Combining every season — this one is larger, give it a moment.
Team reportPremier League 2026
Leeds
A territorially-dominated side, aggressive high-press out of possession, with a moderate attack. Currently #7 of 20 on 11 points (2-5-1, 9:6). Tactical profile below is built from 8 matches of ball-by-ball event data, contextualised against the league.
In one read, what kind of team is Leeds and how good was the season?
Read. Leeds currently sit #7 of 20 on 11 points (2W-5D-1L, 1.38 pts/game), scoring 9 and conceding 6 for a goal difference of +3.
On the ball they are a territorially-dominated team (43% field tilt, #15 in the league); off it they defend with a aggressive high-press (#6 of 20 for press intensity).
Across the 8 matches of event data, they create 1.23 xG/game (#11) and concede 1.23 (#7), a net of -0.00 xG/game — the underlying performance matches the actual goal difference.
Season Context — the league table
The question
Where did Leeds actually finish, and against whom?
The authoritative final table (full season), centred on Leeds.
#
team
P
W
D
L
GF
GA
GD
Pts
4
Liverpool
8
4
3
1
11
8
+3
15
5
Chelsea
8
4
1
3
17
15
+2
13
6
Brentford
8
2
5
1
14
9
+5
11
7
Leeds
8
2
5
1
9
6
+3
11
8
Newcastle
8
3
2
3
12
14
-2
11
9
Aston Villa
8
3
2
3
9
12
-3
11
10
Hull City
8
2
4
2
9
8
+1
10
Match Momentum — when they create and concede danger
The question
Across a whole game, which periods does Leeds actually dominate for chances — and which periods hurt them?
Net xG (created minus conceded) in each 15-minute window, summed across the whole season's matches — above the line is time they dominate for chances, below is time it hurts them. Real on-clock shot timing.
dominant (net xG for)conceding (net xG against)
Vision AI Model Insight
Leeds tend to fade after a strong start — first-half net xG +2.17, second-half -2.20. Their strongest period is 0-15' (net +1.47 xG); their most dangerous period to face is 61-75' (net -1.52 xG against them).
xG Race — the season, match by match
Expected goals piling up over 8 matches: Leeds against what their opponents created. A line pulling away upwards is a side creating more than it gives away; the gap at the end is the season’s balance (-0.0 xG). Hover a point for that match.
LeedsOpponents against Leeds
Tactical DNA
The football question
In five genes, what is Leeds's footballing identity?
Each gene is a 0–100 score — the team's league percentile across the metrics that define that phase of play. It distils the whole report into an identity fingerprint: the taller the gene, the more that trait defines them.
18
Build-Up
possession control from the back
30
Progression
moving the ball upfield with threat
32
Creation
manufacturing chances
48
Press
winning the ball high & hard
65
Solidity
resisting the opponent's threat
Genetic identity
Leeds are defined most by their Solidity DNA (65/100 · average at resisting the opponent's threat), and least by their Build-Up DNA (18/100). Percentiles vs the 20 teams in the league.
The Playstyle Wheel
Fifteen style metrics across five domains — Possess, Disrupt, Finish, Press, Defend — each a percentile against the rest of the league. Metrics marked ~proxy use the closest available event-data signal where the standard definition needs data this feed doesn't carry (explicit pressure events, offside/keeper-sweep incidents).
Possess
Deep buildup
30%
Possession
39%
Field tilt
39%
Disrupt
Central progression ~proxy
13%
Dribble tendency
52%
Progressive passes
26%
Finish
Final-third patience
4%
Shot quality
70%
Open-play xG
61%
Press
Forward press ~proxy
70%
High regains
65%
Pressing efficiency ~proxy
74%
Defend
Def-mid press ~proxy
43%
High line ~proxy
78%
PAdj defensive actions
52%
Leeds is defined most by Press (70% in the league) and weakest on Disrupt (30%). Percentiles vs the 23 teams in the league this season.
In possession
In possession
How they keep the ball, build up, progress, create and finish, and where their attacks die.
How much of the pitch does Leeds actually control, relative to the rest of the league?
Field tilt = share of final-third touches between the two sides across Leeds's matches, the standard proxy for territorial dominance. Plotted against points per game for every side in the league — the real question isn't just who controls territory, it's whether that control actually converts into results. Dashed lines mark the league average on each axis; Leeds highlighted. Hover any dot for the team.
Vision AI Model Insight
Leeds post 43% field tilt, ranking #15 of 20 in the league — below the league mid-point, spending more of the game defending their own territory than dictating it, and their 1.38 points/game sits above the league-average return — a real disconnect: their territorial control isn't showing up on the scoreboard the way it should.
Where the time goes — how long each phase lasts
The question
When Leeds has the ball, how much of that time is spent building, progressing and creating?
The share of Leeds's time on the ball spent in each phase of play, in the order the phases happen. A phase is where the ball is on the pitch (own third, middle third, final third); a transition or a set piece skips the build-up and is counted on its own.
