A territorially-dominated side, aggressive high-press out of possession, with a low-output attack. Finished #20 of 20 on 20 points (3-11-24, 27:68). Tactical profile below is built from 38 matches of ball-by-ball event data, contextualised against the league.
In one read, what kind of team is Wolves and how good was the season?
Read. Wolves finished #20 of 20 on 20 points (3W-11D-24L, 0.53 pts/game), scoring 27 and conceding 68 for a goal difference of -41.
On the ball they are a territorially-dominated team (39% field tilt, #21 in the league); off it they defend with a aggressive high-press (#22 of 23 for press intensity).
Across the 38 matches of event data, they create 0.93 xG/game (#22) and concede 1.48 (#20), a net of -0.55 xG/game — the underlying performance diverges from the actual goal difference.
Season Context — the league table
The question
Where did Wolves actually finish, and against whom?
The authoritative final table (full season), centred on Wolves.
#
team
P
W
D
L
GF
GA
GD
Pts
17
Tottenham
38
10
11
17
48
57
-9
41
18
West Ham
38
10
9
19
46
65
-19
39
19
Burnley
38
4
10
24
38
75
-37
22
20
Wolves
38
3
11
24
27
68
-41
20
Match Momentum — when they create and concede danger
The question
Across a whole game, which periods does Wolves 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
Wolves tend to come alive after the break — first-half net xG -13.42, second-half -7.31. Their strongest period is 61-75' (net -1.45 xG); their most dangerous period to face is 0-15' (net -5.47 xG against them).
xG Race — the season, match by match
Expected goals piling up over 38 matches: Wolves 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 (-20.7 xG). Hover a point for that match.
WolvesOpponents against Wolves
Tactical DNA
The football question
In five genes, what is Wolves'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.
22
Build-Up
possession control from the back
20
Progression
moving the ball upfield with threat
13
Creation
manufacturing chances
7
Press
winning the ball high & hard
13
Solidity
resisting the opponent's threat
Genetic identity
Wolves are defined most by their Build-Up DNA (22/100 · limited at possession control from the back), and least by their Press DNA (7/100). Percentiles vs the 23 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
17%
Possession
17%
Field tilt
9%
Disrupt
Central progression ~proxy
4%
Dribble tendency
83%
Progressive passes
22%
Finish
Final-third patience
57%
Shot quality
9%
Open-play xG
0%
Press
Forward press ~proxy
4%
High regains
4%
Pressing efficiency ~proxy
0%
Defend
Def-mid press ~proxy
4%
High line ~proxy
9%
PAdj defensive actions
17%
Wolves is defined most by Disrupt (36% in the league) and weakest on Press (3%). 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 Wolves actually control, relative to the rest of the league?
Field tilt = share of final-third touches between the two sides across Wolves'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; Wolves highlighted. Hover any dot for the team.
Vision AI Model Insight
Wolves post 39% field tilt, ranking #21 of 23 in the league — below the league mid-point, spending more of the game defending their own territory than dictating it, and their 0.53 points/game sits below the league-average return — territory is genuinely converting into results.
Where the time goes — how long each phase lasts
The question
When Wolves has the ball, how much of that time is spent building, progressing and creating?
The share of Wolves'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
28.3%
Progression
48.8%
Chance creation
20.9%
Attacking transition
2.0%
Vision AI Model Insight
Progression is where Wolves spends most of its time on the ball (49% 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
André ↔ Gomes
364p · 10.1xT
Krejcí ↔ Bueno
339p · 3.5xT
Krejcí ↔ Bueno
338p · 15.6xT
Gomes ↔ Bueno
294p · 12.6xT
André ↔ Krejcí
249p · 6.7xT
Mosquera ↔ Bueno
214p · 4.5xT
Gomes ↔ Krejcí
200p · 9.3xT
André ↔ Bueno
178p · 3.8xT
Sá ↔ Krejcí
178p · 5.2xT
Gomes ↔ Tchatchoua
159p · 8.8xT
Vision AI Model Insight
Busiest connection: André ↔ João Gomes (364 passes). Most dangerous per pass: Hugo Bueno ↔ Mateus Mané (0.08 xT/pass over 106 passes) — the combination that hurts opponents most when it fires, not just the one used most.
Touch Density — where Wolves had the ball
Every touch across 460 matches. Brighter means more touches.
Which Zones Do They Attack Through?
