Noah Nartey

His repeatable signature. A step only appears if its reliability score (sample size × match coverage) reaches at least Medium confidence — pattern mining, not anecdotes.

15 tracked carries (5.5/90, 6 progressive, 147m 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.

Only 6 dribbles attempted this season — too little volume to map a location pattern.

3 key passes (1.10/90), 0 of them assists — where his chance creation specifically comes from, 18 zones (6×3), showing his share of key passes originating there.

18 possessions lost. Directness has a cost — the question is where. Attacking-third losses are cheap; deep ones are dangerous.

Every on-ball touch (pass, dribble, shot) binned into a 6×3 grid — raw counts of where Noah Nartey actually gets on the ball. Hover any zone for its breakdown by action type, its share of all touches, and the exact coordinate range it covers.

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.

31 defensive actions across the season (11.3/90). 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.

Ball-wins in the attacking half only (own half vs. attacking half, a simple 2-way split, not the usual thirds) — the clearest single signal of how much he presses and recovers the ball high up the pitch.

Split by how hard the opponent pressed (opponent PPDA — lower = more intense press). Does Noah Nartey keep producing when the game is tight and the press is high? Tiers of ~2-3 matches — directional evidence, not proof.

Lyon's output per match with Noah Nartey (7 matches) versus without him (1 matches). Insufficient evidence — the without-sample is only 1 matches; treat every Δ below as noise until more games exist. A proper on-off model needs many more minutes.

Of everything Lyon produced this season, the categories where the biggest share ran through Noah Nartey specifically — his share of the team total, not a per-90 rate.

Not raw event counts — football style scores: weighted models scored 0–100 against the 247 attacking mids in the pool (min 450 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.

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: 180 cm / 70 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.

Noah Nartey is a half-space creator: 1.10 key passes /90, 2.6 progressive passes, 1 goals from 0.32 xG. A between-the-lines creator. [Off-ball movement into pockets needs tracking.]