Phase
Share of the time
Build-up
35.6%
Progression
41.9%
Chance creation
20.2%
Attacking transition
2.3%
Vision AI Model Insight
Progression is where Leeds spends most of its time on the ball (42% of its time on the ball).
Passing Network
The question
Who actually plays with whom — the real on-pitch connections, not just average positions?
Every pass linked to its likely receiver (the next same-team touch), aggregated into player-pair combinations at each player's average position. Line width = how often the pair combines; colour = threat generated per pass (blue low → red high). Hover a line for the pair, or click a player to isolate their connections.
width = passes between the paircolour = xT per pass: low → high
Top 10 combinations
Trafford ↔ Muharemovic
115p · -0.6xT
Ampadu ↔ Muharemovic
101p · 1.3xT
Elvedi ↔ Muharemovic
81p · -0.3xT
Tanaka ↔ Muharemovic
78p · -0.0xT
Gudmundsson ↔ Muharemovic
70p · 0.5xT
Justin ↔ Bogle
68p · 0.9xT
Elvedi ↔ Trafford
65p · -0.7xT
Justin ↔ Ampadu
61p · 0.0xT
Elvedi ↔ Justin
60p · -0.5xT
Elvedi ↔ Ampadu
51p · 0.2xT
Vision AI Model Insight
Busiest connection: James Trafford ↔ Tarik Muharemovic (115 passes). Most dangerous per pass: James Justin ↔ Anton Stach (0.04 xT/pass over 38 passes) — the combination that hurts opponents most when it fires, not just the one used most.
Touch Density — where Leeds had the ball
Every touch across 80 matches. Brighter means more touches.
Which Zones Do They Attack Through?
The question
Across the whole pitch, where does Leeds actually do its on-ball work?
Passes on one map, carries (the ball travelling with a player) on the other. Each is binned into the 15 pitch zones (three thirds × five vertical lanes) and shaded by its share of that kind of action; the arrows are the real actions.
Passes
the ball moved by a pass
3407
ACTIONS
Carries
the ball moved with a player
749
ACTIONS
Vision AI Model Insight
Their single busiest zone is the centre of the defensive third (13.9% of all actions). 62% of actions run through the inside lanes (centre + both half-spaces) versus 38% down the two wings — an inside-dominant, half-space build. By third: 36% defensive, 47% middle, 18% attacking.
Attack Sides
Which channel they attacked through this season: the share of every action made in the opponent’s half, down the left, through the middle and down the right. Leeds leaned on their right (38%). The bars under the pitch put the league’s share beside theirs.
Leeds
35%27%38%
League
39%24%37%
Final-Third Entries
Every completed pass or carry that took the ball from outside the final third into it, by the channel it arrived in. Leeds made 275 this season (34.4 a match); 33% came into the right wing. Longer, bolder arrows mean more entries. Entries by carrying the ball in count as well as completed passes; dribbles are not counted.
Channel
Entries
Per match
Share
League share
Left wing
69
8.6
25%
31%
Left half-space
42
5.2
15%
16%
Centre
47
5.9
17%
13%
Right half-space
26
3.2
9%
14%
Right wing
91
11.4
33%
27%
Build-up2 sections
Build-Up — the pass map
The football question
How does Leeds progress possession from defence into attack — and who runs it?
Build-up passes grouped into 5 real categories — not the old zone grid, not raw individual lines: CB (passer is a centre-back), Wide→FB, Central, Long Wide, Long Central. Each gets its own panel below, so nothing competes for attention. Arrow width = volume, labelled with the exact count; hover for the top passer.
1182build-up passes attempted
77%completed
27%reach the middle third
3%go long into the final third
Pass types
434CB · 48%
80Long Wide · 9%
14Long Central · 2%
302Wide → FB · 33%
81Central · 9%
Sequence success — build-up
120build-up sequences this season
4%succeed — reach the next third or a shot
44%turned over with nothing to show
CB434 · 48%
Long Wide80 · 9%
Long Central14 · 2%
Wide → FB302 · 33%
Central81 · 9%
◆ Vision AI Insight
Leeds build predominantly down the left, with Trafford as the fulcrum (253 build-up passes). Only 31% of their build-up passes actually advance a third — the rest is lateral circulation, the hallmark of a possession-controlling side that circulates patiently in midfield before progressing. Their single most-repeated pattern is C def → RHS def (73×).
Pass Explorer
Every pass of Leeds’s last 8 matches as an arrow, theirs first and the opponents’ with other team, each side attacking left to right (solid = completed, dashed grey = failed). Eight families per team — click any map to open it wide, then filter by channel or third, from one to another (or click a cell of the flow grids), by outcome, half, minute and passer. Channels: right wing, right half-space, centre, left half-space, left wing.
From channel
To channel
From third
To third
Outcome
Minutes
Channel to channel · click a cell
Third to third · click a cell
Progression4 sections
How They Progress — the mechanism
Not just how many progressive passes, but where they start, who plays them, and whether they actually penetrate. 307 progressive passes across 8 matches (38.4/game).
Origin lane
Right wing
33%
Right half-space
12%
Central
21%
Left half-space
11%
Left wing
22%
Progression is right-dominant: 45% right side · 21% central · 34% left.