The question
Across the whole pitch, where does Wolves 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
15393
ACTIONS
Carries
the ball moved with a player
3518
ACTIONS
Vision AI Model Insight
Their single busiest zone is the centre of the defensive third (11.8% of all actions). 61% of actions run through the inside lanes (centre + both half-spaces) versus 39% down the two wings — an inside-dominant, half-space build. By third: 32% defensive, 48% 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. Wolves leaned on their left (41%). The bars under the pitch put the league’s share beside theirs.
Wolves
41%22%37%
League
39%23%38%
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. Wolves made 1301 this season (34.2 a match); 30% came into the left 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
386
10.2
30%
29%
Left half-space
201
5.3
15%
16%
Centre
154
4.1
12%
12%
Right half-space
204
5.4
16%
14%
Right wing
356
9.4
27%
29%
Build-up2 sections
Build-Up — the pass map
The football question
How does Wolves 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.
5050build-up passes attempted
75%completed
28%reach the middle third
3%go long into the final third
Pass types
1828CB · 48%
314Long Wide · 8%
51Long Central · 1%
1304Wide → FB · 34%
296Central · 8%
Sequence success — build-up
1111build-up sequences this season
8%succeed — reach the next third or a shot
45%turned over with nothing to show
CB1828 · 48%
Long Wide314 · 8%
Long Central51 · 1%
Wide → FB1304 · 34%
Central296 · 8%
◆ Vision AI Insight
Wolves build predominantly down the left, with Sá as the fulcrum (510 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 → LHS def (286×).
Pass Explorer
Every pass of Wolves’s last 10 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. 1439 progressive passes across 38 matches (37.9/game).
Origin lane
Right wing
28%
Right half-space
15%
Central
18%
Left half-space
14%
Left wing
26%
Progression is right-dominant: 43% right side · 18% central · 39% left.
Who generates it
Hugo Bueno
126 · 9%
José Sá
125 · 9%
Yerson Mosquera
119 · 8%
Ladislav Krejcí
112 · 8%
Sam Johnstone
104 · 7%
Top two — Hugo Bueno & José Sá — account for 17% of all progression.
PROGRESSION
37.9 prog passes/game
mostly right
→
PENETRATION
31% reach final third
24% reach the box
→
THREAT
the other 69% is recycled
progress ≠ danger
What came of it.31% of progressive passes reached the final third and 24% the box; the rest was recycled in front of the block. 17% 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-spacebalanced
Centralbalanced
Left half-spacebalanced
Left wingbalanced
Progression — the pass map
The football question
Once Wolves 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.
7310progression passes attempted
83%completed
15%break into the final third
13%go backward into the defensive third
Pass types
2222CB · 37%
189Long Wide · 3%
39Long Central · 1%
2872Wide → FB · 47%
763Central · 13%
Sequence success — progression
1772progression sequences this season
25%succeed — reach the next third or a shot
43%turned over with nothing to show
CB2222 · 37%
Long Wide189 · 3%
Long Central39 · 1%
Wide → FB2872 · 47%
Central763 · 13%
◆ Vision AI Insight
Wolves progress predominantly down the left, with Gomes the busiest midfield outlet (821 progression passes). Only 15% of passes that start in midfield actually break the final-third line — the rest (72%) 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 LHS mid → LW mid (337×) — 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
49%
Safe / backward
37%
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 Wolves actually progress the ball with their feet, not a pass?
824 progressive carries this season across the whole squad (24% of 3441 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 54m. Most active carrier: Bueno (112). Filter to one player below.
First (253)Second (208)Third (197)Fourth (166)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 Wolves'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.
