Combining every season — this one is larger, give it a moment.
Team reportLigue 1 2025/26
Lille
A possession-dominant side, aggressive high-press out of possession, with a moderate attack. Finished #3 of 18 on 61 points (18-7-9, 52:37). Tactical profile below is built from 34 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 finished #3 of 18 on 61 points (18W-7D-9L, 1.79 pts/game), scoring 52 and conceding 37 for a goal difference of +15.
On the ball they are a possession-dominant team (62% field tilt, #2 in the league); off it they defend with a aggressive high-press (#3 of 18 for press intensity).
Across the 34 matches of event data, they create 1.40 xG/game (#7) and concede 1.00 (#2), a net of +0.41 xG/game — the underlying performance matches 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
34
24
4
6
74
29
+45
76
2
Lens
34
22
4
8
66
35
+31
70
3
Lille
34
18
7
9
52
37
+15
61
4
Lyon
34
18
6
10
53
40
+13
60
5
Marseille
34
18
5
11
63
45
+18
59
6
Rennes
34
17
8
9
59
50
+9
59
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 +7.84, second-half +5.95. Their strongest period is 76-90+' (net +7.29 xG); their most dangerous period to face is 61-75' (net -3.04 xG against them).
xG Race — the season, match by match
Expected goals piling up over 34 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 (+13.8 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.
81
Build-Up
possession control from the back
86
Progression
moving the ball upfield with threat
72
Creation
manufacturing chances
86
Press
winning the ball high & hard
89
Solidity
resisting the opponent's threat
Genetic identity
Lille are defined most by their Solidity DNA (89/100 · elite at resisting the opponent's threat), and least by their Creation DNA (72/100). 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 62% field tilt, ranking #2 of 18 in the league — a genuinely territory-dominant side, and their 1.79 points/game sits above the league-average return — territory is genuinely converting into results.
Where the time goes — how long each phase lasts
The question
When 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
25.3%
Progression
46.7%
Chance creation
25.7%
Attacking transition
2.3%
Vision AI Model Insight
Progression is where Lille spends most of its time on the ball (47% 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
Mandi ↔ Ngoy
508p · 8.3xT
Özer ↔ Ngoy
456p · 13.9xT
Mandi ↔ Perraud
440p · 21.5xT
Mandi ↔ Özer
421p · 16.9xT
Meunier ↔ Ngoy
350p · 13.4xT
André ↔ Perraud
244p · 12.4xT
Mandi ↔ Bentaleb
240p · 7.4xT
André ↔ Mandi
223p · 11.7xT
Perraud ↔ Correia
222p · 16.7xT
André ↔ Haraldsson
222p · 9.0xT
Vision AI Model Insight
Busiest connection: Aïssa Mandi ↔ Nathan Ngoy (508 passes). Most dangerous per pass: Romain Perraud ↔ Félix Correia (0.08 xT/pass over 222 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 306 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
18296
ACTIONS
Carries
the ball moved with a player
4631
ACTIONS
Vision AI Model Insight
Their single busiest zone is the centre of the middle third (10.7% of all actions). 60% of actions run through the inside lanes (centre + both half-spaces) versus 40% down the two wings — an inside-dominant, half-space build. By third: 27% defensive, 49% middle, 25% 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 (43%). The bars under the pitch put the league’s share beside theirs.
Lille
43%21%36%
League
39%22%39%
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 1710 this season (50.3 a match); 34% came into the left wing. Longer, bolder arrows mean more entries. Entries by carrying the ball in count as well as completed passes; dribbles are not counted.
Channel
Entries
Per match
Share
League share
Left wing
586
17.2
34%
30%
Left half-space
242
7.1
14%
15%
Centre
172
5.1
10%
11%
Right half-space
223
6.6
13%
14%
Right wing
487
14.3
28%
30%
Build-up1 section
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.
4838build-up passes attempted
85%completed
28%reach the middle third
1%go long into the final third
Pass types
1748CB · 42%
201Long Wide · 5%
48Long Central · 1%
1678Wide → FB · 41%
458Central · 11%
Sequence success — build-up
882build-up sequences this season
14%succeed — reach the next third or a shot
37%turned over with nothing to show
CB1748 · 42%
Long Wide201 · 5%
Long Central48 · 1%
Wide → FB1678 · 41%
Central458 · 11%
◆ Vision AI Insight
Lille build predominantly down the right, with Özer as the fulcrum (942 build-up passes). Only 29% 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 (340×).
