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 / MOVE
occupy the box, drop or run channel — mostly in the middle third (60% of his 90 passes)
20 tracked carries (3.6/90, 3 progressive, 153m 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 20 carries originating in each zone · own goal left, attacking →
Every carry, exact path — median 6m, longest 23m
By zone — where the carry started → where it ended
First (20)grouped by carry pattern (start + end position), largest group first
startendown goal left, attacking →
Dribbles (1v1s)
Only 3 dribbles attempted this season — too little volume to map a location pattern.
04
How does he create chances?
9 key passes (1.60/90), 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 (9)assist (0)% of the selected group's key passes originating in each zone
Box deliveries — key passes reaching the final third, by flank
from the left (5)from the right (4)of 9 key passes landing in the final thirdattacking ↑ toward goal
02
Can he finish?
13 shots · 1.57 xG · 1 goals (-0.57 vs xG). Mostly left-footed. Circle size = xG.
shots 13 on target 7 goals 1 xG 1.57 xG on target 1.09 xG / shot 0.121
Where he shoots from — click an area of the box
05
Where does he become vulnerable?
22 possessions lost. Directness has a cost — the question is where. Attacking-third losses are cheap; deep ones are dangerous.
9
attacking third (cheap)
10
middle third
3
own third (dangerous)
Vision AI 41% of losses are in the attacking third — a notable share happen deeper, which invites transitions.
Mistake danger (proxy): 1.04 danger-index/90 · 0.266 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 9 key passes, his busiest route is a delivery where he plays inside from the right hs toward the central 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 right half-space in the middle third: 38% of his 8 touches there are lost (3 times).
47 defensive actions across the season (8.4/90). 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 →
3
tackles · 33% won
1
interceptions · 0.2/90
7
recoveries · 1.2/90
3
clearances · 0.5/90
2
blocks
52
avg action height
15
won high (att 3rd)
21
middle third
11
own third
Vision AI A front-foot / high defender. He wins the ball mainly by stepping in — tackles, interceptions & recoveries outweigh clearances. 11 proactive ball-wins (2.0/90), tackle success 33%. He defends high, stepping out to intercept — suits a high line.
Defensive work in the attacking half
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.
24
attacking-half actions · 4.28/90
07
Against Different Pressing Intensities
Split by how hard the opponent pressed (opponent PPDA — lower = more intense press). Does Kieron Bowie keep producing when the game is tight and the press is high? Tiers of ~3-3 matches — directional evidence, not proof.
press tier
xT /90
prog /90
key P /90
dribble%
High press (PPDA 4.7, 3m)
0.17
0.4
0.80
50%
Mid press (PPDA 6.1, 3m)
0.13
0.0
0.00
0%
Low press (PPDA 7.7, 3m)
0.27
0.9
3.32
0%
Team
Team context
How much of the team runs through him, and what changes without him.
Insufficient sample: Kieron Bowie played nearly every Sassuolo match in the loaded data, so there is no meaningful "without him" comparison yet. This section fills in as more matches (and the rest of the season) are scraped.
10
His Share of the Team
Of everything Sassuolo produced this season, the categories where the biggest share ran through Kieron Bowie specifically — his share of the team total, not a per-90 rate.
category
share
him / team
expected goals (xG)
11%
5.0 / 43.7
shots
9%
46 / 509
goals
8%
1 / 13
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 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.
aerial ability, marking, off-ball running — not in event data
Line-breaking
66
Progression
57
Territory (wide vs central)
56
Transition ~proxy
50
Build-up
47
Carrying
45
Ball-winning
44
Box threat
44
Creativity
43
Passing directness
42
Defensive aggression
42
Retention
41
Final-third threat
41
Receiving zones
37
Crossing
37
Through-balls ~proxy
30
Press resistance ~proxy
29
Dribbling
23
12
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: 190 cm / 83 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 Serie A, Bundesliga, La Liga, Ligue 1 and Premier League, 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.
Robinio Vaz Hull City · 185 cm / 75 kg
73%
Driven by: dribbling (25%), press resistance (24%), crossing (11%). Different mechanism — Bowie more via passing directness, build-up; Vaz more via ball-winning, receiving zones.
Terem Moffi Hamburg · Bundesliga · 188 cm / 87 kg
71%
Driven by: dribbling (26%), through-balls (21%), press resistance (15%). Different mechanism — Bowie more via ball-winning, through-balls.
Jamie Vardy Cremonese 25/26 · 179 cm / 74 kg
69%
Driven by: press resistance (21%), dribbling (15%), through-balls (9%). Different mechanism — Bowie more via box threat, carrying; Vardy more via passing directness.
Conrad Harder RBL · Bundesliga · 185 cm / 71 kg
68%
Driven by: press resistance (21%), dribbling (21%), crossing (17%). Different mechanism — Bowie more via crossing, passing directness; Harder more via carrying, receiving zones.
Willem Geubbels Lecce · 185 cm / 81 kg
66%
Driven by: press resistance (26%), dribbling (26%), through-balls (13%). Different mechanism — Bowie more via line-breaking, retention; Geubbels more via territory (wide vs central), box threat.
Mikkel Kaufmann Heidenheim · Bundesliga 25/26 · 190 cm / 81 kg
65%
Driven by: press resistance (27%), dribbling (26%), through-balls (14%). Different mechanism — Bowie more via line-breaking, carrying; Kaufmann more via passing directness, crossing.
13
Summary
Kieron Bowie is a false 9: 1 goals from 1.57 xG (-0.57 vs expected), 13 shots, 3 box entries. Under-performing his chances. [Off-ball runs, aerials and hold-up under contact need tracking/aerial data.]
Based on 9 matches (505 minutes) — an early read, treat with caution until more minutes are logged. He profiles as a false 9 — best used in a system built around that identity.
His biggest room for growth is creativity — even a modest improvement there would meaningfully lift his overall false 9 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.