Julián Vignolo

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

24 tracked carries (11.1/90, 10 progressive, 252m 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.

Does he carry to create or drift wide? Dribbles by channel. Season rate: 5.10 dribbles attempted /90.

1 key passes (0.46/90), 0 of them assists — where his chance creation specifically comes from, 18 zones (6×3), showing his share of key passes originating there.

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

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.

1 crosses (0.46/90, 0 completed) and 5 cutbacks (2.32/90, 3 completed) — two separate wide-delivery patterns into the box, each its own map with a completed/failed filter. Neither is a tagged event, so both are inferred from pass geometry — the intended end point of a failed attempt is still real data (an interception doesn’t erase where the ball was aimed): a cross is a pass aimed into the box starting from a wide channel; a cutback starts almost on the byline but is squared back rather than driven forward, aimed centrally around the box. The completed-cross count was checked against this platform’s own real match-level cross totals and lands within about 1 cross of the true count.

27 defensive actions across the season (12.5/90). 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.

Toulouse's output per match with Julián Vignolo (4 matches) versus without him (4 matches). Insufficient evidence — the without-sample is only 4 matches; treat every Δ below as noise until more games exist. A proper on-off model needs many more minutes.

Of everything Toulouse produced this season, the categories where the biggest share ran through Julián Vignolo 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: 185 cm) 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.

Julián Vignolo is a half-space creator: 0.46 key passes /90, 0.0 progressive passes, 0 goals from 0.24 xG. A between-the-lines creator. [Off-ball movement into pockets needs tracking.]