Who generates it
James Trafford
61 · 20%
Ethan Ampadu
45 · 15%
James Justin
31 · 10%
Jayden Bogle
31 · 10%
Tarik Muharemovic
28 · 9%
Top two — James Trafford & Ethan Ampadu — account for 35% of all progression.
PROGRESSION
38.4 prog passes/game
mostly right
→
PENETRATION
31% reach final third
23% reach the box
→
THREAT
the other 69% is recycled
progress ≠ danger
What came of it.31% of progressive passes reached the final third and 23% the box; the rest was recycled in front of the block. 35% of the progression ran through two players, mostly down the right side.
Which lanes actually create the threat
Share of passes played from each lane against its share of the threat (xT) they add. Efficient = more threat than volume, sterile = lots of the ball, little damage.
Per lane — volume vs threat
Right wingefficient
Right half-spacesterile
Centralbalanced
Left half-spacebalanced
Left wingbalanced
Progression — the pass map
The football question
Once Leeds have the ball in midfield, how do they actually break into the final third — and who carries that job?
Progression passes grouped into 5 real categories: CB, Wide→FB, Central, Long Wide, Long Central. Each gets its own panel below, so nothing competes for attention. Arrow width = volume, labelled with the exact count; hover for the top passer.
1595progression passes attempted
80%completed
16%break into the final third
18%go backward into the defensive third
Pass types
512CB · 40%
44Long Wide · 3%
8Long Central · 1%
532Wide → FB · 42%
180Central · 14%
Sequence success — progression
209progression sequences this season
23%succeed — reach the next third or a shot
50%turned over with nothing to show
CB512 · 40%
Long Wide44 · 3%
Long Central8 · 1%
Wide → FB532 · 42%
Central180 · 14%
◆ Vision AI Insight
Leeds progress predominantly down the right, with Ampadu the busiest midfield outlet (211 progression passes). Only 16% of passes that start in midfield actually break the final-third line — the rest (66%) is recycled back into midfield or (18%) played backward, the hallmark of a side that carries progression itself through midfield rather than just passing through it. Their most-repeated pattern is LHS mid → LW mid (66×) — press that lane and midfield progression stalls.
Decision Quality — not all completed passes are equal
A 90% pass completion can hide a sideways habit. Grading every pass by the threat it adds separates value creation from safe circulation.
Excellent (high xT gain)
14%
Progressive
0%
Neutral / sideways
51%
Safe / backward
34%
14% of passes are progressive-or-better; the remaining 86% keep possession without advancing it. A control-first side — safe in build-up, reliant on a few players for the incision.
Where does Leeds actually progress the ball with their feet, not a pass?
180 progressive carries this season across the whole squad (24% of 749 tracked carries total), grouped into up to 4 spatial patterns by where each carry starts and ends together — not just its origin zone. Median 15m, longest 41m. Most active carrier: Elvedi (30). Filter to one player below.
First (71)Second (43)Third (37)Fourth (29)grouped by carry pattern (start + end position), largest group first
startendown goal left, attacking →
Chance creation4 sections
Chance creation — how the shots come
The question
Where do Leeds's chances actually come from?
Every shot sorted twice. First by the kind of attack: an attacking transition (the shot came within 15 seconds of winning the ball), a settled attack (open play after the team had the ball), or a set piece. Then by how it was set up: a cross, a cut-back, a long ball, a pass into the box, or no pass at all (the player created it himself). So a cut-back from an attacking transition is counted apart from a cut-back in a settled attack.
95shots
9.8xG
0.10xG per shot
5big chances (0.3+ xG)
8goals
#20key passes / game in the league
#15box entries / game in the league
Kind of attack, then how the shot was set up
Shots
Goals
xG
xG / shot
Attacking transition20% of shots
19
1
2.25
0.12
Cross
3
1
0.52
0.17
Pass into the box / short pass
6
0
0.32
0.05
Own creation (carry, dribble, rebound)
10
0
1.41
0.14
Settled attack60% of shots
57
4
5.46
0.10
Cross
19
1
2.23
0.12
Cut-back
4
1
0.32
0.08
Long ball
2
0
0.07
0.04
Pass into the box / short pass
13
1
1.56
0.12
Own creation (carry, dribble, rebound)
19
1
1.27
0.07
Set piece20% of shots
19
3
2.12
0.11
Corner
15
2
2.03
0.14
Free kick
4
1
0.09
0.02
Every attempt to create — and what came of it
Every pass into the box, every cut-back and every pass before a shot. Red led to a shot, gold to a goal; grey ones were tried but no shot came: solid found a team-mate, dashed lost the ball. Filter by outcome, type or player; tap an arrow for the detail.
