Combining every season — this one is larger, give it a moment.
Team reportLigue 1 2026
Nice
A balanced-territory side, aggressive high-press out of possession, with a low-output attack. Currently #10 of 18 on 11 points (3-2-3, 8:10). 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 Nice and how good was the season?
Read. Nice currently sit #10 of 18 on 11 points (3W-2D-3L, 1.38 pts/game), scoring 8 and conceding 10 for a goal difference of -2.
On the ball they are a balanced-territory team (53% field tilt, #5 in the league); off it they defend with a aggressive high-press (#11 of 18 for press intensity).
Across the 8 matches of event data, they create 0.88 xG/game (#18) and concede 1.36 (#8), a net of -0.48 xG/game — the underlying performance matches the actual goal difference.
Season Context — the league table
The question
Where did Nice actually finish, and against whom?
The authoritative final table (full season), centred on Nice.
#
team
P
W
D
L
GF
GA
GD
Pts
7
Lens
8
4
1
3
16
12
+4
13
8
Marseille
8
4
0
4
18
13
+5
12
9
Lorient
8
3
3
2
7
6
+1
12
10
Nice
8
3
2
3
8
10
-2
11
11
Toulouse
8
2
4
2
12
11
+1
10
12
Auxerre
8
3
1
4
12
18
-6
10
13
Brest
8
2
3
3
14
12
+2
9
Match Momentum — when they create and concede danger
The question
Across a whole game, which periods does Nice 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
Nice tend to come alive after the break — first-half net xG -2.60, second-half -1.21. Their strongest period is 61-75' (net +0.72 xG); their most dangerous period to face is 76-90+' (net -2.38 xG against them).
xG Race — the season, match by match
Expected goals piling up over 8 matches: Nice 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 (-3.8 xG). Hover a point for that match.
NiceOpponents against Nice
Tactical DNA
The football question
In five genes, what is Nice'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.
69
Build-Up
possession control from the back
81
Progression
moving the ball upfield with threat
17
Creation
manufacturing chances
56
Press
winning the ball high & hard
56
Solidity
resisting the opponent's threat
Genetic identity
Nice are defined most by their Progression DNA (81/100 · strong at moving the ball upfield with threat), and least by their Creation DNA (17/100). 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
40%
Possession
45%
Field tilt
35%
Disrupt
Central progression ~proxy
25%
Dribble tendency
60%
Progressive passes
35%
Finish
Final-third patience
55%
Shot quality
50%
Open-play xG
25%
Press
Forward press ~proxy
20%
High regains
35%
Pressing efficiency ~proxy
40%
Defend
Def-mid press ~proxy
30%
High line ~proxy
25%
PAdj defensive actions
40%
Nice is defined most by Finish (43% in the league) and weakest on Press (32%). 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 Nice actually control, relative to the rest of the league?
Field tilt = share of final-third touches between the two sides across Nice'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; Nice highlighted. Hover any dot for the team.
Vision AI Model Insight
Nice post 53% field tilt, ranking #5 of 18 in the league — comfortably above the league mid-point for territorial control, and their 1.38 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 Nice has the ball, how much of that time is spent building, progressing and creating?
The share of Nice'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
30.3%
Progression
46.7%
Chance creation
21.4%
Attacking transition
1.6%
Vision AI Model Insight
Progression is where Nice 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
Ndayishimiye ↔ Abdelmonem
226p · -1.0xT
Ndayishimiye ↔ Mandza
203p · -1.1xT
Diouf ↔ Ndayishimiye
179p · -0.8xT
Clauss ↔ Hein
135p · 2.6xT
Diouf ↔ Mandza
120p · -0.5xT
Clauss ↔ Abdelmonem
109p · 0.3xT
Hein ↔ Abdelmonem
80p · 0.8xT
Witsel ↔ Ndayishimiye
73p · -0.5xT
Nkounkou ↔ Mandza
66p · -1.1xT
Witsel ↔ Clauss
62p · 0.6xT
Vision AI Model Insight
Busiest connection: Youssouf Ndayishimiye ↔ Mohamed Abdelmonem (226 passes). Most dangerous per pass: Niels Nkounkou ↔ Mohammed Amoura (0.05 xT/pass over 36 passes) — the combination that hurts opponents most when it fires, not just the one used most.