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. 1745 progressive passes across 34 matches (51.3/game).
Origin lane
Right wing
32%
Right half-space
12%
Central
12%
Left half-space
13%
Left wing
31%
Progression is left-dominant: 43% right side · 12% central · 45% left.
Who generates it
Romain Perraud
193 · 11%
Thomas Meunier
182 · 10%
Félix Correia
180 · 10%
Aïssa Mandi
180 · 10%
Hákon Haraldsson
104 · 6%
Top two — Romain Perraud & Thomas Meunier — account for 21% of all progression.
PROGRESSION
51.3 prog passes/game
mostly left
→
PENETRATION
24% reach final third
21% reach the box
→
THREAT
the other 76% is recycled
progress ≠ danger
What came of it.24% of progressive passes reached the final third and 21% the box; the rest was recycled in front of the block. 21% of the progression ran through two players, mostly down the left side.
Which lanes actually create the threat
Share of passes played from each lane against its share of the threat (xT) they add. Efficient = more threat than volume, sterile = lots of the ball, little damage.
Per lane — volume vs threat
Right wingefficient
Right half-spacebalanced
Centralsterile
Left half-spacebalanced
Left wingbalanced
Progression — the pass map
The football question
Once 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.
8811progression passes attempted
88%completed
17%break into the final third
12%go backward into the defensive third
Pass types
2471CB · 32%
253Long Wide · 3%
66Long Central · 1%
3949Wide → FB · 51%
972Central · 13%
Sequence success — progression
1656progression sequences this season
29%succeed — reach the next third or a shot
38%turned over with nothing to show
CB2471 · 32%
Long Wide253 · 3%
Long Central66 · 1%
Wide → FB3949 · 51%
Central972 · 13%
◆ Vision AI Insight
Lille progress predominantly down the left, with Mandi the busiest midfield outlet (1004 progression passes). Only 17% of passes that start in midfield actually break the final-third line — the rest (71%) is recycled back into midfield or (12%) 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 (410×) — 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)
15%
Progressive
1%
Neutral / sideways
47%
Safe / backward
37%
16% of passes are progressive-or-better; the remaining 84% 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?
1165 progressive carries this season across the whole squad (25% of 4594 tracked carries total), grouped into up to 4 spatial patterns by where each carry starts and ends together — not just its origin zone. Median 15m, longest 61m. Most active carrier: Mandi (172). Filter to one player below.
First (377)Second (312)Third (261)Fourth (215)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.
462shots
47.7xG
0.10xG per shot
18big chances (0.3+ xG)
50goals
#5key passes / game in the league
#3box entries / game in the league
Kind of attack, then how the shot was set up
Shots
Goals
xG
xG / shot
Attacking transition22% of shots
100
13
10.19
0.10
Cross
7
0
0.88
0.13
Cut-back
10
2
1.24
0.12
Long ball
4
1
0.22
0.06
Pass into the box / short pass
52
6
5.28
0.10
Own creation (carry, dribble, rebound)
27
4
2.57
0.10
Settled attack55% of shots
255
25
23.91
0.09
Cross
46
4
5.10
0.11
Cut-back
34
5
4.29
0.13
Long ball
17
2
1.67
0.10
Pass into the box / short pass
109
9
9.24
0.08
Own creation (carry, dribble, rebound)
49
5
3.60
0.07
Set piece23% of shots
107
12
13.58
0.13
Corner
93
4
7.06
0.08
Free kick
6
1
0.17
0.03
Penalty
8
7
6.35
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
Matias Fernandez-Pardo
33
4.43
5
Félix Correia
30
3.15
5
Hákon Haraldsson
33
2.88
1
Romain Perraud
24
2.66
3
Thomas Meunier
20
2.20
1
Who takes them
Player
Shots
xG
Goals
Matias Fernandez-Pardo
68
8.76
8
Olivier Giroud
41
7.60
7
Hákon Haraldsson
50
5.89
8
Félix Correia
37
3.85
4
Benjamin André
27
2.40
2
Vision AI Model Insight
The biggest source of Lille's chances, by xG, is settled attack (23.9 of 47.7 xG, 255 of 462 shots), mostly pass into the box / short pass. 100 of the 462 shots (22%) came within 15 seconds of winning the ball. 107 of the 462 shots came from set pieces; 50 goals from 47.7 xG (+2.3).
