaleb
6Mbappé
5Kjærgaard
5André
5Santos
67key passes #15 of 18
13.2box entries/game #7 of 18
30.4final-3rd entries/game #14 of 18
1.10xG/game #15 of 18
11goals #11 of 18
+2.2finishing vs xG #6 of 18
◆ Vision AI Insight
Lille lean right — 40% of their key passes come from that flank and its half-space (27 of 67), most often from Mbappé (6). They funnel chances into the c of the final third, most often via RW att → C att (6 key passes, most often from Santos). Haraldsson is the primary source overall. They generate 1.10 xG a game and finish it clinical — they beat their xG, led by Olivier Giroud (2G), Ayase Ueda (2G), Dilane Bakwa (2G).
Crosses & Cutbacks — squad-wide
The question
How does Lille deliver into the box from wide areas, and what comes of it?
77 crosses: 28 found a team-mate (36%), 6 led to a shot (8%). 20 cut-backs: 10 found a team-mate (50%), 5 led to a shot (25%). 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 Lille actually cross from?
77 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 right wing; 1 come from the half-spaces and 0 from the centre.
Territory
Crosses
Share
Led to a shot
byline · right wing
21
27%
4
byline · left wing
18
23%
1
advanced · left wing
17
22%
0
advanced · right wing
13
17%
1
deep · right wing
5
6%
0
deep · left wing
2
3%
0
byline · left half-space
1
1%
0
Finishing3 sections
Shot Map
90 shots for 8.8 xG (1.10/game, #15 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.
Moderate attack. 11.2 shots/game at 0.098 xG/shot. Box entries 13/game (#7). Threat created 10.6 xT/game (#12).
Goal Sequences
The move behind each of Lille’s 11 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 — 11 goals traced.
How Do They Score?
The question
Where do the goals actually come from — open play, dead balls, which foot?
Every goal Lille scored across the loaded matches, broken down by how it was created and finished.
By situation
Open play
10 · 91%
Set pieces
0 · 0%
Penalties
1 · 9%
By finish
Right foot
3 · 27%
Left foot
7 · 64%
Header
1 · 9%
Vision AI Model Insight
11 goals scored. 91% come in open play; set pieces add 0%. Open play is clearly their main route to goal.
When they lose it1 section
Ball-Loss Map — where attacks die
Every turnover has a location and a cause. This is a rate, not a raw count — the share of pass attempts from each zone that end in a turnover, so a zone they simply play through more often doesn't automatically look riskier. Grey cells are real zones with too few attempts to trust a rate.
Loss rate by zone
How they lose it
Incomplete pass — attacking third
181
Failed forward pass
125
Failed take-on
88
Lost in the defensive third
222
Their weakest zone is the right wing in the defensive third: 28% of 148 pass attempts there are lost (42 times). Separately, 29% of all losses happen in the attacking third — a fair share happen deeper, which invites transitions.
Out of possession
Out of possession
How they press, where they win the ball back, and how they concede.
How hard does Lille 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.
21
Pressing Index /100 · passive
Intensity15
PPDA, high-press share, high-zone actions
Height6
how high up the pitch they defend
Effectiveness39
high recoveries & high-regain share
Disruption19
opponent completion & progression allowed
Every defensive action & where it happens
Where they win the ball back — by lane
RW
19%
RHS
19%
C
23%
LHS
24%
LW
15%
By third (high / mid / low regain)
Def 52%
Mid 35%
Att 13%
Read
Ranked #15 of 18 for press intensity, this is a high press. Note the tension: 52% of their raw defensive actions still occur in their own third — a possession effect, not a passive one. Dominating the ball (20% high-press share) means that when they do defend, it is often deep. Of recoveries, 54% are won high up the pitch.
Every pressing metric, ranked in the league
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#15/18
lower = more intense; rank is what matters
Defensive actions / game
151
#11/18
High-press share
20%
#16/18
def actions won high up
Defensive-action height (0–100)
36.2
#17/18
avg pitch height of actions
High recoveries / game
21.9
#13/18
ball won in mid+att third
High-recovery share
54%
#9/18
share of recoveries won high
Opponent completion allowed
84%
#11/18
lower = more disruptive
Opponent progressive allowed
10.0%
#18/18
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 Lille win it back with a defensive action?
Counted over the 235 opposition possessions that Lille 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 Lille step in. Lille win it after 5.4 opponent passes in the first half-hour and after 4.2 after the hour mark (sooner).