Touch Density — where Nice 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 Nice 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
4682
ACTIONS
Carries
the ball moved with a player
1076
ACTIONS
Vision AI Model Insight
Their single busiest zone is the right half-space of the middle third (11.7% of all actions). 62% of actions run through the inside lanes (centre + both half-spaces) versus 38% down the two wings — an inside-dominant, half-space build. By third: 31% defensive, 48% middle, 21% 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. Nice leaned on their right (47%). The bars under the pitch put the league’s share beside theirs.
Nice
34%20%47%
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. Nice made 369 this season (46.1 a match); 37% 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
93
11.6
25%
30%
Left half-space
35
4.4
9%
13%
Centre
33
4.1
9%
11%
Right half-space
73
9.1
20%
14%
Right wing
135
16.9
37%
32%
Build-up2 sections
Build-Up — the pass map
The football question
How does Nice 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.
1426build-up passes attempted
85%completed
24%reach the middle third
1%go long into the final third
Pass types
600CB · 50%
51Long Wide · 4%
8Long Central · 1%
432Wide → FB · 36%
115Central · 10%
Sequence success — build-up
47build-up sequences this season
13%succeed — reach the next third or a shot
53%turned over with nothing to show
CB600 · 50%
Long Wide51 · 4%
Long Central8 · 1%
Wide → FB432 · 36%
Central115 · 10%
◆ Vision AI Insight
Nice build predominantly down the right, with Ndayishimiye as the fulcrum (287 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 → RHS def (119×).
Pass Explorer
Every pass of Nice’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. 391 progressive passes across 8 matches (48.9/game).
Origin lane
Right wing
41%
Right half-space
13%
Central
6%
Left half-space
8%
Left wing
32%
Progression is right-dominant: 53% right side · 6% central · 40% left.
Who generates it
Jonathan Clauss
84 · 21%
Gauthier Hein
50 · 13%
Niels Nkounkou
49 · 13%
Mohamed Abdelmonem
39 · 10%
Xavier Mandza
39 · 10%
Top two — Jonathan Clauss & Gauthier Hein — account for 34% of all progression.
PROGRESSION
48.9 prog passes/game
mostly right
→
PENETRATION
28% reach final third
18% reach the box
→
THREAT
the other 72% is recycled
progress ≠ danger
What came of it.28% of progressive passes reached the final third and 18% the box; the rest was recycled in front of the block. 34% 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
Centralsterile
Left half-spacebalanced
Left wingefficient
Progression — the pass map
The football question
Once Nice 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.
2236progression passes attempted
90%completed
14%break into the final third
12%go backward into the defensive third
Pass types
841CB · 42%
73Long Wide · 4%
13Long Central · 1%
883Wide → FB · 44%
210Central · 10%
Sequence success — progression
88progression sequences this season
35%succeed — reach the next third or a shot
40%turned over with nothing to show
CB841 · 42%
Long Wide73 · 4%
Long Central13 · 1%
Wide → FB883 · 44%
Central210 · 10%
◆ Vision AI Insight
Nice progress predominantly down the right, with Ndayishimiye the busiest midfield outlet (297 progression passes). Only 14% of passes that start in midfield actually break the final-third line — the rest (74%) 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 RHS mid → RW mid (141×) — 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
46%
Safe / backward
40%
14% of passes are progressive-or-better; the remaining 86% keep possession without advancing it. A control-first side — safe in build-up, reliant on a few players for the incision.
Where does Nice actually progress the ball with their feet, not a pass?
231 progressive carries this season across the whole squad (21% of 1076 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 46m. Most active carrier: Hein (31). Filter to one player below.
First (83)Second (57)Third (46)Fourth (45)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 Nice'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.
