Combining every season — this one is larger, give it a moment.
Team reportPremier League 2026
Aston Villa
A territorially-dominated side, aggressive high-press out of possession, with a moderate attack. Currently #9 of 20 on 11 points (3-2-3, 9:12). 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 Aston Villa and how good was the season?
Read. Aston Villa currently sit #9 of 20 on 11 points (3W-2D-3L, 1.38 pts/game), scoring 9 and conceding 12 for a goal difference of -3.
On the ball they are a territorially-dominated team (40% field tilt, #17 in the league); off it they defend with a aggressive high-press (#17 of 20 for press intensity).
Across the 8 matches of event data, they create 1.04 xG/game (#18) and concede 1.45 (#17), a net of -0.42 xG/game — the underlying performance matches the actual goal difference.
Season Context — the league table
The question
Where did Aston Villa actually finish, and against whom?
The authoritative final table (full season), centred on Aston Villa.
#
team
P
W
D
L
GF
GA
GD
Pts
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
11
Everton
8
2
4
2
8
9
-1
10
12
Ipswich
8
3
1
4
12
16
-4
10
Match Momentum — when they create and concede danger
The question
Across a whole game, which periods does Aston Villa 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
Aston Villa tend to fade after a strong start — first-half net xG -0.66, second-half -2.67. Their strongest period is 0-15' (net +1.13 xG); their most dangerous period to face is 76-90+' (net -1.49 xG against them).
xG Race — the season, match by match
Expected goals piling up over 8 matches: Aston Villa 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 (-3.3 xG). Hover a point for that match.
Aston VillaOpponents against Aston Villa
Tactical DNA
The football question
In five genes, what is Aston Villa'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.
35
Build-Up
possession control from the back
12
Progression
moving the ball upfield with threat
17
Creation
manufacturing chances
15
Press
winning the ball high & hard
15
Solidity
resisting the opponent's threat
Genetic identity
Aston Villa are defined most by their Build-Up DNA (35/100 · limited at possession control from the back), and least by their Progression DNA (12/100). 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
78%
Possession
61%
Field tilt
52%
Disrupt
Central progression ~proxy
70%
Dribble tendency
43%
Progressive passes
39%
Finish
Final-third patience
43%
Shot quality
57%
Open-play xG
57%
Press
Forward press ~proxy
17%
High regains
22%
Pressing efficiency ~proxy
30%
Defend
Def-mid press ~proxy
22%
High line ~proxy
17%
PAdj defensive actions
26%
Aston Villa is defined most by Possess (64% in the league) and weakest on Defend (22%). 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 Aston Villa actually control, relative to the rest of the league?
Field tilt = share of final-third touches between the two sides across Aston Villa'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; Aston Villa highlighted. Hover any dot for the team.
Vision AI Model Insight
Aston Villa post 40% field tilt, ranking #17 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 Aston Villa has the ball, how much of that time is spent building, progressing and creating?
The share of Aston Villa'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
31.6%
Progression
45.7%
Chance creation
20.9%
Attacking transition
1.8%
Vision AI Model Insight
Progression is where Aston Villa spends most of its time on the ball (46% 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
Lindelöf ↔ Suzuki
112p · -0.2xT
Lindelöf ↔ Cash
104p · -1.6xT
Lindelöf ↔ Kamara
79p · -0.5xT
Barkley ↔ Kamara
75p · 0.3xT
Mings ↔ Suzuki
64p · -0.4xT
Cash ↔ Kamara
63p · 0.0xT
Lindelöf ↔ Mings
51p · -0.1xT
Torres ↔ Suzuki
51p · -0.3xT
McGinn ↔ Cash
47p · 1.6xT
Lindelöf ↔ Torres
45p · -0.4xT
Vision AI Model Insight
Busiest connection: Victor Lindelöf ↔ Zion Suzuki (112 passes). Most dangerous per pass: John McGinn ↔ Aaron Wan-Bissaka (0.04 xT/pass over 19 passes) — the combination that hurts opponents most when it fires, not just the one used most.