PASSES THEY GET
5.1passes
median 3 · before Lille win it
TIME THEY KEEP IT
15sec
median 9s · 29.4 wins per game
How many passes the opponent got in before the ball was won
42%
25%
16%
17%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
6.2
17s
15–30′
4.7
15s
30–45+′
5.0
15s
45–60′
7.1
20s
60–75′
5.3
15s
75–90+′
3.6
9s
average passesaverage seconds
Attitude in each third
Every other style number here says how much Lille 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 Lille 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) 1,282 passes
nothing unusual in this zone
Width of use-0.7819.38
Wide-channel share-0.780.26
Side bias+0.180.82
Pass length-0.1622.14
Long-ball share-0.460.18
Forward gain per pass-0.2210.49
Backward-pass share+0.170.21
Deep-pass share-0.480.24
Completion+0.390.83
Seconds between passes-0.133.25
Side used
left 27% · central 49% · right 24%
Progression (middle third) 1,949 passes
nothing unusual in this zone
Width of use+0.0425.65
Wide-channel share-0.110.40
Side bias+0.562.29
Pass length-0.1816.97
Long-ball share-0.460.08
Forward gain per pass-0.682.57
Backward-pass share+0.520.36
Deep-pass share-0.480.14
Completion+0.550.88
Seconds between passes-0.443.00
Side used
left 38% · central 31% · right 31%
Final third 754 passes
safer, quicker
Width of use-0.3030.88
Wide-channel share-0.340.56
Side bias+0.684.33
Pass length-0.5614.59
Long-ball share-0.660.07
Forward gain per pass-0.080.94
Backward-pass share-0.420.36
Deep-pass share-0.690.06
Completion+0.940.77
Seconds between passes-0.862.75
Side used
left 46% · central 18% · right 36%
Reference measured across roughly 3,500 match-teams
in this competition, per third, so the comparison is with Lille'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 Lille 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 share18%
#7 of 18 · reaches final third in ≤3 passes
Patient build-up share33%
#5 of 18 · spells of 6+ passes
Tempo (possession length)20.5s
#14 of 18 · shorter = faster tempo
Passes per possession5.1
#6 of 18
Counter-attack shots /game0.79
#16 of 18 · shot within 12s of a regain in own half
Press→shot /game2.67
#14 of 18 · shot within 8s of a high regain
Speed to final third after regain13.5s
#12 of 18 · lower = faster break
Sequence success — attacking transition
7attacking transition sequences this season
0%succeed — produce a shot
43%turned over with nothing to show
Sequence success — defensive transition
29defensive transition sequences this season
59%succeed — reach the next third or a shot
7%turned over with nothing to show
Vision AI Model Insight
Lille are balanced between tempo and patience: 18% of possessions reach the final third in 3 passes or fewer (#7 of 18), against 33% built patiently over 6+ passes (#5 of 18). Winning the ball back leads to a shot inside 12 seconds 0.79 times a game (#16), and a high regain converts to a shot within 8 seconds 2.67 times a game (#14 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 Lille? 424 opponent progressive passes and 125 shots faced (12.1 xG against) across the season.
Opponent threat by lane (attacking into Lille)
Right wing
34%
Right half-space
16%
Central
13%
Left half-space
10%
Left wing
27%
Where the danger came from. Opponents generated most of their threat through the right wing (34% of the danger conceded).
How Do They Concede?
The question
Where do the goals against Lille actually come from — open play, dead balls, and who supplies them?
Every goal Lille conceded across the loaded matches — the opponent's shots in Lille'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 (90 total)
By situation
Open play
3 · 60%
Set pieces
2 · 40%
Penalties
0 · 0%
Vision AI Model Insight
5 goals conceded. 60% come in open play, and 40% from set pieces — a genuine defensive frailty at dead balls to fix on the training ground. 20% are headers — aerial defending is not where the damage comes from. Their supply line runs mainly through the right half-space of the attacking third (24% 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
10 g · 7.1 xG
Set pieces
0 g · 0.9 xG
Penalties
1 g
0% of goals come from set pieces (0 of 11), worth 0.9 xG from 14 shots. Set pieces are a secondary threat; the danger is in open play.
Where do Lille's corners go?
24 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 (9)
Delivered into the box 100%Short 0%
Left side (15)
Delivered into the box 93%Short 7%
Where opponents attack Lille from set pieces
22 shots from opponent corners against Lille, 2 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 (11)
Shot 11Goals 1
Left flag (11)
Shot 11Goals 1
Tactical Strengths & Weaknesses
Strengths (top-third vs league)
none stand out
Weaknesses (bottom-third vs league)
shots /game — 11.25 (11th pct)
xG created /game — 1.10 (17th pct)
press intensity (PPDA) — 6.56 (17th pct)
field tilt % — 43.85 (22th pct)
progression /game — 41.00 (22th 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.