92shots
7.1xG
0.08xG per shot
0big chances (0.3+ xG)
6goals
#16key passes / game in the league
#11box entries / game in the league
Kind of attack, then how the shot was set up
Shots
Goals
xG
xG / shot
Attacking transition22% of shots
20
2
1.73
0.09
Cross
2
0
0.24
0.12
Cut-back
1
0
0.13
0.13
Long ball
2
1
0.19
0.09
Pass into the box / short pass
11
0
0.87
0.08
Own creation (carry, dribble, rebound)
4
1
0.31
0.08
Settled attack54% of shots
50
3
4.18
0.08
Cross
14
2
1.63
0.12
Cut-back
7
0
0.79
0.11
Long ball
1
0
0.04
0.04
Pass into the box / short pass
19
1
1.19
0.06
Own creation (carry, dribble, rebound)
9
0
0.53
0.06
Set piece24% of shots
22
1
1.16
0.05
Corner
21
1
1.14
0.05
Free kick
1
0
0.02
0.02
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
Gauthier Hein
9
1.11
1
Jonathan Clauss
10
1.08
1
Sofiane Diop
10
0.79
2
Mohammed Amoura
7
0.56
0
Niels Nkounkou
3
0.45
0
Who takes them
Player
Shots
xG
Goals
Elye Wahi
12
1.79
3
Jonathan Clauss
17
1.21
1
Mohammed Amoura
12
0.98
1
Axel Witsel
7
0.60
0
Gauthier Hein
6
0.51
0
Vision AI Model Insight
The biggest source of Nice's chances, by xG, is settled attack (4.2 of 7.1 xG, 50 of 92 shots), mostly cross. 20 of the 92 shots (22%) came within 15 seconds of winning the ball. 22 of the 92 shots came from set pieces; 6 goals from 7.1 xG (-1.1).
Chance Creation — the pass map
The football question
How does Nice 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
6CB · 9%
3Long Wide · 5%
6Long Central · 9%
18Wide → FB · 28%
32Central · 49%
Sequence success — final-third
89final-third sequences this season
26%succeed — produce a shot
42%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
CB6 · 9%
Long Wide3 · 5%
Long Central6 · 9%
Wide → FB18 · 28%
Central32 · 49%
Who creates — volume × threat
16Clauss
9Amoura
9Diop
9Hein
4Wahi
4Mandza
3Nkounkou
65key passes #16 of 18
12.1box entries/game #11 of 18
37.8final-3rd entries/game #4 of 18
0.88xG/game #18 of 18
6goals #15 of 18
-1.1finishing vs xG #9 of 18
◆ Vision AI Insight
Nice lean right — 40% of their key passes come from that flank and its half-space (26 of 65), most often from Clauss (12). They funnel chances into the c of the final third, most often via RW att → C att (10 key passes, most often from Clauss). Clauss is the primary source overall. They generate 0.88 xG a game and finish it wasteful — they under-perform their xG, led by Elye Wahi (3G), Sofiane Diop (1G), Mohammed Amoura (1G).
Crosses & Cutbacks — squad-wide
The question
How does Nice deliver into the box from wide areas, and what comes of it?
145 crosses: 51 found a team-mate (35%), 16 led to a shot (11%). 30 cut-backs: 10 found a team-mate (33%), 6 led to a shot (20%). 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 Nice actually cross from?
145 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 right wing; 8 come from the half-spaces and 0 from the centre.
Territory
Crosses
Share
Led to a shot
byline · right wing
35
24%
6
byline · left wing
35
24%
4
advanced · right wing
31
21%
3
advanced · left wing
20
14%
3
deep · left wing
11
8%
0
deep · right wing
5
3%
0
byline · right half-space
3
2%
0
advanced · left half-space
2
1%
0
byline · left half-space
2
1%
0
advanced · right half-space
1
1%
0
Finishing3 sections
Shot Map
92 shots for 7.1 xG (0.88/game, #18 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.