Touch Density — where Aston Villa 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 Aston Villa 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
3401
ACTIONS
Carries
the ball moved with a player
861
ACTIONS
Vision AI Model Insight
Their single busiest zone is the centre of the defensive third (13.5% of all actions). 65% of actions run through the inside lanes (centre + both half-spaces) versus 35% down the two wings — an inside-dominant, half-space build. By third: 34% defensive, 46% middle, 20% 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. Aston Villa leaned on their right (37%). The bars under the pitch put the league’s share beside theirs.
Aston Villa
37%27%37%
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. Aston Villa made 285 this season (35.6 a match); 27% 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
75
9.4
26%
31%
Left half-space
46
5.8
16%
16%
Centre
43
5.4
15%
13%
Right half-space
43
5.4
15%
14%
Right wing
78
9.8
27%
27%
Build-up2 sections
Build-Up — the pass map
The football question
How does Aston Villa 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.
1175build-up passes attempted
77%completed
26%reach the middle third
2%go long into the final third
Pass types
440CB · 49%
73Long Wide · 8%
5Long Central · 1%
283Wide → FB · 31%
104Central · 11%
Sequence success — build-up
118build-up sequences this season
7%succeed — reach the next third or a shot
49%turned over with nothing to show
CB440 · 49%
Long Wide73 · 8%
Long Central5 · 1%
Wide → FB283 · 31%
Central104 · 11%
◆ Vision AI Insight
Aston Villa build predominantly down the right, with Suzuki as the fulcrum (219 build-up passes). Only 29% 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 LHS def → C def (71×).
Pass Explorer
Every pass of Aston Villa’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. 291 progressive passes across 8 matches (36.4/game).
Origin lane
Right wing
26%
Right half-space
12%
Central
23%
Left half-space
16%
Left wing
23%
Progression is left-dominant: 38% right side · 23% central · 39% left.
Who generates it
John McGinn
32 · 11%
Zion Suzuki
32 · 11%
Victor Lindelöf
26 · 9%
Matty Cash
25 · 9%
Aaron Wan-Bissaka
22 · 8%
Top two — John McGinn & Zion Suzuki — account for 22% of all progression.
PROGRESSION
36.4 prog passes/game
mostly left
→
PENETRATION
32% reach final third
16% reach the box
→
THREAT
the other 68% is recycled
progress ≠ danger
What came of it.32% of progressive passes reached the final third and 16% the box; the rest was recycled in front of the block. 22% of the progression ran through two players, mostly down the left 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 wingbalanced
Right half-spacesterile
Centralbalanced
Left half-spacebalanced
Left wingbalanced
Progression — the pass map
The football question
Once Aston Villa 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.
1507progression passes attempted
86%completed
16%break into the final third
13%go backward into the defensive third
Pass types
532CB · 41%
41Long Wide · 3%
7Long Central · 1%
516Wide → FB · 40%
195Central · 15%
Sequence success — progression
152progression sequences this season
22%succeed — reach the next third or a shot
37%turned over with nothing to show
CB532 · 41%
Long Wide41 · 3%
Long Central7 · 1%
Wide → FB516 · 40%
Central195 · 15%
◆ Vision AI Insight
Aston Villa progress predominantly down the right, with Kamara the busiest midfield outlet (193 progression passes). Only 16% of passes that start in midfield actually break the final-third line — the rest (71%) is recycled back into midfield or (13%) played backward, the hallmark of a side that carries progression itself through midfield rather than just passing through it. Their most-repeated pattern is RHS mid → C mid (71×) — 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)
13%
Progressive
1%
Neutral / sideways
47%
Safe / backward
38%
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 Aston Villa actually progress the ball with their feet, not a pass?
206 progressive carries this season across the whole squad (24% of 861 tracked carries total), grouped into up to 4 spatial patterns by where each carry starts and ends together — not just its origin zone. Median 16m, longest 58m. Most active carrier: Lindelöf (44). Filter to one player below.
First (67)Second (50)Third (45)Fourth (44)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 Aston Villa'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.
