Thin sample this season. Jorrel Hato has played 6 matches (418 min) so far, so sections that need a bigger sample to say anything reliable are not shown here — they are missing on purpose, not broken. See all seasons combined →
01
What Patterns Define Him?
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 Wing, Middle third — wide or inverted — mostly in the middle third (42% of his 147 passes)
78% completion · 147× in 6/6 matches
↓
PROGRESS
carry or combine up the flank
1.7 prog/90 · 8× in 5/6 matches
↓
DRIBBLE
beat his man up the line
57% won · 7× in 4/6 matches
↓
DELIVER
cross or cut-back into the box
4 key passes · 5× in 4/6 matches
↓
DEFEND
1v1 and cover on his side
64 def actions · lost 37% deep · 64× in 6/6 matches
Signature: receive wide/inverted → progress the flank → deliver into the box → recover to defend 1v1. Attack the space behind him in transition.
13
Where is he influential — vs where is he merely present?
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.
own goal left, attacking →
24
passes / match
13.8
def actions / 90
17–41
pass range (matches)
12
clearances
Pass range = his fewest and most passes in any single match this season (17 at the low end, 41 at the high) — a measure of match-to-match involvement, not pass distance.
In possession
In possession
What he does with the ball: build-up, progression, creation, carrying and finishing.
28 tracked carries (6.0/90, 8 progressive, 304m total) — the ball moving with his touches, a different signal from dribbles below (beating an opponent 1v1). even a player who rarely dribbles past a man can still carry the ball forward constantly in build-up; this is that.
progressivelateral/short% of his 28 carries originating in each zone · own goal left, attacking →
Every carry, exact path — median 8m, longest 41m
By zone — where the carry started → where it ended
First (16)Second (12)grouped by carry pattern (start + end position), largest group first
startendown goal left, attacking →
Beaten in the tackle
How often an attacker gets past him in a 1v1 defensive duel — the closest signal the data has to "dribbled past" (no attacker-beat-defender field exists directly, so this counts tackles he attempted and lost).
12
tackle duels
4
won
8
beaten · 67%
brighter band = more tackle duels · own goal left, attacking →
INSIDE 42%
OUTSIDE 58%
Tackle-duel location — 5 inside · 7 outside
Right Wing
0 · 0%
Right Half-Space
0 · 0%
Central
1 · 8%
Left Half-Space
4 · 33%
Left Wing
7 · 58%
Vision AI beaten in 8 of 12 tackle duels (67% loss rate) — a real defensive leak worth flagging.
Every tackle duel, exact location
wonlost — dribbled pastown goal left, attacking →
He attempted only 7 dribbles of his own all season — dribbling is not part of this role; his ball-carrying is covered above, his defending against the dribble below.
03
How does he create chances?
4 key passes (0.86/90), 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 (4)assist (0)% of the selected group's key passes originating in each zone
07
Where does he become vulnerable?
35 times he gave the ball away. For a defender the danger is where — a loss in his own third can turn into a chance against; higher up it costs little.
11
attacking third (cheap)
11
middle third
13
own third (dangerous)
Vision AI 31% of losses are in the attacking third — a notable share happen deeper, which invites transitions.
Mistake danger (proxy): 2.82 danger-index/90 · 0.374 avg/turnover. Weights each loss by threat conceded at that location. True xT-conceded needs the opponent possession model; positional proxy.
04
Where Does He Receive?
The zones where Jorrel Hato 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).
1 = primary, 2 = secondary, 3 = third · own goal left, attacking →
His collection zones
31%
Primary
Left Wing, Middle third
25%
Secondary
Left Wing, Attacking third
16%
Third
Left Wing, Defensive third
These are the zones where he collected the ball most.
05
Where Does He Operate? — Every Zone
Every on-ball touch (pass, dribble, shot) binned into a 6×3 grid — raw counts of where Jorrel Hato 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.
Filter:
own goal left, attacking → · brighter + higher number = more touches
Passes only — completed vs incomplete, by zone
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.
own goal left, attacking →
08
Every Pass This Season — completed vs incomplete
Every pass this season, plotted individually — green = completed, grey (dashed) = incomplete, gold = key pass. Filter by outcome or isolate one match below.
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 →
How does he progress the ball forward?
His most common route for progressing play: he plays straight through the left wing, advancing it forward in the middle third — 3 passes worth 0.41 xT.
Where do his chances come from?
Of 4 key passes, his busiest route is a delivery where he plays inside from the left 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 left wing in the defensive third: 39% of his 23 touches there are lost (9 times).