Who sets the shots up
Player
Set-ups
xG created
Goals
Ethan Ampadu
12
1.59
1
James Justin
7
1.17
1
Tarik Muharemovic
3
0.51
1
Anton Stach
6
0.36
0
Noah Okafor
3
0.35
0
Who takes them
Player
Shots
xG
Goals
Dominic Calvert-Lewin
14
2.72
3
Jayden Bogle
10
2.10
2
Anton Stach
11
0.83
1
Ao Tanaka
9
0.75
0
Tarik Muharemovic
7
0.69
0
Vision AI Model Insight
The biggest source of Leeds's chances, by xG, is settled attack (5.5 of 9.8 xG, 57 of 95 shots), mostly cross. 19 of the 95 shots (20%) came within 15 seconds of winning the ball. 19 of the 95 shots came from set pieces; 8 goals from 9.8 xG (-1.8).
Chance Creation — the pass map
The football question
How does Leeds consistently create dangerous chances — from which zones, and who creates them?
Key passes (the pass immediately before a shot) grouped into 5 real categories: CB, Wide→FB, Central, Long Wide, Long Central. Each gets its own panel below, so nothing competes for attention. Arrow width = volume, labelled with the exact count; hover for the top creator.
Pass types
11CB · 22%
6Long Wide · 12%
8Long Central · 16%
10Wide → FB · 20%
14Central · 29%
Sequence success — final-third
128final-third sequences this season
24%succeed — produce a shot
46%turned over with nothing to show
Danger Funnel — which service carries threat
Every key pass pooled by where it was played FROM, then drawn with its arrow width set by total xT rather than by count. A zone that produces a lot of cheap balls shrinks; one that produces a few decisive ones dominates. Where this disagrees with the volume map, the volume map is the misleading one.
0 key passes shown● low threat● mid● high threatarrow width = total xT, not count
CB11 · 22%
Long Wide6 · 12%
Long Central8 · 16%
Wide → FB10 · 20%
Central14 · 29%
Who creates — volume × threat
10Stach
9Ampadu
6Bogle
6Justin
5Wilson
3Gudmundsson
3Okafor
49key passes #20 of 20
11.0box entries/game #15 of 20
29.0final-3rd entries/game #17 of 20
1.23xG/game #11 of 20
8goals #11 of 20
-1.8finishing vs xG #14 of 20
◆ Vision AI Insight
Leeds lean right — 55% of their key passes come from that flank and its half-space (27 of 49), most often from Stach (6). They funnel chances into the c of the final third, most often via RW att → C att (14 key passes, most often from Stach). Stach is the primary source overall. They generate 1.23 xG a game and finish it wasteful — they under-perform their xG, led by Dominic Calvert-Lewin (3G), Jayden Bogle (2G), Noah Okafor (1G).
Crosses & Cutbacks — squad-wide
The question
How does Leeds deliver into the box from wide areas, and what comes of it?
145 crosses: 45 found a team-mate (31%), 17 led to a shot (12%). 18 cut-backs: 5 found a team-mate (28%), 4 led to a shot (22%). A cross starts wide and is aimed into the box; a cut-back starts near the byline and is squared back. Grey ones were tried but no shot came: solid found a team-mate, dashed lost the ball.
Crosses by Territory
The question
Which part of the final third does Leeds actually cross from?
145 crosses this season (the same crosses as the map above), split into fifteen territories — five channels (both wings, both half-spaces and the centre) by three depths from the halfway line to the byline. 30% come from one territory, the advanced right wing; 6 come from the half-spaces and 0 from the centre.
Territory
Crosses
Share
Led to a shot
advanced · right wing
43
30%
8
advanced · left wing
28
19%
2
byline · right wing
28
19%
4
byline · left wing
26
18%
1
deep · right wing
7
5%
0
deep · left wing
7
5%
0
advanced · left half-space
3
2%
1
byline · right half-space
2
1%
0
byline · left half-space
1
1%
1
Finishing3 sections
Shot Map
95 shots for 9.8 xG (1.23/game, #11 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.
Moderate attack. 11.9 shots/game at 0.104 xG/shot. Box entries 11/game (#15). Threat created 9.6 xT/game (#14).
Goal Sequences
The move behind each of Leeds’s 9 goals this season, from the moment they got the ball to the finish. Pick a goal from the list, then step through it or press Play: numbered circles are passes and touches, dotted lines are carries (the ball travelling with a player), dashed grey is a pass that was lost, the star is the shot.
Each goal’s possession, rebuilt from the raw event feed — 9 goals traced.
How Do They Score?
The question
Where do the goals actually come from — open play, dead balls, which foot?
Every goal Leeds scored across the loaded matches, broken down by how it was created and finished.
By situation
Open play
5 · 62%
Set pieces
3 · 38%
Penalties
0 · 0%
By finish
Right foot
5 · 62%
Left foot
2 · 25%
Header
1 · 12%
Vision AI Model Insight
8 goals scored. 62% come in open play, but 38% from set pieces — a genuine dead-ball weapon that bypasses the open-play build-up.
When they lose it1 section
Ball-Loss Map — where attacks die
Every turnover has a location and a cause. This is a rate, not a raw count — the share of pass attempts from each zone that end in a turnover, so a zone they simply play through more often doesn't automatically look riskier. Grey cells are real zones with too few attempts to trust a rate.
