العربيةSeason
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Building the full report
Combining every season — this one is larger, give it a moment.
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Who he is and how he compares with his peers.
His repeatable signature. A step only appears if its reliability score (sample size × match coverage) reaches at least Medium confidence — pattern mining, not anecdotes.
Red fill = raw touch territory. Gold rings = zones where his influence (xT added, progression, line-breaks, key passes) outweighs how often he's actually there — one combined map instead of two separate maps to compare by eye. Red with no ring: present but not dangerous. A ring on faint red: rare touches, outsized impact.
What he does with the ball: build-up, progression, creation, carrying and finishing.
125 tracked carries (4.2/90, 31 progressive, 1155m 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 drop to link or stay central? Touches by channel. Season rate: 1.79 dribbles attempted /90.
How he moves the ball — his 4 busiest progression routes, not where every pass starts and ends separately. Arrow width = how often, colour = xT gained, dashed = switch of play. The full breakdown is in the lane matrix below.
Primary route: the right half-space in the middle third → the right half-space in the final third — 3 progressive passes (13% of his progression) at 0.162 xT each. He keeps it in the same lane.
His progression is inward: 52% of progressive passes move the ball toward the middle versus 26% going wider — he cuts in rather than holding width.
Only 5% of his passes switch play, so his threat is same-side — contain the flank he occupies and the route closes.
Quality behind the volume: 9 line-breaking passes and 28 final-third entries, -4.9 total xT added.
How much he drives the ball forward. Progressive = completed pass advancing ≥15 toward goal.
16 key passes (0.54/90), 0 of them assists — where his chance creation specifically comes from, 18 zones (6×3), showing his share of key passes originating there.
79 shots · 9.24 xG · 7 goals (-2.24 vs xG). Mostly right-footed. Circle size = xG.
190 possessions lost. Directness has a cost — the question is where. Attacking-third losses are cheap; deep ones are dangerous.
His progressive passes as zone→zone routes, weighted by expected threat (xT) added — arrow width and the number are the total xT generated on each route, not just how often he plays it. This is where he actually moves the needle.
The zones where Igor Jesus gets on the ball — his top three ball-collection areas highlighted (gold = primary). Reception is proxied by where his on-ball actions begin (no explicit receiver is recorded).
Every on-ball touch (pass, dribble, shot) binned into a 6×3 grid — raw counts of where Igor Jesus 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.
Same zone grid, restricted to passes so completed/incomplete and match filters actually mean something — a shot or a dribble has no "completed" in the pass sense.
Every pass this season, plotted individually — green = completed, grey (dashed) = incomplete, gold = key pass. Filter by outcome or isolate one match below.
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.
Four arrows, one per movement class of his ball-carries: outside→inside (cutting in with the ball), inside→outside (carrying wide), outside→outside (down the touchline) and inside→inside (through the middle). Each count is measured from the carry's real start and end coordinates.
Every carry's end point matched against his own very next pass or shot in the same match, when one starts almost exactly there — the direct continuation of that touch, not a separate stat. “No clean link” means no pass or shot of his own started close enough to count (often the carry ended in a tackle or a loss); it isn't a claim about exactly what happened, only that nothing traceable followed.
0 crosses (0.00/90, 0 completed) and 6 cutbacks (0.20/90, 4 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.
Where and how he defends and presses.
404 defensive actions across the season (13.6/90). 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 Igor Jesus keep producing when the game is tight and the press is high? Tiers of ~14-14 matches — directional evidence, not proof.
How much of the team runs through him, and what changes without him.
Nottingham Forest's output per match with Igor Jesus (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.
Of everything Nottingham Forest produced this season, the categories where the biggest share ran through Igor Jesus specifically — his share of the team total, not a per-90 rate.
Style profile, closest comparables, and the summary.
Not raw event counts — football style scores: weighted models scored 0–100 against the 299 strikers 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 / 73 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 Premier League, Bundesliga, La Liga, Ligue 1 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.
Igor Jesus is a transition runner / pressing forward: 7 goals from 9.24 xG (-2.24 vs expected), 79 shots, 8 box entries. Under-performing his chances. [Off-ball runs, aerials and hold-up under contact need tracking/aerial data.]
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.