Low-output attack. 11.5 shots/game at 0.077 xG/shot. Box entries 12/game (#11). Threat created 11.9 xT/game (#3).
Goal Sequences
The move behind each of Nice’s 8 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 — 8 goals traced.
How Do They Score?
The question
Where do the goals actually come from — open play, dead balls, which foot?
Every goal Nice scored across the loaded matches, broken down by how it was created and finished.
By situation
Open play
5 · 83%
Set pieces
1 · 17%
Penalties
0 · 0%
By finish
Right foot
5 · 83%
Left foot
1 · 17%
Header
0 · 0%
Vision AI Model Insight
6 goals scored. 83% come in open play; set pieces add 17%. 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
281
Failed forward pass
181
Failed take-on
84
Lost in the defensive third
220
Their weakest zone is the right half-space in the attacking third: 33% of 209 pass attempts there are lost (68 times). Separately, 39% 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 Nice 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.
42
Pressing Index /100 · average
Intensity35
PPDA, high-press share, high-zone actions
Height33
how high up the pitch they defend
Effectiveness42
high recoveries & high-regain share
Disruption64
opponent completion & progression allowed
Every defensive action & where it happens
Where they win the ball back — by lane
RW
16%
RHS
19%
C
26%
LHS
18%
LW
21%
By third (high / mid / low regain)
Def 51%
Mid 35%
Att 14%
Read
Ranked #11 of 18 for press intensity, this is a high press. Note the tension: 51% of their raw defensive actions still occur in their own third — a possession effect, not a passive one. Dominating the ball (22% high-press share) means that when they do defend, it is often deep. Of recoveries, 52% are won high up the pitch.
Every pressing metric, ranked in the league
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#11/18
lower = more intense; rank is what matters
Defensive actions / game
148
#13/18
High-press share
22%
#11/18
def actions won high up
Defensive-action height (0–100)
38.0
#12/18
avg pitch height of actions
High recoveries / game
23.5
#8/18
ball won in mid+att third
High-recovery share
52%
#12/18
share of recoveries won high
Opponent completion allowed
83%
#9/18
lower = more disruptive
Opponent progressive allowed
7.9%
#4/18
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 Nice win it back with a defensive action?
Counted over the 288 opposition possessions that Nice 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 Nice step in. Nice win it after 6.2 opponent passes in the first half-hour and after 4.7 after the hour mark (sooner).
PASSES THEY GET
5.4passes
median 4 · before Nice win it
TIME THEY KEEP IT
16sec
median 10s · 36.0 wins per game
How many passes the opponent got in before the ball was won
38%
30%
17%
16%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
5.1
14s
15–30′
7.0
20s
30–45+′
5.0
14s
45–60′
5.8
18s
60–75′
4.2
12s
75–90+′
5.3
16s
average passesaverage seconds
Attitude in each third
Every other style number here says how much Nice 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 Nice 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) 1,426 passes
more recycling
Width of use-0.3220.34
Wide-channel share-0.620.27
Side bias-0.23-0.69
Pass length-0.2721.74
Long-ball share-0.590.17
Forward gain per pass-0.698.47
Backward-pass share+1.200.25
Deep-pass share-0.510.24
Completion+0.560.84
Seconds between passes-0.133.25
Side used
left 25% · central 47% · right 29%
Progression (middle third) 2,236 passes
more recycling, right-sided, safer
Width of use+0.3526.31
Wide-channel share+0.090.41
Side bias-0.91-3.54
Pass length+0.6418.12
Long-ball share-0.230.09
Forward gain per pass-0.742.49
Backward-pass share+0.950.38
Deep-pass share-0.490.14
Completion+0.880.90
Seconds between passes-0.443.00
Side used
left 31% · central 27% · right 42%
Final third 1,020 passes
right-sided
Width of use+0.2732.28
Wide-channel share+0.290.61
Side bias-0.96-7.25
Pass length+0.3315.98
Long-ball share+0.040.10
Forward gain per pass-0.560.30
Backward-pass share+0.640.43
Deep-pass share-0.050.09
Completion+0.240.72
Seconds between passes-0.203.25
Side used
left 33% · central 14% · right 52%