Loss rate by zone
How they lose it
Incomplete pass — attacking third
283
Failed forward pass
209
Failed take-on
89
Lost in the defensive third
271
Their weakest zone is the left wing in the attacking third: 51% of 167 pass attempts there are lost (86 times). Separately, 32% of all losses happen in the attacking third — a fair share happen deeper, which invites transitions.
Out of possession
Out of possession
How they press, where they win the ball back, and how they concede.
How hard does Leeds press, where, and does it actually work — beyond a single PPDA number?
A composite Pressing Index built from four event-derived dimensions, each ranked against the league. Not one metric — intensity, height, effectiveness and opponent disruption together. The pitch shows every defensive action (the only five events this data records); the white line is the average height that feeds the score. Toggle by action type below, hover any dot for the player.
71
Pressing Index /100 · strong
Intensity82
PPDA, high-press share, high-zone actions
Height75
how high up the pitch they defend
Effectiveness58
high recoveries & high-regain share
Disruption70
opponent completion & progression allowed
Every defensive action & where it happens
Where they win the ball back — by lane
RW
19%
RHS
22%
C
24%
LHS
16%
LW
19%
By third (high / mid / low regain)
Def 45%
Mid 37%
Att 18%
Read
Ranked #6 of 20 for press intensity, this is a high press. 55% of defensive actions happen in the middle & attacking thirds, at an average height of 40. Of recoveries, 58% are won high up the pitch.
Every pressing metric, ranked in the league
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#6/20
lower = more intense; rank is what matters
Defensive actions / game
169
#4/20
High-press share
26%
#2/20
def actions won high up
Defensive-action height (0–100)
40.3
#5/20
avg pitch height of actions
High recoveries / game
20.5
#12/20
ball won in mid+att third
High-recovery share
58%
#5/20
share of recoveries won high
Opponent completion allowed
78%
#5/20
lower = more disruptive
Opponent progressive allowed
8.4%
#7/20
of opp passes, lower = better
Transition timing
This source carries per-event timestamps, so genuine transition metrics — counter-attack shots, press→shot conversions, time-to-recover — are measured; see the dedicated Transitions section below.
How long they keep it — passes and seconds until the ball is won
The question
Once the opponent has the ball, how many passes and how many seconds pass before Leeds win it back with a defensive action?
Counted over the 210 opposition possessions that Leeds ended with a tackle, interception, recovery or challenge. Fewer passes and seconds means the ball is won quickly (a press); more means the opponent is allowed to play before Leeds step in. Leeds win it after 5.9 opponent passes in the first half-hour and after 4.7 after the hour mark (sooner).
PASSES THEY GET
5.0passes
median 3 · before Leeds win it
TIME THEY KEEP IT
14sec
median 8s · 26.2 wins per game
How many passes the opponent got in before the ball was won
43%
26%
18%
13%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
5.8
18s
15–30′
6.1
16s
30–45+′
4.6
14s
45–60′
4.2
13s
60–75′
6.3
19s
75–90+′
3.6
8s
average passesaverage seconds
Attitude in each third
Every other style number here says how much Leeds does. This says
how: wide or central, short or long, patient or direct. Bars are standard deviations
from the league mean for passes played from that third; a solid bar marks a dimension
where Leeds are genuinely unusual, and a verdict is only written where something clears
0.85 sd — a side that is ordinary in a third is told so rather than given a label
for average play.
Build-up (own third) 1,182 passes
slower
Width of use-0.2820.42
Wide-channel share-0.240.29
Side bias-0.13-0.35
Pass length+0.7525.42
Long-ball share+0.470.28
Forward gain per pass+0.6014.04
Backward-pass share-0.320.18
Deep-pass share+0.430.33
Completion-0.360.76
Seconds between passes+0.913.88
Side used
left 26% · central 47% · right 27%
Progression (middle third) 1,595 passes
riskier
Width of use+0.0125.58
Wide-channel share+0.080.41
Side bias-0.22-0.83
Pass length+0.2417.55
Long-ball share+0.350.11
Forward gain per pass-0.223.27
Backward-pass share+0.440.35
Deep-pass share+0.330.18
Completion-0.870.79
Seconds between passes+0.693.50
Side used
left 33% · central 29% · right 37%
Final third 630 passes
more direct, riskier, trying to break lines
Width of use+0.0931.82
Wide-channel share+0.210.60
Side bias-0.14-1.45
Pass length+1.2517.41
Long-ball share+2.110.19
Forward gain per pass+1.102.50
Backward-pass share-0.950.32
Deep-pass share+1.410.14
Completion-1.830.55
Seconds between passes+1.134.25
Side used
left 39% · central 19% · right 42%
Reference measured across roughly 3,500 match-teams
in this competition, per third, so the comparison is with Leeds's own league. The means
behave as football should: width of use grows 21.0 → 25.6 → 31.6 going forward,
pass length shrinks 22.7 → 17.2 → 15.5 m, and completion is hardest in the
final third. Tempo is integer-resolution because the feed records whole seconds.
