His repeatable signature. A step only appears if its reliability score (sample size × match coverage) reaches at least Medium confidence — pattern mining, not anecdotes.
RECEIVE
Left Half-Space, Middle third — mostly in the middle third (49% of his 93 passes)
24 tracked carries (11.1/90, 1 progressive, 205m total) — the ball moving with his touches, a different signal from dribbles below (beating an opponent 1v1). this counts every touch-carry, not just his dribble attempts below — a fuller picture of how much of his ball-carrying is control-and-move rather than beating a man.
progressivelateral/short% of his 24 carries originating in each zone · own goal left, attacking →
Every carry, exact path — median 7m, longest 21m
By zone — where the carry started → where it ended
First (13)Second (11)grouped by carry pattern (start + end position), largest group first
startendown goal left, attacking →
Dribbles (1v1s)
Only 1 dribble attempted this season — too little volume to map a location pattern.
02
How does he create chances?
5 key passes (2.31/90), 0 of them assists — where his chance creation specifically comes from, 18 zones (6×3), showing his share of key passes originating there.
Chance creation — key pass origin zone share
Filter:
chance creation (5)assist (0)% of the selected group's key passes originating in each zone
05
Where does he become vulnerable?
18 possessions lost. Directness has a cost — the question is where. Attacking-third losses are cheap; deep ones are dangerous.
5
attacking third (cheap)
9
middle third
4
own third (dangerous)
Vision AI 28% of losses are in the attacking third — a notable share happen deeper, which invites transitions.
Mistake danger (proxy): 3.02 danger-index/90 · 0.364 avg/turnover. Weights each loss by threat conceded at that location. True xT-conceded needs the opponent possession model; positional proxy.
03
Tactical patterns — how his football actually looks
Three clustered sequence diagrams from his own event data — his three busiest routes: the way he progresses, the pattern behind his chances, and where a turnover turns dangerous.
own goal left, attacking →
Where do his chances come from?
Of 5 key passes, his busiest route is a delivery where he switches it out from the central to the left hs in the final third — 2 times. The receiver isn't identifiable in this data, only where the pass started and ended.
Where does he lose the ball most?
This is a RATE, not a raw count — the share of his touches in each zone that end in a turnover, so a zone he simply plays in more often doesn't automatically look riskier. His weakest zone is the left wing in the middle third: 22% of his 9 touches there are lost (2 times).
15 defensive actions across the season (6.9/90). Where he wins the ball, and how — the core question for a defender. Aerial duels are not recorded for this competition, so aerial dominance is not measured here.
click a card to isolate that action type · own goal left, attacking →
1
tackles · 100% won
1
interceptions · 0.5/90
7
recoveries · 3.2/90
2
clearances · 0.9/90
0
blocks
41
avg action height
2
won high (att 3rd)
6
middle third
7
own third
Vision AI A mid-block defender. He wins the ball mainly by stepping in — tackles, interceptions & recoveries outweigh clearances. 9 proactive ball-wins (4.2/90), tackle success 100%. He defends high, stepping out to intercept — suits a high line.
Team
Team context
How much of the team runs through him, and what changes without him.
Lille's output per match with Maurits Kjærgaard (5 matches) versus without him (3 matches). Insufficient evidence — the without-sample is only 3 matches; treat every Δ below as noise until more games exist. A proper on-off model needs many more minutes.
WITH (5)
WITHOUT (3)
Δ
goals for /match
1.20
1.67
-0.47
xG for /match
1.20
0.36
+0.83
goals against /match
0.80
0.67
+0.13
points /match
1.60
2.33
-0.73
08
His Share of the Team
Of everything Lille produced this season, the categories where the biggest share ran through Maurits Kjærgaard specifically — his share of the team total, not a per-90 rate.
category
share
him / team
box entries
2%
9 / 435
half-space entries
2%
15 / 806
expected goals (xG)
2%
0.6 / 37.3
Recruitment
Style and verdict
Style profile, closest comparables, and the summary.
Not raw event counts — football style scores: weighted models scored 0–100 against the 433 central mids in the pool (min 180 min). This is what separates players who post similar numbers but play differently. Proxy dimensions are inferred from on-ball data; true off-ball running / under-pressure context is not measured.
carrying, territory (wide vs central), defensive aggression, ball-winning
Unknown
aerial ability, marking, off-ball running — not in event data
Box threat
97
Receiving zones
97
Creativity
95
Line-breaking
94
Through-balls ~proxy
94
Passing directness
91
Final-third threat
65
Press resistance ~proxy
64
Crossing
62
Dribbling
61
Retention
60
Progression
57
Build-up
57
Transition ~proxy
42
Carrying
32
Territory (wide vs central)
32
Defensive aggression
17
Ball-winning
12
10
Similar Players — in style space
Closest comparables measured on the style scores above, within the same position family — so it separates how players are alike, not just how much they touch the ball. Each line explains the shared strength and the divergent mechanism. A light physical-profile term (height / weight; this player: 192 cm / 77 kg) is included at a quarter of one style axis's weight — enough to break ties between stylistic twins, not to override style.Scope: pooled across the Ligue 1, Bundesliga, La Liga, Premier League and Serie A, percentiled within each player's own league before comparing — a match from another league is marked with its league name.
Style, not quality:a high match here means two players tend to produce the ball in similar ways — it is not a ranking, a scouting recommendation, or a claim that they are equally good.
Christian Eriksen Wolfsburg · Bundesliga 25/26 · 182 cm / 76 kg
84%
Driven by: creativity (21%), box threat (16%), receiving zones (12%). Different mechanism — Kjærgaard more via passing directness, receiving zones; Eriksen more via ball-winning, defensive aggression.
Carlos Soler Real Sociedad · La Liga · 180 cm / 76 kg
81%
Driven by: creativity (16%), box threat (13%), receiving zones (12%). Different mechanism — Kjærgaard more via passing directness, box threat; Soler more via ball-winning.
Giacomo Calò Frosinone · Serie A · 180 cm / 74 kg
80%
Driven by: creativity (16%), box threat (14%), ball-winning (11%). Different mechanism — Calò more via progression, final-third threat.
Giovani Lo Celso Real Betis · La Liga · 177 cm / 68 kg
70%
Driven by: box threat (17%), creativity (16%), receiving zones (12%). Different mechanism — Kjærgaard more via passing directness; Celso more via territory (wide vs central), carrying.
Will Hughes Crystal Palace · Premier League · 185 cm / 74 kg
66%
Driven by: passing directness (22%), creativity (20%), ball-winning (11%). Different mechanism — Kjærgaard more via box threat, through-balls; Hughes more via ball-winning, defensive aggression.
Ísak Bergmann Jóhannesson Koln · Bundesliga · 180 cm / 72 kg
64%
Driven by: ball-winning (19%), through-balls (12%), defensive aggression (12%). Different mechanism — Kjærgaard more via box threat, creativity; Jóhannesson more via carrying, final-third threat.
11
Summary
Maurits Kjærgaard is an advanced playmaker (8) / tempo controller: 2.3 progressive passes, 2.31 key passes /90, 8 box entries, 0 goals from 0.60 xG. A box-to-box contributor across every phase.
Based on 5 matches (195 minutes) — an early read, treat with caution until more minutes are logged. He profiles as an advanced playmaker (8) / tempo controller — best used in a system built around that identity.
His biggest room for growth is progression — even a modest improvement there would meaningfully lift his overall advanced playmaker (8) profile.
✨ This report is interactive
Click any stat card to filter its map, hover a dot for the exact match, and use the dropdowns to slice passes zone-to-zone. Nothing here is static.