Strasbourg
81%
Lorient
62%
Angers
48%
Lyon
39%
Nice
27%
Performance vs Opponent Styles
The question
Not just how Lille 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 Lille's own passing behaviour to the result.
Results
opponent type
goals for/g
goals against/g
points/g
vs high-pressing opponents (2m)
1.50
1.50
1.00
vs mid-block opponents (2m)
1.50
0.00
3.00
vs low-block opponents (4m)
1.25
0.75
1.75
Behaviour — how Lille actually play against each
opponent type
retention
directness (long balls)
prog. passes/g
xT/g
box entries/g
vs high-pressing opponents
79%
18%
29
7.4
5.5
vs mid-block opponents
82%
13%
37
9.7
8.5
vs low-block opponents
87%
9%
49
12.6
19.5
Vision AI Model Insight
Facing a high press, retention drops to 79% vs 87% against a low block (8pt lower under pressure), and directness (long-ball share) sits at 18% when pressed vs 9% 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. Lille are territorially-dominated with a aggressive high-press.
Their attack creates 1.10 xG a game and they concede 1.52 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 Lille’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 Lille’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).
+11.8on-ball value, 8 matches
Where the value came from
Passes3985 actions+3.77
Carries982 actions-0.06
Dribbles144 actions+0.53
Shots90 actions+4.72
Ball wins472 actions+2.82
Most on-ball value (OBV)
Actions
OBV
per 100
Ethan Mbappé
279
+1.38
+0.49
Nabil Bentaleb
502
+1.29
+0.26
Olivier Giroud
86
+1.02
+1.19
Berke Özer
336
+0.84
+0.25
Nathan Ngoy
647
+0.84
+0.13
Hákon Haraldsson
347
+0.69
+0.20
Alexsandro
599
+0.65
+0.11
Maurits Kjærgaard
132
+0.65
+0.49
Biggest single moves
OBV
1 Olivier Giroud · shot
+0.805
2 Basar Önal · pass
+0.459
3 Ethan Mbappé · shot
+0.334
4 Berke Özer · pass
+0.272
5 Ayase Ueda · shot
+0.268
6 Maurits Kjærgaard · shot
+0.242
Set Pieces by Player
Who delivers Lille’s dead balls and who finishes them this season. 24 corners taken, most by Hákon Haraldsson (9); 14 shots from corners and free kicks, 0 goals. “Into the box” counts corners that landed inside the penalty area; free-kick takers are the players who shot from one, because this feed does not mark who delivers the others.
Player
Corners taken
Into the box
Set-piece shots
Goals
xG
Free-kick shots
Penalties
Hákon Haraldsson
9
9 (100%)
1
—
0.06
—
—
Nabil Bentaleb
—
—
4
—
0.10
3
—
Ethan Mbappé
4
3 (75%)
1
—
0.03
—
—
Dilane Bakwa
6
6 (100%)
—
—
0.00
—
—
Alexsandro
—
—
2
—
0.14
—
—
Tanguy Nianzou
—
—
2
—
0.34
—
—
Basar Önal
3
1 (33%)
—
—
0.00
—
—
Tiago Santos
—
—
1
—
0.09
—
—
Ayase Ueda
—
—
1
—
0.07
—
—
Benjamin André
—
—
1
—
0.06
—
—
Olivier Giroud
—
—
—
—
0.00
—
1/1
Maxima Goffi
—
—
1
—
0.01
—
—
Calvin Verdonk
1
1 (100%)
—
—
0.00
—
—
Romain Perraud
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
Hákon Haraldsson
M
581
1.86
18
5.5
Dilane Bakwa
M
346
2.08
17
6.0
Ethan Mbappé
M
426
1.27
11
4.7
Tiago Santos
D
494
0.91
10
6.0
Nabil Bentaleb
M
631
1.00
3
10.1
Romain Perraud
D
631
0.29
6
7.8
Olivier Giroud
F
421
0.64
3
0.8
Ngal'ayel Mukau
M
515
0.17
4
4.0
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)
Tiago Santos
FB
494
6 (33%)
7 (57%)
Romain Perraud
FB
631
3 (67%)
9 (22%)
Basar Önal
W
213
1 (0%)
1 (100%)
Defensive Volume Leaders
Who does the defensive work — tackles, interceptions, recoveries.