64 defensive actions across the season (13.8/90). 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 →
12
tackles · 33% won
3
interceptions · 0.6/90
14
recoveries · 3.0/90
12
clearances · 2.6/90
3
blocks
34
avg action height
10
won high (att 3rd)
15
middle third
39
own third
Vision AI A mid-block defender. He wins the ball mainly by stepping in — tackles, interceptions & recoveries outweigh clearances. 29 proactive ball-wins (6.2/90), tackle success 33%. He defends mostly around the middle third.
10
Can he play under pressure?
Split by how hard the opponent pressed (opponent PPDA — lower = more intense press). Can Jorrel Hato still play out when the opponent presses high? His build-up quality split by press intensity — the key question for a ball-playing defender. Tiers of ~2-2 matches — directional evidence, not proof.
press tier
completion
prog /90
line-breaks /90
xT /90
High press (PPDA 3.1, 2m)
76%
2.8
2.8
0.87
Mid press (PPDA 4.3, 2m)
79%
1.5
7.3
0.52
Low press (PPDA 5.2, 2m)
80%
0.7
4.6
0.82
Team
Team context
How much of the team runs through him, and what changes without him.
Chelsea's output per match with Jorrel Hato (6 matches) versus without him (2 matches). Insufficient evidence — the without-sample is only 2 matches; treat every Δ below as noise until more games exist. What his own defending should move — goals conceded and results — leads; attacking output (largely out of a defender's hands) is shown after, for context. A proper on-off model needs many more minutes.
WITH (6)
WITHOUT (2)
Δ
goals against /match
1.83
2.00
-0.17
points /match
1.67
1.50
+0.17
goals for /match
2.00
2.50
-0.50
xG for /match
1.30
0.80
+0.50
12
His Share of the Team
Of everything Chelsea produced this season, the categories where the biggest share ran through Jorrel Hato specifically — his share of the team total, not a per-90 rate.
category
share
him / team
tackles
6%
48 / 861
clearances
5%
56 / 1210
interceptions
4%
21 / 481
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 232 full-backs 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.
Inverted full-back80%Overlapping full-back39%
Why Inverted full-back 80%: a weighted blend of build-up 92 (weight 35%) + retention 97 (weight 30%) + press resistance 84 (weight 10%) + progression 15 (weight 25%). The score reflects how distinctively his profile SHAPE matches this archetype vs his peers.
aerial ability, marking, off-ball running — not in event data
Retention
97
Build-up
92
Press resistance ~proxy
84
Defensive aggression
74
Dribbling
73
Ball-winning
68
Creativity
63
Receiving zones
55
Crossing
54
Carrying
37
Passing directness
34
Transition ~proxy
31
Box threat
24
Final-third threat
21
Through-balls ~proxy
17
Progression
15
Line-breaking
9
Territory (wide vs central)
2
15
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: 182 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 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.
Marius Louër Angers · Ligue 1 · 181 cm / 70 kg
71%
Driven by: progression (13%), retention (12%), press resistance (12%). Different mechanism — Hato more via receiving zones, creativity; Louër more via passing directness, territory (wide vs central).
Genesis Antwi Strasbourg · Ligue 1 · 189 cm
69%
Driven by: retention (13%), press resistance (13%), progression (12%). Different mechanism — Hato more via creativity, receiving zones; Antwi more via territory (wide vs central).
Fodé Ballo-Touré Metz · Ligue 1 25/26 · 182 cm / 70 kg
65%
Driven by: press resistance (16%), retention (15%), progression (12%). Different mechanism — Hato more via receiving zones, defensive aggression; Ballo-Touré more via territory (wide vs central), carrying.
Reinildo Mandava Sunderland · 183 cm / 73 kg
62%
Driven by: progression (15%), line-breaking (11%), press resistance (10%). Different mechanism — Hato more via creativity, receiving zones; Mandava more via territory (wide vs central).
José Salinas Malaga · La Liga · 179 cm / 69 kg
59%
Driven by: territory (wide vs central) (12%), line-breaking (12%), retention (12%). Different mechanism — Hato more via build-up, crossing.
Piero Hincapié Arsenal · 183 cm / 77 kg
58%
Driven by: build-up (19%), territory (wide vs central) (16%), line-breaking (15%). Different mechanism — Hato more via dribbling, receiving zones; Hincapié more via passing directness, carrying.
16
Summary
Jorrel Hato is an inverted full-back: 1.7 progressive passes and 5 box entries, delivering 4 key passes. Overlap-first full-back. [1v1 recovery pace and off-ball overlap timing need tracking.]
Based on 6 matches (418 minutes) — an early read, treat with caution until more minutes are logged. He profiles as an inverted full-back — 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 inverted full-back 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.