Reference measured across roughly 3,500 match-teams
in this competition, per third, so the comparison is with Nice'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 Nice 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 share16%
#13 of 18 · reaches final third in ≤3 passes
Patient build-up share30%
#11 of 18 · spells of 6+ passes
Tempo (possession length)17.6s
#8 of 18 · shorter = faster tempo
Passes per possession4.7
#11 of 18
Counter-attack shots /game0.95
#11 of 18 · shot within 12s of a regain in own half
Press→shot /game2.36
#18 of 18 · shot within 8s of a high regain
Speed to final third after regain12.0s
#7 of 18 · lower = faster break
Sequence success — attacking transition
6attacking transition sequences this season
0%succeed — produce a shot
33%turned over with nothing to show
Sequence success — defensive transition
31defensive transition sequences this season
48%succeed — reach the next third or a shot
0%turned over with nothing to show
Vision AI Model Insight
Nice are balanced between tempo and patience: 16% of possessions reach the final third in 3 passes or fewer (#13 of 18), against 30% built patiently over 6+ passes (#11 of 18). Winning the ball back leads to a shot inside 12 seconds 0.95 times a game (#11), and a high regain converts to a shot within 8 seconds 2.36 times a game (#18 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 Nice? 314 opponent progressive passes and 97 shots faced (10.9 xG against) across the season.
Opponent threat by lane (attacking into Nice)
Right wing
29%
Right half-space
16%
Central
16%
Left half-space
16%
Left wing
24%
Where the danger came from. Opponents generated most of their threat through the right wing (29% of the danger conceded).
How Do They Concede?
The question
Where do the goals against Nice actually come from — open play, dead balls, and who supplies them?
Every goal Nice conceded across the loaded matches — the opponent's shots in Nice'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 (71 total)
By situation
Open play
9 · 90%
Set pieces
0 · 0%
Penalties
1 · 10%
Vision AI Model Insight
10 goals conceded. 90% come in open play; set pieces account for only 0%, so dead-ball defending is not the main issue. 10% are headers — aerial defending is not where the damage comes from. Their supply line runs mainly through the right wing 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
5 g · 5.9 xG
Set pieces
1 g · 1.2 xG
Penalties
0 g
17% of goals come from set pieces (1 of 6), worth 1.2 xG from 22 shots. Set pieces are a secondary threat; the danger is in open play.
Where do Nice's corners go?
47 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 (26)
Delivered into the box 96%Short 4%
Left side (21)
Delivered into the box 86%Short 14%
Where opponents attack Nice from set pieces
12 shots from opponent corners against Nice, 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 (7)
Shot 7Goals 0
Left flag (5)
Shot 5Goals 0
Tactical Strengths & Weaknesses
Strengths (top-third vs league)
threat created /game — 11.94 (83th pct)
progression /game — 48.88 (78th pct)
field tilt % — 52.80 (72th pct)
pass retention % — 84.69 (67th pct)
Weaknesses (bottom-third vs league)
xG created /game — 0.88 (0th pct)
shots /game — 11.50 (17th pct)
box entries /game — 12.12 (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.
Monaco
82%
Toulouse
37%
Lorient
32%
Lille
27%
Marseille
11%
Performance vs Opponent Styles
The question
Not just how Nice 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 Nice'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
1.00
3.00
vs mid-block opponents (2m)
1.50
1.50
1.50
vs low-block opponents (4m)
0.25
1.25
0.50
Behaviour — how Nice actually play against each
opponent type
retention
directness (long balls)
prog. passes/g
xT/g
box entries/g
vs high-pressing opponents
82%
14%
35
8.5
9.0
vs mid-block opponents
84%
12%
42
10.9
12.5
vs low-block opponents
86%
10%
59
14.2
13.5
Vision AI Model Insight
Facing a high press, retention drops to 82% vs 86% against a low block (4pt lower under pressure), and directness (long-ball share) sits at 14% when pressed vs 10% against a low block — a mild adjustment, not a wholesale change.