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
14CB · 4%
26Long Wide · 8%
35Long Central · 10%
107Wide → FB · 31%
158Central · 46%
Sequence success — final-third
1340final-third sequences this season
20%succeed — produce a shot
39%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
CB14 · 4%
Long Wide26 · 8%
Long Central35 · 10%
Wide → FB107 · 31%
Central158 · 46%
Who creates — volume × threat
49Haraldsson
38Correia
36Perraud
35Fernandez-Pardo
23André
18Meunier
18Bouaddi
340key passes #5 of 18
16.3box entries/game #3 of 18
40.0final-3rd entries/game #3 of 18
1.40xG/game #7 of 18
50goals #8 of 18
+2.3finishing vs xG #9 of 18
◆ Vision AI Insight
Lille lean left — 40% of their key passes come from that flank and its half-space (135 of 340), most often from Perraud (29). They funnel chances into the c of the final third, most often via RW att → C att (42 key passes, most often from Haraldsson). Haraldsson is the primary source overall. They generate 1.40 xG a game and finish it clinical — they beat their xG, led by Hákon Haraldsson (8G), Matias Fernandez-Pardo (8G), Olivier Giroud (7G).
Crosses & Cutbacks — squad-wide
The question
How does Lille deliver into the box from wide areas, and what comes of it?
514 crosses: 185 found a team-mate (36%), 57 led to a shot (11%). 154 cut-backs: 79 found a team-mate (51%), 47 led to a shot (31%). 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?
514 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. 24% come from one territory, the byline left wing; 29 come from the half-spaces and 0 from the centre.
Territory
Crosses
Share
Led to a shot
byline · left wing
122
24%
17
byline · right wing
119
23%
15
advanced · right wing
99
19%
8
advanced · left wing
97
19%
11
deep · right wing
26
5%
0
deep · left wing
22
4%
0
byline · left half-space
8
2%
1
advanced · right half-space
7
1%
3
advanced · left half-space
7
1%
1
byline · right half-space
7
1%
1
Finishing3 sections
Shot Map
462 shots for 47.7 xG (1.40/game, #7 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.
Moderate attack. 13.6 shots/game at 0.103 xG/shot. Box entries 16/game (#3). Threat created 12.3 xT/game (#2).
Goal Sequences
The move behind each of Lille’s 52 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 — 52 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
38 · 76%
Set pieces
5 · 10%
Penalties
7 · 14%
By finish
Right foot
25 · 50%
Left foot
20 · 40%
Header
5 · 10%
Vision AI Model Insight
50 goals scored. 76% come in open play; set pieces add 10%. 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
1237
Failed forward pass
796
Failed take-on
342
Lost in the defensive third
705
Their weakest zone is the right half-space in the attacking third: 33% of 668 pass attempts there are lost (219 times). Separately, 41% 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.
78
Pressing Index /100 · strong
Intensity80
PPDA, high-press share, high-zone actions
Height89
how high up the pitch they defend
Effectiveness75
high recoveries & high-regain share
Disruption67
opponent completion & progression allowed
Every defensive action & where it happens
Where they win the ball back — by lane
RW
18%
RHS
21%
C
21%
LHS
20%
LW
20%
By third (high / mid / low regain)
Def 45%
Mid 39%
Att 16%
Read
Ranked #3 of 18 for press intensity, this is a high press. 55% of defensive actions happen in the middle & attacking thirds, at an average height of 40. Of recoveries, 58% are won high up the pitch.
Every pressing metric, ranked in the league
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#3/18
lower = more intense; rank is what matters
Defensive actions / game
124
#13/18
High-press share
24%
#3/18
def actions won high up
Defensive-action height (0–100)
39.8
#2/18
avg pitch height of actions
High recoveries / game
24.9
#4/18
ball won in mid+att third
High-recovery share
58%
#5/18
share of recoveries won high
Opponent completion allowed
80%
#3/18
lower = more disruptive
Opponent progressive allowed
8.3%
#9/18
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 1149 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 4.0 after the hour mark (sooner).