Transitions1 section
Transitions — attacking & defensive
The question
When Leeds win or lose the ball, what actually happens in the seconds after?
Reconstructed from the full timestamped event stream: real possession spells (not zone-clustered), and shots that follow a regain within a fixed window. This phase of play needs ordered, per-event timestamps — which every match here carries.
Direct play share19%
#3 of 20 · reaches final third in ≤3 passes
Patient build-up share22%
#18 of 20 · spells of 6+ passes
Tempo (possession length)16.0s
#5 of 20 · shorter = faster tempo
Passes per possession4.0
#17 of 20
Counter-attack shots /game0.67
#17 of 20 · shot within 12s of a regain in own half
Press→shot /game3.91
#1 of 20 · shot within 8s of a high regain
Speed to final third after regain11.2s
#2 of 20 · lower = faster break
Sequence success — attacking transition
9attacking transition sequences this season
11%succeed — produce a shot
56%turned over with nothing to show
Sequence success — defensive transition
46defensive transition sequences this season
59%succeed — reach the next third or a shot
15%turned over with nothing to show
Vision AI Model Insight
Leeds are a direct, fast-break side: 19% of possessions reach the final third in 3 passes or fewer (#3 of 20), against 22% built patiently over 6+ passes (#18 of 20). Winning the ball back leads to a shot inside 12 seconds 0.67 times a game (#17), and a high regain converts to a shot within 8 seconds 3.91 times a game (#1 of 20) — a genuine transition weapon.
Defending the box2 sections
Vulnerability Map — how opponents hurt them
The report flipped: where do opponents create their threat against Leeds? 319 opponent progressive passes and 114 shots faced (9.9 xG against) across the season.
Opponent threat by lane (attacking into Leeds)
Right wing
29%
Right half-space
12%
Central
17%
Left half-space
12%
Left wing
31%
Where the danger came from. Opponents generated most of their threat through the left wing (31% of the danger conceded).
How Do They Concede?
The question
Where do the goals against Leeds actually come from — open play, dead balls, and who supplies them?
Every goal Leeds conceded across the loaded matches — the opponent's shots in Leeds's own matches — plus every individual opponent key pass faced, each drawn as its own real arrow (not zone-clustered) with the % share of each zone behind it.
Where the goals go in
Every opponent key pass against them (76 total)
By situation
Open play
5 · 83%
Set pieces
1 · 17%
Penalties
0 · 0%
Vision AI Model Insight
6 goals conceded. 83% come in open play; set pieces account for only 17%, so dead-ball defending is not the main issue. 17% are headers — aerial defending is not where the damage comes from. Their supply line runs mainly through the left wing of the attacking third (20% of key passes faced) — that is the zone to shut off.
Context
Shape and tactics
Set pieces, strengths and weaknesses, and who they resemble.
The tactical idea behind each phase of this team's play, written from the same numbers shown above — what they do in build-up, how they progress, where they create, and how they defend it.
How much of the goal threat is manufactured from dead balls — a route to goal that bypasses their open-play mechanism entirely.
Open play
5 g · 7.7 xG
Set pieces
3 g · 2.1 xG
Penalties
0 g
38% of goals come from set pieces (3 of 8), worth 2.1 xG from 19 shots. A genuine set-piece weapon — defend the box seriously.
Where do Leeds's corners go?
33 corners taken this season, split by which flag they were taken from — arrows from the flag, dots at the delivery point, shaded by % of that side's corners landing in each zone (same treatment as the concede map below). No swing-direction (inswinger/outswinger) data is recorded here, only a start and end point per pass, which cannot show how the ball curved — so this shows what is genuinely knowable: where it lands, and short vs actually delivered. Tap or hover a dot for who took it and the match; filter to one match below and the shading recomputes for just that game.
Right side (14)
Delivered into the box 100%Short 0%
Left side (19)
Delivered into the box 100%Short 0%
Where opponents attack Leeds from set pieces
15 shots from opponent corners against Leeds, 1 conceded, split by the flag the corner came from — arrows from the flag, dots where the shot was taken (gold = goal), shaded by the % of that flag's shots in each zone. Tap or hover a shot for who took it, the minute and the match; filter to one match and the shading recomputes for just that game.
Right flag (6)
Shot 6Goals 0
Left flag (9)
Shot 9Goals 1
Tactical Strengths & Weaknesses
Strengths (top-third vs league)
press intensity (PPDA) — 4.18 (70th pct)
xG conceded /game — 1.23 (65th pct)
Weaknesses (bottom-third vs league)
pass retention % — 74.38 (10th pct)
field tilt % — 43.39 (25th pct)
box entries /game — 11.00 (25th pct)
shots /game — 11.88 (25th pct)
progression /game — 38.38 (30th pct)
Style Archetype & Similar Teams
Team archetype: Direct / counter high-press. Their closest stylistic matches in the league (cosine similarity on style metrics) — sides that play a similar way.
Sunderland
80%
Hull City
71%
Coventry
61%
Crystal Palace
27%
Brentford
26%
Performance vs Opponent Styles
The question
Not just how Leeds results against a high press or a low block — how does their own play actually change?