91shots
8.3xG
0.09xG per shot
3big chances (0.3+ xG)
9goals
#14key passes / game in the league
#16box entries / game in the league
Kind of attack, then how the shot was set up
Shots
Goals
xG
xG / shot
Attacking transition29% of shots
26
1
2.63
0.10
Cross
2
1
0.19
0.09
Cut-back
5
0
0.51
0.10
Long ball
1
0
0.06
0.06
Pass into the box / short pass
14
0
1.56
0.11
Own creation (carry, dribble, rebound)
4
0
0.30
0.08
Settled attack59% of shots
54
7
5.17
0.10
Cross
5
1
0.98
0.20
Cut-back
6
2
0.56
0.09
Long ball
3
0
0.16
0.05
Pass into the box / short pass
25
4
1.67
0.07
Own creation (carry, dribble, rebound)
15
0
1.79
0.12
Set piece12% of shots
11
1
0.50
0.05
Corner
8
1
0.41
0.05
Free kick
3
0
0.09
0.03
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
John McGinn
11
1.38
4
Nicolas Jackson
6
0.85
0
Johan Manzambi
7
0.54
2
Emiliano Buendía
7
0.53
0
Ross Barkley
8
0.52
0
Who takes them
Player
Shots
xG
Goals
Nicolas Jackson
19
1.78
4
Johan Manzambi
15
1.59
2
Emiliano Buendía
14
1.06
1
John McGinn
7
0.91
1
Victor Lindelöf
1
0.65
0
Vision AI Model Insight
The biggest source of Aston Villa's chances, by xG, is settled attack (5.2 of 8.3 xG, 54 of 91 shots), mostly own creation (carry, dribble, rebound). 26 of the 91 shots (29%) came within 15 seconds of winning the ball. 11 of the 91 shots came from set pieces; 9 goals from 8.3 xG (+0.7).
Chance Creation — the pass map
The football question
How does Aston Villa 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
8CB · 12%
5Long Wide · 8%
1Long Central · 2%
28Wide → FB · 42%
24Central · 36%
Sequence success — final-third
92final-third sequences this season
16%succeed — produce a shot
28%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
CB8 · 12%
Long Wide5 · 8%
Long Central1 · 2%
Wide → FB28 · 42%
Central24 · 36%
Who creates — volume × threat
13McGinn
7Buendía
7Barkley
7Manzambi
6Jackson
4Gomes
4Mbaye
66key passes #14 of 20
10.4box entries/game #16 of 20
27.6final-3rd entries/game #16 of 20
1.04xG/game #18 of 20
9goals #11 of 20
+0.7finishing vs xG #7 of 20
◆ Vision AI Insight
Aston Villa lean left — 35% of their key passes come from that flank and its half-space (23 of 66), most often from Buendía (5). They funnel chances into the lhs of the final third, most often via C att → LHS att (9 key passes, most often from Barkley). McGinn is the primary source overall. They generate 1.04 xG a game and finish it clinical — they beat their xG, led by Nicolas Jackson (4G), Johan Manzambi (2G), Alysson (1G).
Crosses & Cutbacks — squad-wide
The question
How does Aston Villa deliver into the box from wide areas, and what comes of it?
77 crosses: 23 found a team-mate (30%), 8 led to a shot (10%). 25 cut-backs: 14 found a team-mate (56%), 9 led to a shot (36%). 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 Aston Villa actually cross from?
77 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. 29% come from one territory, the advanced right wing; 4 come from the half-spaces and 0 from the centre.
Territory
Crosses
Share
Led to a shot
advanced · right wing
22
29%
1
advanced · left wing
15
19%
5
byline · right wing
14
18%
1
byline · left wing
12
16%
1
deep · right wing
6
8%
0
deep · left wing
4
5%
0
advanced · right half-space
2
3%
0
advanced · left half-space
1
1%
0
byline · right half-space
1
1%
0
Finishing3 sections
Shot Map
91 shots for 8.3 xG (1.04/game, #18 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.
Moderate attack. 11.4 shots/game at 0.091 xG/shot. Box entries 10/game (#16). Threat created 9.3 xT/game (#17).