Scouting Summary — the team in brief
In brief. Nice are balanced-territory with a aggressive high-press.
Their attack creates 0.88 xG a game and they concede 1.36 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 Nice’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 Nice’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).
+9.9on-ball value, 8 matches
Where the value came from
Passes4682 actions+3.92
Carries1076 actions+0.01
Dribbles127 actions-0.19
Shots92 actions+2.89
Ball wins535 actions+3.27
Most on-ball value (OBV)
Actions
OBV
per 100
Jonathan Clauss
607
+2.07
+0.34
Mohammed Amoura
233
+1.69
+0.72
Elye Wahi
167
+1.30
+0.78
Niels Nkounkou
359
+0.95
+0.26
Youssouf Ndayishimiye
841
+0.72
+0.09
Yéhvann Diouf
464
+0.69
+0.15
Mohamed Abdelmonem
651
+0.64
+0.10
Djibril Coulibaly
311
+0.52
+0.17
Biggest single moves
OBV
1 Elye Wahi · ball win
+0.598
2 Jonathan Clauss · pass
+0.403
3 Mohammed Amoura · shot
+0.345
4 Niels Nkounkou · pass
+0.316
5 Elye Wahi · shot
+0.304
6 Jonathan Clauss · pass
+0.272
Set Pieces by Player
Who delivers Nice’s dead balls and who finishes them this season. 47 corners taken, most by Jonathan Clauss (26); 22 shots from corners and free kicks, 1 goals, led by Elye Wahi (1 goals, 0.25 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
Jonathan Clauss
26
20 (77%)
4
—
0.10
1
—
Mohammed Amoura
2
1 (50%)
5
—
0.14
—
—
Gauthier Hein
16
14 (88%)
—
—
0.00
—
—
Xavier Mandza
—
—
2
—
0.06
—
—
Mohamed Abdelmonem
—
—
2
—
0.12
—
—
Axel Witsel
—
—
2
—
0.21
—
—
Elye Wahi
—
—
1
1
0.25
—
—
Antoine Mendy
—
—
1
—
0.07
—
—
Melvin Bard
—
—
1
—
0.03
—
—
Hamza Koutoune
—
—
1
—
0.03
—
—
Djibril Coulibaly
—
—
1
—
0.08
—
—
Youssouf Ndayishimiye
—
—
1
—
0.07
—
—
Alidu Seidu
—
—
1
—
0.02
—
—
Sofiane Diop
2
2 (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
Jonathan Clauss
M
677
2.13
27
14.6
Gauthier Hein
M
643
1.26
19
11.1
Mohammed Amoura
M
454
1.78
12
4.1
Sofiane Diop
M
340
2.38
8
4.3
Niels Nkounkou
M
508
0.53
11
8.4
Elye Wahi
F
552
0.65
6
1.9
Xavier Mandza
D
646
0.56
2
9.0
Hicham Boudaoui
M
262
0.69
3
1.7
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)
Jonathan Clauss
W
677
14 (57%)
10 (30%)
Defensive Volume Leaders
Who does the defensive work — tackles, interceptions, recoveries.
player
pos
min
def actions
tackles
intercept
recoveries
Youssouf Ndayishimiye
D
723
137
9
12
36
Mohamed Abdelmonem
D
627
129
20
12
28
Xavier Mandza
D
646
123
16
9
45
Niels Nkounkou
M
508
108
17
11
28
Djibril Coulibaly
M
511
91
6
6
25
Jonathan Clauss
M
677
79
11
6
37
Axel Witsel
M
571
71
3
8
28
Mohammed Amoura
M
454
62
10
2
16
Full Squad Data
Every player with 100+ minutes (18 players).