Every match split into three tiers by how hard that specific opponent pressed (opponent PPDA — lower means a more aggressive press), matching Leeds's own passing behaviour to the result.
Results
opponent type
goals for/g
goals against/g
points/g
vs high-pressing opponents (2m)
1.00
1.00
1.00
vs mid-block opponents (2m)
1.00
1.00
1.50
vs low-block opponents (4m)
1.25
0.50
1.50
Behaviour — how Leeds actually play against each
opponent type
retention
directness (long balls)
prog. passes/g
xT/g
box entries/g
vs high-pressing opponents
67%
22%
28
7.3
9.5
vs mid-block opponents
73%
20%
36
9.1
10.0
vs low-block opponents
77%
16%
45
11.0
12.2
Vision AI Model Insight
Facing a high press, retention drops to 67% vs 77% against a low block (9pt lower under pressure), and directness (long-ball share) sits at 22% when pressed vs 16% against a low block — a real style switch: they go noticeably more direct when pressed rather than trying to play through it.
Scouting Summary — the team in brief
In brief. Leeds are territorially-dominated with a aggressive high-press.
Their attack creates 1.23 xG a game and they concede 1.23 a game.
[Event-data based — no tracking, so off-ball structure and marking scheme need video.]
For each of ten match numbers, the one match this season that departed furthest from Leeds’s own average, the eight biggest swings first. Bar right of centre means more than usual, left means less. It shows how much the way they play moves from game to game, and which opponent moved it.
Leeds
across 8 matches this season
PPDAat Brighton · 1-1 · 2026-09-055.7avg 4.2
Progressive passesvs Man Utd · 1-1 · 2026-10-1859avg 38
Every pass, carry, take-on, shot and ball win is worth the change it made to the chance of scoring minus the chance of conceding in the next 10 seconds. This is Leeds’s last 8 matches added up: where the value came from, who produced it, and the six biggest single moves. OBV only measures what a player does with the ball: it shows who added the most on-ball value, not who was the most important player (defending off the ball, positioning and pressing are not in it).
+8.9on-ball value, 8 matches
Where the value came from
Passes3407 actions+1.34
Carries749 actions+0.07
Dribbles146 actions-0.07
Shots95 actions+5.35
Ball wins431 actions+2.21
Most on-ball value (OBV)
Actions
OBV
per 100
Dominic Calvert-Lewin
193
+1.59
+0.82
James Justin
418
+1.13
+0.27
Nico Elvedi
417
+1.07
+0.26
Jayden Bogle
345
+0.90
+0.26
James Trafford
481
+0.83
+0.17
Tarik Muharemovic
598
+0.65
+0.11
Anton Stach
364
+0.49
+0.14
Ao Tanaka
324
+0.43
+0.13
Biggest single moves
OBV
1 Dominic Calvert-Lewin · shot
+0.638
2 Nico Elvedi · pass
+0.469
3 Dominic Calvert-Lewin · shot
+0.356
4 Dominic Calvert-Lewin · shot
+0.352
5 James Justin · pass
+0.336
6 Ao Tanaka · shot
+0.320
Set Pieces by Player
Who delivers Leeds’s dead balls and who finishes them this season. 33 corners taken, most by Anton Stach (26); 19 shots from corners and free kicks, 3 goals, led by Jayden Bogle (1 goals, 0.95 xG). “Into the box” counts corners that landed inside the penalty area; free-kick takers are the players who shot from one, because this feed does not mark who delivers the others.
Player
Corners taken
Into the box
Set-piece shots
Goals
xG
Free-kick shots
Penalties
Anton Stach
26
26 (100%)
4
1
0.09
4
—
Jayden Bogle
—
—
3
1
0.95
—
—
Tarik Muharemovic
—
—
3
—
0.23
—
—
Harry Wilson
7
6 (86%)
—
—
0.00
—
—
Dominic Calvert-Lewin
—
—
2
1
0.30
—
—
Ao Tanaka
—
—
2
—
0.27
—
—
Noah Okafor
—
—
2
—
0.08
—
—
Daniel James
—
—
1
—
0.03
—
—
James Justin
—
—
1
—
0.05
—
—
Nico Elvedi
—
—
1
—
0.12
—
—
Top Performers
Ranked by Vision AI Score — the same tactical behavioural evaluation each player's own report page shows (min 216 minutes). Coloured chips are that player's strongest capability blocks.