Goal Sequences
The move behind each of Aston Villa’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 Aston Villa scored across the loaded matches, broken down by how it was created and finished.
By situation
Open play
8 · 89%
Set pieces
1 · 11%
Penalties
0 · 0%
By finish
Right foot
6 · 67%
Left foot
1 · 11%
Header
2 · 22%
Vision AI Model Insight
9 goals scored. 89% come in open play; set pieces add 11%. Open play is clearly their main route to goal.
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
164
Failed forward pass
111
Failed take-on
95
Lost in the defensive third
270
Their weakest zone is the left wing in the defensive third: 41% of 145 pass attempts there are lost (60 times). Separately, 25% 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 Aston Villa 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.
9
Pressing Index /100 · passive
Intensity5
PPDA, high-press share, high-zone actions
Height0
how high up the pitch they defend
Effectiveness0
high recoveries & high-regain share
Disruption38
opponent completion & progression allowed
Every defensive action & where it happens
Where they win the ball back — by lane
RW
15%
RHS
18%
C
21%
LHS
26%
LW
20%
By third (high / mid / low regain)
Def 54%
Mid 33%
Att 13%
Read
Ranked #17 of 20 for press intensity, this is a high press. Note the tension: 54% of their raw defensive actions still occur in their own third — a possession effect, not a passive one. Dominating the ball (19% high-press share) means that when they do defend, it is often deep. Of recoveries, 44% are won high up the pitch.
Every pressing metric, ranked in the league
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#17/20
lower = more intense; rank is what matters
Defensive actions / game
153
#14/20
High-press share
19%
#20/20
def actions won high up
Defensive-action height (0–100)
35.5
#20/20
avg pitch height of actions
High recoveries / game
14.6
#20/20
ball won in mid+att third
High-recovery share
44%
#20/20
share of recoveries won high
Opponent completion allowed
83%
#17/20
lower = more disruptive
Opponent progressive allowed
8.5%
#8/20
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 Aston Villa win it back with a defensive action?
Counted over the 211 opposition possessions that Aston Villa 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 Aston Villa step in. Aston Villa win it after 4.8 opponent passes in the first half-hour and after 5.4 after the hour mark (later).
PASSES THEY GET
5.0passes
median 3 · before Aston Villa win it
TIME THEY KEEP IT
15sec
median 8s · 26.4 wins per game
How many passes the opponent got in before the ball was won
42%
24%
18%
16%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
5.3
17s
15–30′
3.8
11s
30–45+′
3.4
10s
45–60′
5.4
15s
60–75′
5.8
17s
75–90+′
5.2
16s
average passesaverage seconds
Attitude in each third
Every other style number here says how much Aston Villa 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 Aston Villa 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) 1,175 passes
nothing unusual in this zone
Width of use-0.4919.99
Wide-channel share-0.340.28
Side bias-0.25-0.78
Pass length+0.5124.55
Long-ball share+0.310.26
Forward gain per pass+0.4413.36
Backward-pass share+0.360.22
Deep-pass share+0.510.34
Completion-0.370.76
Seconds between passes+0.703.75
Side used
left 25% · central 48% · right 27%
Progression (middle third) 1,507 passes
narrow, central
Width of use-0.9623.53
Wide-channel share-0.740.35
Side bias-0.42-1.59
Pass length-0.0617.14
Long-ball share-0.330.09
Forward gain per pass-0.472.90
Backward-pass share+0.420.35
Deep-pass share+0.120.17
Completion+0.020.84
Seconds between passes+0.693.50
Side used
left 30% · central 35% · right 35%
Final third 719 passes
nothing unusual in this zone
Width of use-0.0831.42
Wide-channel share-0.100.58
Side bias+0.674.28
Pass length-0.3114.98
Long-ball share-0.620.07
Forward gain per pass-0.520.35
Backward-pass share+0.570.42
Deep-pass share+0.120.09
Completion+0.720.76
Seconds between passes+0.303.62
Side used
left 46% · central 17% · right 36%
Reference measured across roughly 3,500 match-teams
in this competition, per third, so the comparison is with Aston Villa'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 Aston Villa 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 share16%
#16 of 20 · reaches final third in ≤3 passes
Patient build-up share30%
#5 of 20 · spells of 6+ passes
Tempo (possession length)21.0s
#17 of 20 · shorter = faster tempo
Passes per possession4.9
#6 of 20
Counter-attack shots /game1.20
#6 of 20 · shot within 12s of a regain in own half
Press→shot /game2.57
#17 of 20 · shot within 8s of a high regain
Speed to final third after regain14.4s
#19 of 20 · lower = faster break
Sequence success — attacking transition
10attacking transition sequences this season
20%succeed — produce a shot
60%turned over with nothing to show
Sequence success — defensive transition
30defensive transition sequences this season
67%succeed — reach the next third or a shot
10%turned over with nothing to show
Vision AI Model Insight
Aston Villa are a patient build-up side: 16% of possessions reach the final third in 3 passes or fewer (#16 of 20), against 30% built patiently over 6+ passes (#5 of 20). Winning the ball back leads to a shot inside 12 seconds 1.20 times a game (#6), and a high regain converts to a shot within 8 seconds 2.57 times a game (#17 of 20) — not yet a major source of chances.