player
pos
min
def actions
tackles
intercept
recoveries
Alexsandro Ribeiro
D
662
118
8
7
15
Nathan Ngoy
D
663
106
6
10
26
Nabil Bentaleb
M
631
103
6
6
42
Romain Perraud
D
631
93
13
6
19
Ngal'ayel Mukau
M
515
90
13
10
26
Tiago Santos
D
494
86
10
5
20
Benjamin André
M
386
85
16
4
22
Hákon Haraldsson
M
581
82
6
4
28
Full Squad Data
Every player with 100+ minutes (20 players).
player
pos
apps
min
goals
xG
key P
prog
def
xT
Nathan Ngoy
D
7
663
0
0.1
1
14
106
6.4
Berke Özer
G
7
662
0
0.0
0
36
22
8.5
Alexsandro Ribeiro
D
7
662
0
0.2
1
17
118
6.8
Nabil Bentaleb
M
8
631
0
0.3
7
34
103
10.1
Romain Perraud
D
8
631
0
0.2
2
44
93
7.8
Hákon Haraldsson
M
8
581
1
0.6
12
18
82
5.5
Ngal'ayel Mukau
M
8
515
0
0.1
1
12
90
4.0
Tiago Santos
D
6
494
2
0.5
5
30
86
6.0
Ethan Mbappé
M
7
426
2
1.2
6
30
56
4.7
Olivier Giroud
F
7
421
2
1.5
3
1
45
0.8
Benjamin André
M
6
386
0
0.1
5
7
85
2.7
Dilane Bakwa
M
6
346
2
0.7
8
32
56
6.0
Tanguy Nianzou
D
3
283
0
0.6
1
3
29
2.0
Ayase Ueda
F
4
241
2
1.4
1
2
22
0.3
Basar Önal
M
4
213
0
0.1
3
9
33
1.7
Maurits Kjærgaard
M
5
195
0
0.6
5
6
15
2.5
Maxima Goffi
D
3
194
0
0.1
2
7
42
2.8
Gaëtan Perrin
M
4
146
0
0.1
0
2
24
0.8
Calvin Verdonk
D
5
141
0
0.0
0
18
29
2.4
Osame Sahraoui
M
4
131
0
0.2
3
2
13
1.3
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.
Nathan Ngoy
Stopper / marker · Defender
minutes663
goals · xG0 · 0.1
prog passes /901.9
key passes /900.14
def actions /9014.4
pass accuracy92%
Berke Özer
Goalkeeper · Goalkeeper
minutes662
goals · xG0 · 0.0
prog passes /904.9
key passes /900.00
def actions /903.0
pass accuracy68%
Alexsandro Ribeiro
Stopper / marker · Defender
minutes662
goals · xG0 · 0.2
prog passes /902.3
key passes /900.14
def actions /9016.0
pass accuracy91%
Romain Perraud
Ball-playing defender · Defender
minutes631
goals · xG0 · 0.2
prog passes /906.3
key passes /900.29
def actions /9013.3
pass accuracy83%
Nabil Bentaleb
Ball-winner / destroyer · Midfielder
minutes631
goals · xG0 · 0.3
prog passes /904.8
key passes /901.00
def actions /9014.7
pass accuracy85%
Hákon Haraldsson
Ball-winner / destroyer · Midfielder
minutes581
goals · xG1 · 0.6
prog passes /902.8
key passes /901.86
def actions /9012.7
pass accuracy82%
Ngal'ayel Mukau
Ball-winner / destroyer · Midfielder
minutes515
goals · xG0 · 0.1
prog passes /902.1
key passes /900.17
def actions /9015.7
pass accuracy85%
Tiago Santos
Ball-playing defender · Defender
minutes494
goals · xG2 · 0.5
prog passes /905.5
key passes /900.91
def actions /9015.7
pass accuracy82%
Ethan Mbappé
Deep-lying playmaker · Midfielder
minutes426
goals · xG2 · 1.2
prog passes /906.3
key passes /901.27
def actions /9011.8
pass accuracy77%
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: shots /game (11th pct)
Sign a shot-taking forward to raise shot volume. The data flags this because shot volume is low (11.25, 11th percentile vs the league).
Need — bottom-third: xG created /game (17th pct)
Sign a higher-volume goalscorer or chance creator. The data flags this because the attack under-produces xG (1.10, 17th percentile vs the league).
Need — bottom-third: press intensity (PPDA) (17th pct)
Sign a high-energy presser to lift defensive intensity. The data flags this because the press is passive (6.56, 17th percentile vs the league).