PASSES THEY GET
4.5passes
median 3 · before Lille win it
TIME THEY KEEP IT
13sec
median 6s · 33.8 wins per game
How many passes the opponent got in before the ball was won
45%
30%
12%
13%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
5.5
15s
15–30′
5.2
15s
30–45+′
4.8
13s
45–60′
3.9
11s
60–75′
4.4
14s
75–90+′
3.7
11s
average passesaverage seconds
Phases of play
Every moment of the match read twice — what Lille were doing with the
ball, and what the opposition were doing about it at that exact moment. Built from the
real action sequence in time order, so one possession is split across the phases it
genuinely passed through rather than labelled once by where it began.
6,599phase segments analysed
10.0smedian time before the opposition engage
2.0their passes per defensive action when pressed
#3of 18 for share of defending spent pressing high
High press 10.8%Mid block 15.9%Low block 43.4%Unopposed 29.9%
What opponents do against Lille
The mirror image, and the part no other section can show: whether teams come
out to press Lille or sit in and invite them on. A side that is always faced with a low
block is playing a different sport from one that is pressed every week.
High press 5.1%Mid block 10.6%Low block 55.0%Unopposed 29.2%
Which phases actually produce
Phase
Segments
Avg length
Avg actions
Ends in shot
Turned over
Build-up
1,837
6.0s
3.4
0.3%
42.1%
Progression
2,787
7.3s
4.2
0.8%
40.3%
Chance creation
1,839
6.1s
3.5
23.9%
53.0%
Attacking transition
136
7.4s
4.6
1.5%
28.7%
Most common routes through the phases
How a possession actually travelled, in order. The chain is the tactical signature — a move that climbs all three thirds is a different thing from one that never leaves the first.
Route
Times
Ends in shot
build_up
592
0.2%
progression
592
0.3%
progression → chance_creation
369
22.5%
chance_creation
361
34.9%
build_up → progression
277
0.7%
build_up → progression → chance_creation
203
25.6%
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) 4,838 passes
nothing unusual in this zone
Width of use-0.5519.87
Wide-channel share-0.570.27
Side bias-0.14-0.35
Pass length-0.4721.00
Long-ball share-0.580.17
Forward gain per pass-0.559.08
Backward-pass share+0.090.20
Deep-pass share-0.660.22
Completion+0.720.85
Seconds between passes-0.053.29
Side used
left 26% · central 47% · right 27%
Progression (middle third) 8,811 passes
nothing unusual in this zone
Width of use-0.0925.37
Wide-channel share-0.060.40
Side bias+0.321.32
Pass length+0.3717.74
Long-ball share-0.340.09
Forward gain per pass-0.293.16
Backward-pass share+0.080.34
Deep-pass share-0.150.15
Completion+0.440.87
Seconds between passes+0.493.41
Side used
left 37% · central 30% · right 33%
Final third 4,647 passes
nothing unusual in this zone
Width of use+0.0631.76
Wide-channel share+0.140.60
Side bias+0.442.64
Pass length-0.5314.65
Long-ball share-0.580.07
Forward gain per pass-0.400.51
Backward-pass share+0.200.40
Deep-pass share-0.310.08
Completion+0.420.73
Seconds between passes-0.263.21
Side used
left 46% · central 15% · right 39%
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
#7 of 18
Counter-attack shots /game0.79
#17 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
#11 of 18 · lower = faster break
Sequence success — attacking transition
104attacking transition sequences this season
14%succeed — produce a shot
52%turned over with nothing to show
Sequence success — defensive transition
414defensive transition sequences this season
67%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 (#17), 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? 1241 opponent progressive passes and 308 shots faced (33.9 xG against) across the season.
Opponent threat by lane (attacking into Lille)
Right wing
28%
Right half-space
12%
Central
18%
Left half-space
15%
Left wing
27%
Where the danger came from. Opponents generated most of their threat through the right wing (28% 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 (197 total)
By situation
Open play
31 · 86%
Set pieces
3 · 8%
Penalties
2 · 6%
Vision AI Model Insight
36 goals conceded. 86% come in open play; set pieces account for only 8%, so dead-ball defending is not the main issue. 14% are headers — aerial defending is not where the damage comes from. Their supply line runs mainly through the left half-space of the attacking third (20% 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
38 g · 34.1 xG
Set pieces
5 g · 7.2 xG
Penalties
7 g
10% of goals come from set pieces (5 of 50), worth 7.2 xG from 99 shots. Set pieces are a secondary threat; the danger is in open play.