player
pos
apps
min
goals
xG
key P
prog
def
xT
Yéhvann Diouf
G
8
762
0
0.0
0
14
31
6.0
Youssouf Ndayishimiye
D
8
723
0
0.1
0
21
137
9.0
Jonathan Clauss
M
8
677
1
1.2
16
84
79
14.6
Xavier Mandza
D
7
646
0
0.2
4
39
123
9.0
Gauthier Hein
M
8
643
0
0.5
9
50
43
11.1
Mohamed Abdelmonem
D
8
627
0
0.2
2
39
129
10.0
Axel Witsel
M
7
571
0
0.6
2
10
71
4.5
Elye Wahi
F
7
552
3
1.8
4
3
40
1.9
Djibril Coulibaly
M
7
511
0
0.3
2
10
91
2.8
Niels Nkounkou
M
6
508
0
0.1
3
49
108
8.4
Mohammed Amoura
M
6
454
1
1.0
9
15
62
4.1
Sofiane Diop
M
8
340
1
0.4
9
16
38
4.3
Hicham Boudaoui
M
4
262
0
0.1
2
5
51
1.7
Melvin Bard
M
2
175
0
0.0
2
12
32
1.5
Nathan Ngoumou
F
5
170
0
0.3
0
3
20
0.9
Alidu Seidu
D
3
154
0
0.0
0
7
24
1.7
Antoine Mendy
D
3
139
0
0.1
1
3
26
2.4
Everton Pereira
M
3
126
0
0.0
0
2
22
0.5
Player Role Architecture
The question
Who does what — and what tactical job does each key player actually perform?
The nine most-used players (450+ minutes), each with the role their event fingerprint points to — not just their listed position.
Yéhvann Diouf
Goalkeeper · Goalkeeper
minutes762
goals · xG0 · 0.0
prog passes /901.7
key passes /900.00
def actions /903.7
pass accuracy79%
Youssouf Ndayishimiye
Stopper / marker · Defender
minutes723
goals · xG0 · 0.1
prog passes /902.6
key passes /900.00
def actions /9017.1
pass accuracy91%
Jonathan Clauss
Advanced creator · Attacking mid
minutes677
goals · xG1 · 1.2
prog passes /9011.2
key passes /902.13
def actions /9010.5
pass accuracy74%
Xavier Mandza
Ball-playing defender · Defender
minutes646
goals · xG0 · 0.2
prog passes /905.4
key passes /900.56
def actions /9017.1
pass accuracy88%
Gauthier Hein
Deep-lying playmaker · Midfielder
minutes643
goals · xG0 · 0.5
prog passes /907.0
key passes /901.26
def actions /906.0
pass accuracy85%
Mohamed Abdelmonem
Ball-playing defender · Defender
minutes627
goals · xG0 · 0.2
prog passes /905.6
key passes /900.29
def actions /9018.5
pass accuracy92%
Axel Witsel
Ball-winner / destroyer · Midfielder
minutes571
goals · xG0 · 0.6
prog passes /901.6
key passes /900.32
def actions /9011.2
pass accuracy95%
Elye Wahi
Box-to-box midfielder · Midfielder
minutes552
goals · xG3 · 1.8
prog passes /900.5
key passes /900.65
def actions /906.5
pass accuracy79%
Djibril Coulibaly
Ball-winner / destroyer · Midfielder
minutes511
goals · xG0 · 0.3
prog passes /901.8
key passes /900.35
def actions /9016.0
pass accuracy89%
Recruitment Intelligence — profiles to sign
The question
If you were sporting director, which player profiles would move the needle most?
Each recruitment target is derived directly from the team's weakest areas vs the league — the gap, why it shows in the data, and the profile that closes it.
Need — bottom-third: xG created /game (0th pct)
Sign a higher-volume goalscorer or chance creator. The data flags this because the attack under-produces xG (0.88, 0th percentile vs the league).
Need — bottom-third: shots /game (17th pct)
Sign a shot-taking forward to raise shot volume. The data flags this because shot volume is low (11.50, 17th percentile vs the league).
Need — bottom-third: box entries /game (33th pct)
Sign a penetrative wide player or runner to create box entries. The data flags this because few entries into the box (12.12, 33th percentile vs the league).