Defending the box2 sections
Vulnerability Map — how opponents hurt them
The report flipped: where do opponents create their threat against Aston Villa? 338 opponent progressive passes and 126 shots faced (11.6 xG against) across the season.
Opponent threat by lane (attacking into Aston Villa)
Right wing
30%
Right half-space
13%
Central
13%
Left half-space
12%
Left wing
32%
Where the danger came from. Opponents generated most of their threat through the left wing (32% of the danger conceded).
How Do They Concede?
The question
Where do the goals against Aston Villa actually come from — open play, dead balls, and who supplies them?
Every goal Aston Villa conceded across the loaded matches — the opponent's shots in Aston Villa'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 (88 total)
By situation
Open play
11 · 100%
Set pieces
0 · 0%
Penalties
0 · 0%
Vision AI Model Insight
11 goals conceded. 100% come in open play; set pieces account for only 0%, so dead-ball defending is not the main issue. 27% are headers — aerial defending is not where the damage comes from. Their supply line runs mainly through the left half-space of the attacking third (25% 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
8 g · 7.8 xG
Set pieces
1 g · 0.5 xG
Penalties
0 g
11% of goals come from set pieces (1 of 9), worth 0.5 xG from 11 shots. Set pieces are a secondary threat; the danger is in open play.
Where do Aston Villa's corners go?
26 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 (12)
Delivered into the box 100%Short 0%
Left side (14)
Delivered into the box 93%Short 7%
Where opponents attack Aston Villa from set pieces
18 shots from opponent corners against Aston Villa, 0 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 (9)
Shot 9Goals 0
Left flag (9)
Shot 9Goals 0
Tactical Strengths & Weaknesses
Strengths (top-third vs league)
none stand out
Weaknesses (bottom-third vs league)
xG created /game — 1.04 (10th pct)
progression /game — 36.38 (10th pct)
xG conceded /game — 1.45 (15th pct)
press intensity (PPDA) — 5.85 (15th pct)
field tilt % — 39.87 (15th 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.
Everton
89%
Ipswich
88%
Crystal Palace
79%
Coventry
75%
Chelsea
69%
Performance vs Opponent Styles
The question
Not just how Aston Villa 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 Aston Villa'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
3.00
0.50
vs mid-block opponents (2m)
2.00
1.00
3.00
vs low-block opponents (4m)
0.75
1.00
1.00
Behaviour — how Aston Villa actually play against each
opponent type
retention
directness (long balls)
prog. passes/g
xT/g
box entries/g
vs high-pressing opponents
76%
17%
32
7.8
9.0
vs mid-block opponents
81%
15%
35
8.8
9.0
vs low-block opponents
81%
13%
40
10.3
11.8
Vision AI Model Insight
Facing a high press, retention drops to 76% vs 81% against a low block (5pt lower under pressure), and directness (long-ball share) sits at 17% when pressed vs 13% against a low block — a mild adjustment, not a wholesale change.