Where do Lille's corners go?
194 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 (91)
Delivered into the box 95%Short 5%
Left side (103)
Delivered into the box 98%Short 2%
Where opponents attack Lille from set pieces
34 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 (14)
Shot 14Goals 0
Left flag (20)
Shot 20Goals 2
Tactical Strengths & Weaknesses
Strengths (top-third vs league)
xG conceded /game — 1.00 (89th pct)
field tilt % — 61.58 (89th pct)
threat created /game — 12.25 (89th pct)
press intensity (PPDA) — 6.10 (83th pct)
progression /game — 51.32 (83th pct)
Weaknesses (bottom-third vs league)
none stand out
Style Archetype & Similar Teams
Team archetype: High-press possession side. Their closest stylistic matches in the league (cosine similarity on style metrics) — sides that play a similar way.
PSG
88%
Lens
82%
Lyon
77%
Monaco
69%
Marseille
57%
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 (11m)
1.27
1.18
1.91
vs mid-block opponents (11m)
1.55
1.27
1.55
vs low-block opponents (12m)
1.75
0.83
1.92
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
81%
13%
47
10.9
14.4
vs mid-block opponents
83%
11%
54
12.3
17.6
vs low-block opponents
85%
9%
53
13.4
16.9
Vision AI Model Insight
Facing a high press, retention drops to 81% vs 85% against a low block (4pt lower under pressure), and directness (long-ball share) sits at 13% when pressed vs 9% against a low block — a mild adjustment, not a wholesale change.
Scouting Summary — the team in brief
In brief. Lille are possession-dominant with a aggressive high-press.
Their attack creates 1.40 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 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 12 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).
+18.4on-ball value, 12 matches
Where the value came from
Passes6714 actions+6.57
Carries1636 actions-1.83
Dribbles197 actions-0.24
Shots141 actions+9.62
Ball wins734 actions+4.24
Most on-ball value (OBV)
Actions
OBV
per 100
Matias Fernandez-Pardo
432
+4.18
+0.97
Hákon Haraldsson
692
+2.63
+0.38
Olivier Giroud
65
+1.68
+2.59
Benjamin André
724
+1.17
+0.16
Ngal'ayel Mukau
477
+1.12
+0.24
Gaëtan Perrin
201
+0.98
+0.49
Thomas Meunier
719
+0.91
+0.13
Nabil Bentaleb
476
+0.87
+0.18
Biggest single moves
OBV
1 Matias Fernandez-Pardo · shot
+0.833
2 Olivier Giroud · shot
+0.825
3 Matias Fernandez-Pardo · shot
+0.790
4 Matias Fernandez-Pardo · pass
+0.537
5 Hákon Haraldsson · shot
+0.406
6 Matias Fernandez-Pardo · shot
+0.388
Set Pieces by Player
Who delivers Lille’s dead balls and who finishes them this season. 194 corners taken, most by Romain Perraud (45); 99 shots from corners and free kicks, 5 goals, led by Benjamin André (1 goals, 1.21 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
Romain Perraud
45
40 (89%)
6
1
0.21
2
—
Hákon Haraldsson
44
41 (93%)
4
—
0.13
1
—
Benjamin André
—
—
16
1
1.21
—
—
Matias Fernandez-Pardo
13
8 (62%)
8
—
1.55
—
2/2
Nathan Ngoy
—
—
13
1
0.96
—
—
Félix Correia
24
19 (79%)
4
—
0.09
—
—
Nabil Bentaleb
13
12 (92%)
4
—
0.14
1
1/1
Gaëtan Perrin
18
16 (89%)
3
—
0.09
1
—
Olivier Giroud
—
—
4
—
0.25
—
3/4
Aïssa Mandi
—
—
8
1
0.50
—
—
Ethan Mbappé
20
16 (80%)
1
—
0.02
1
—
Alexsandro Ribeiro
—
—
7
—
0.41
—
—
Chancel Mbemba
—
—
5
—
0.30
—
—
Hamza Igamane
—
—
4
—
0.27
—
—
Top Performers
Ranked by Vision AI Score — the same tactical behavioural evaluation each player's own report page shows (min 300 minutes). Coloured chips are that player's strongest capability blocks.
← BACK TO SEARCHSeason