Chance creation leaders — key passes /90 and box entries.
player
pos
min
key P /90
box entries
xT
Ethan Ampadu
M
752
1.08
18
12.0
Anton Stach
M
680
1.32
13
5.7
Jayden Bogle
M
643
0.84
10
5.9
James Justin
D
735
0.73
8
6.2
Harry Wilson
M
248
1.81
9
2.9
Noah Okafor
M
478
0.56
9
2.9
Gabriel Gudmundsson
M
542
0.50
6
4.5
Tarik Muharemovic
D
752
0.24
3
8.8
Wide Delivery Leaders
Crosses and cutbacks — wingers and full-backs only (by real average position, not the raw D/M position code), ranked by combined volume.
player
pos
min
crosses (completed)
cutbacks (completed)
Noah Okafor
W
478
5 (40%)
5 (40%)
Harry Wilson
W
248
3 (67%)
1 (0%)
Daniel James
W
65
2 (50%)
1 (0%)
Defensive Volume Leaders
Who does the defensive work — tackles, interceptions, recoveries.
player
pos
min
def actions
tackles
intercept
recoveries
Tarik Muharemovic
D
752
175
5
13
23
James Justin
D
735
141
21
9
26
Ethan Ampadu
M
752
133
17
15
35
Anton Stach
M
680
131
16
11
27
Dominic Calvert-Lewin
F
701
123
1
1
16
Jayden Bogle
M
643
105
9
3
26
Nico Elvedi
D
520
87
3
5
18
Ao Tanaka
M
496
78
15
3
19
Full Squad Data
Every player with 100+ minutes (16 players).
player
pos
apps
min
goals
xG
key P
prog
def
xT
Ethan Ampadu
M
8
752
0
0.1
9
45
133
12.0
Tarik Muharemovic
D
8
752
0
0.7
2
28
175
8.8
James Trafford
G
8
752
0
0.0
0
61
30
13.4
James Justin
D
8
735
1
0.4
6
31
141
6.2
Dominic Calvert-Lewin
F
8
701
3
2.7
0
5
123
1.1
Anton Stach
M
8
680
1
0.8
10
17
131
5.7
Jayden Bogle
M
8
643
2
2.1
6
31
105
5.9
Gabriel Gudmundsson
M
6
542
0
0.1
3
16
66
4.5
Nico Elvedi
D
6
520
0
0.3
2
14
87
4.8
Ao Tanaka
M
7
496
0
0.7
0
17
78
3.5
Noah Okafor
M
8
478
1
0.4
3
10
53
2.9
Brenden Aaronson
M
7
297
0
0.0
1
2
60
0.7
Jaka Bijol
D
5
278
0
0.2
0
7
71
2.0
Harry Wilson
M
6
248
0
0.0
5
12
34
2.9
Lukas Nmecha
F
7
132
0
0.5
1
1
17
0.6
Joe Rodon
D
2
126
0
0.1
1
5
23
0.9
Player Role Architecture
The question
Who does what — and what tactical job does each key player actually perform?
The nine most-used players (450+ minutes), each with the role their event fingerprint points to — not just their listed position.
Tarik Muharemovic
Stopper / marker · Defender
minutes752
goals · xG0 · 0.7
prog passes /903.4
key passes /900.24
def actions /9020.9
pass accuracy85%
James Trafford
Goalkeeper · Goalkeeper
minutes752
goals · xG0 · 0.0
prog passes /907.3
key passes /900.00
def actions /903.6
pass accuracy67%
Ethan Ampadu
Ball-winner / destroyer · Midfielder
minutes752
goals · xG0 · 0.1
prog passes /905.4
key passes /901.08
def actions /9015.9
pass accuracy78%
James Justin
Stopper / marker · Defender
minutes735
goals · xG1 · 0.4
prog passes /903.8
key passes /900.73
def actions /9017.3
pass accuracy71%
Dominic Calvert-Lewin
Ball-winner / destroyer · Midfielder
minutes701
goals · xG3 · 2.7
prog passes /900.6
key passes /900.00
def actions /9015.8
pass accuracy54%
Anton Stach
Ball-winner / destroyer · Midfielder
minutes680
goals · xG1 · 0.8
prog passes /902.2
key passes /901.32
def actions /9017.3
pass accuracy63%
Jayden Bogle
Ball-winner / destroyer · Midfielder
minutes643
goals · xG2 · 2.1
prog passes /904.3
key passes /900.84
def actions /9014.7
pass accuracy69%
Gabriel Gudmundsson
Ball-winner / destroyer · Midfielder
minutes542
goals · xG0 · 0.1
prog passes /902.7
key passes /900.50
def actions /9011.0
pass accuracy64%
Nico Elvedi
Stopper / marker · Defender
minutes520
goals · xG0 · 0.3
prog passes /902.4
key passes /900.35
def actions /9015.1
pass accuracy90%
Recruitment Intelligence — profiles to sign
The question
If you were sporting director, which player profiles would move the needle most?
Each recruitment target is derived directly from the team's weakest areas vs the league — the gap, why it shows in the data, and the profile that closes it.
Need — bottom-third: pass retention % (10th pct)
Sign a secure ball-retaining midfielder. The data flags this because possession is turned over cheaply (74.38, 10th percentile vs the league).
Need — bottom-third: field tilt % (25th pct)
Sign a possession-controlling midfielder to win the territorial battle. The data flags this because they get pinned back (43.39, 25th percentile vs the league).
Need — bottom-third: box entries /game (25th pct)
Sign a penetrative wide player or runner to create box entries. The data flags this because few entries into the box (11.00, 25th percentile vs the league).