Scouting Summary — the team in brief
In brief. Aston Villa are territorially-dominated with a aggressive high-press.
Their attack creates 1.04 xG a game and they concede 1.45 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 Aston Villa’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.
Aston Villa
across 8 matches this season
Progressive carriesat Hull City · 0-0 · 2026-09-05102avg 42
Passesat Hull City · 0-0 · 2026-09-05730avg 425
Final-third entriesat Hull City · 0-0 · 2026-09-0569avg 36
Pass accuracyat Hull City · 0-0 · 2026-09-0588%avg 80%
PPDAat Brighton · 0-4 · 2026-08-239.7avg 5.9
Progressive passesat Hull City · 0-0 · 2026-09-0557avg 36
Field tiltat Hull City · 0-0 · 2026-09-0570%avg 40%
Threat from passingat Brighton · 0-4 · 2026-08-231.71avg 4.60
On-Ball Value — who moved the game
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 Aston Villa’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).
+9.8on-ball value, 8 matches
Where the value came from
Passes3401 actions+2.92
Carries861 actions+0.08
Dribbles155 actions+0.37
Shots91 actions+4.20
Ball wins400 actions+2.25
Most on-ball value (OBV)
Actions
OBV
per 100
Nicolas Jackson
212
+1.85
+0.87
Johan Manzambi
157
+1.57
+1.00
Victor Lindelöf
594
+1.38
+0.23
Emiliano Buendía
325
+1.12
+0.34
Boubacar Kamara
490
+0.97
+0.20
Zion Suzuki
387
+0.83
+0.21
Ian Maatsen
258
+0.56
+0.22
Ross Barkley
328
+0.55
+0.17
Biggest single moves
OBV
1 Victor Lindelöf · shot
+0.426
2 Johan Manzambi · shot
+0.382
3 Ibrahim Mbaye · take-on
+0.284
4 Nicolas Jackson · shot
+0.274
5 Nicolas Jackson · shot
+0.271
6 João Gomes · pass
+0.270
Set Pieces by Player
Who delivers Aston Villa’s dead balls and who finishes them this season. 26 corners taken, most by John McGinn (12); 11 shots from corners and free kicks, 1 goals, led by Alysson (1 goals, 0.13 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
John McGinn
12
10 (83%)
—
—
0.00
—
—
Emiliano Buendía
6
3 (50%)
1
—
0.03
1
—
Alysson
2
1 (50%)
2
1
0.13
—
—
Ross Barkley
1
1 (100%)
2
—
0.12
—
—
Johan Manzambi
—
—
2
—
0.06
2
—
Matty Cash
2
1 (50%)
1
—
0.02
—
—
Nicolas Jackson
—
—
1
—
0.04
—
—
Tyrone Mings
—
—
1
—
0.07
—
—
Ian Maatsen
—
—
1
—
0.04
—
—
Ibrahim Mbaye
2
1 (50%)
—
—
0.00
—
—
Alejandro Garnacho
1
0 (0%)
—
—
0.00
—
—
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
John McGinn
M
664
1.76
17
7.6
Nicolas Jackson
F
559
0.97
14
2.5
Johan Manzambi
M
326
1.93
10
3.4
Emiliano Buendía
M
581
1.08
6
3.5
Ross Barkley
M
385
1.64
5
4.3
Boubacar Kamara
M
752
0.36
6
7.0
Matty Cash
D
595
0.30
4
5.9
Victor Lindelöf
D
767
0.35
3
8.0
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)
Ian Maatsen
FB
444
1 (0%)
3 (67%)
Alysson
W
227
0
1 (0%)
Defensive Volume Leaders
Who does the defensive work — tackles, interceptions, recoveries.
player
pos
min
def actions
tackles
intercept
recoveries
Boubacar Kamara
M
752
144
21
6
31
Emiliano Buendía
M
581
126
21
5
30
Matty Cash
D
595
98
22
3
8
John McGinn
M
664
96
11
5
28
Victor Lindelöf
D
767
89
2
3
12
Tyrone Mings
D
477
73
4
1
13
João Gomes
M
430
73
13
6
12
Ian Maatsen
D
444
62
11
8
15
Full Squad Data
Every player with 100+ minutes (19 players).
player
pos
apps
min
goals
xG
key P
prog
def
xT
Victor Lindelöf
D
8
767
0
0.6
3
26
89
8.0
Boubacar Kamara
M
8
752
0
0.1
3
21
144
7.0
Zion Suzuki
G
7
672
0
0.0
0
32
28
8.8
John McGinn
M
8
664
1
0.9
13
32
96
7.6
Matty Cash
D
7
595
0
0.2
2
25
98
5.9
Emiliano Buendía
M
8
581
1
1.1
7
18
126
3.5
Nicolas Jackson
F
7
559
4
1.8
6
9
56
2.5
Tyrone Mings
D
6
477
0
0.3
1
18
73
4.1
Ian Maatsen
D
5
444
0
0.5
1
17
62
3.4
João Gomes
M
5
430
0
0.0
4
8
73
3.3
Ross Barkley
M
8
385
0
0.5
7
11
60
4.3
Johan Manzambi
M
5
326
2
1.6
7
8
42
3.4
George Hemmings
M
5
311
0
0.1
3
2
50
0.6
Pau Torres
D
5
310
0
0.0
0
15
42
2.9
Aaron Wan-Bissaka
D
6
299
0
0.0
2
22
46
3.9
Alysson
M
7
227
1
0.2
1
2
43
0.5
Matteo Ruggeri
D
4
176
0
0.0
2
10
42
1.7
Ibrahim Mbaye
F
5
143
0
0.3
4
5
23
1.3
Tammy Abraham
F
4
100
0
0.0
0
0
7
0.1
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.
Victor Lindelöf
Stopper / marker · Defender
minutes767
goals · xG0 · 0.6
prog passes /903.1
key passes /900.35
def actions /9010.4
pass accuracy90%
Boubacar Kamara
Ball-winner / destroyer · Midfielder
minutes752
goals · xG0 · 0.1
prog passes /902.5
key passes /900.36
def actions /9017.2
pass accuracy87%
Zion Suzuki
Goalkeeper · Goalkeeper
minutes672
goals · xG0 · 0.0
prog passes /904.3
key passes /900.00
def actions /903.8
pass accuracy69%
John McGinn
Ball-winner / destroyer · Midfielder
minutes664
goals · xG1 · 0.9
prog passes /904.3
key passes /901.76
def actions /9013.0
pass accuracy65%
Matty Cash
Stopper / marker · Defender
minutes595
goals · xG0 · 0.2
prog passes /903.8
key passes /900.30
def actions /9014.8
pass accuracy79%
Emiliano Buendía
Ball-winner / destroyer · Midfielder
minutes581
goals · xG1 · 1.1
prog passes /902.8
key passes /901.08
def actions /9019.5
pass accuracy72%
Nicolas Jackson
Ball-winner / destroyer · Midfielder
minutes559
goals · xG4 · 1.8
prog passes /901.4
key passes /900.97
def actions /909.0
pass accuracy75%
Tyrone Mings
Stopper / marker · Defender
minutes477
goals · xG0 · 0.3
prog passes /903.4
key passes /900.19
def actions /9013.8
pass accuracy87%
Ian Maatsen
Stopper / marker · Defender
minutes444
goals · xG0 · 0.5
prog passes /903.4
key passes /900.20
def actions /9012.6
pass accuracy76%
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: xG created /game (10th pct)
Sign a higher-volume goalscorer or chance creator. The data flags this because the attack under-produces xG (1.04, 10th percentile vs the league).
Need — bottom-third: progression /game (10th pct)
Sign a progressive passer / ball-carrier to break lines. The data flags this because progression is limited (36.38, 10th percentile vs the league).
Need — bottom-third: xG conceded /game (15th pct)
Sign a defensive organiser / shot-suppressing centre-back. The data flags this because too much xG is conceded (1.45, 15th percentile vs the league).