370shots
35.5xG
0.10xG per shot
21big chances (0.3+ xG)
26goals
#21key 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 transition19% of shots
72
6
5.82
0.08
Cross
11
0
1.12
0.10
Cut-back
3
0
0.50
0.17
Long ball
2
0
0.13
0.06
Pass into the box / short pass
38
6
3.08
0.08
Own creation (carry, dribble, rebound)
18
0
1.00
0.06
Settled attack56% of shots
206
11
18.00
0.09
Cross
51
2
4.17
0.08
Cut-back
16
2
1.82
0.11
Long ball
9
0
0.51
0.06
Pass into the box / short pass
81
5
7.53
0.09
Own creation (carry, dribble, rebound)
49
2
3.97
0.08
Set piece25% of shots
92
9
11.67
0.13
Corner
68
6
7.17
0.11
Free kick
19
0
0.53
0.03
Penalty
5
3
3.97
0.79
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
Hugo Bueno
27
2.17
1
Jackson Tchatchoua
19
1.88
1
Hwang Hee-Chan
13
1.57
2
Mateus Mané
20
1.53
2
David Møller Wolfe
8
1.31
2
Who takes them
Player
Shots
xG
Goals
Tolu Arokodare
47
5.70
3
Jørgen Strand Larsen
21
3.19
1
Yerson Mosquera
30
2.87
0
Adam Armstrong
18
2.83
2
Jhon Arias
16
2.32
1
Vision AI Model Insight
The biggest source of Wolves's chances, by xG, is settled attack (18.0 of 35.5 xG, 206 of 370 shots), mostly pass into the box / short pass. 72 of the 370 shots (19%) came within 15 seconds of winning the ball. 92 of the 370 shots came from set pieces; 26 goals from 35.5 xG (-9.5).
Chance Creation — the pass map
The football question
How does Wolves 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
39CB · 16%
11Long Wide · 4%
36Long Central · 15%
54Wide → FB · 22%
106Central · 43%
Sequence success — final-third
1008final-third sequences this season
21%succeed — produce a shot
44%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
CB39 · 16%
Long Wide11 · 4%
Long Central36 · 15%
Wide → FB54 · 22%
Central106 · 43%
Who creates — volume × threat
37Bueno
29Mané
19Tchatchoua
18André
16Gomes
14Hee-Chan
12Bellegarde
246key passes #21 of 23
11.6box entries/game #16 of 23
26.9final-3rd entries/game #17 of 23
0.93xG/game #22 of 23
26goals #20 of 23
-9.5finishing vs xG #22 of 23
◆ Vision AI Insight
Wolves lean right — 40% of their key passes come from that flank and its half-space (99 of 246), most often from Tchatchoua (17). They funnel chances into the c of the final third, most often via RW att → C att (38 key passes, most often from Bueno). Bueno is the primary source overall. They generate 0.93 xG a game and finish it wasteful — they under-perform their xG, led by Tolu Arokodare (3G), Rodrigo Gomes (3G), Santiago Bueno (3G).
Crosses & Cutbacks — squad-wide
The question
How does Wolves deliver into the box from wide areas, and what comes of it?
572 crosses: 183 found a team-mate (32%), 65 led to a shot (11%). 104 cut-backs: 44 found a team-mate (42%), 19 led to a shot (18%). 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 Wolves actually cross from?
572 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. 25% come from one territory, the advanced right wing; 22 come from the half-spaces and 0 from the centre.
Territory
Crosses
Share
Led to a shot
advanced · right wing
141
25%
13
byline · left wing
126
22%
18
byline · right wing
113
20%
15
advanced · left wing
97
17%
10
deep · right wing
39
7%
4
deep · left wing
34
6%
2
byline · right half-space
5
1%
0
deep · left half-space
4
1%
0
advanced · right half-space
4
1%
0
advanced · left half-space
4
1%
2
byline · left half-space
3
1%
1
deep · right half-space
2
0%
0
Finishing3 sections
Shot Map
370 shots for 35.5 xG (0.93/game, #22 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.
Low-output attack. 9.7 shots/game at 0.096 xG/shot. Box entries 12/game (#16). Threat created 9.2 xT/game (#19).
Goal Sequences
The move behind each of Wolves’s 27 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 — 27 goals traced.
How Do They Score?
The question
Where do the goals actually come from — open play, dead balls, which foot?
Every goal Wolves scored across the loaded matches, broken down by how it was created and finished.
By situation
Open play
17 · 65%
Set pieces
6 · 23%
Penalties
3 · 12%
By finish
Right foot
17 · 65%
Left foot
4 · 15%
Header
5 · 19%
Vision AI Model Insight
26 goals scored. 65% come in open play, but 23% 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
1040
Failed forward pass
719
Failed take-on
388
Lost in the defensive third
1257
Their weakest zone is the right wing in the attacking third: 39% of 852 pass attempts there are lost (333 times). Separately, 30% 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 Wolves 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.
15
Pressing Index /100 · passive
Intensity26
PPDA, high-press share, high-zone actions
Height9
how high up the pitch they defend
Effectiveness7
high recoveries & high-regain share
Disruption17
opponent completion & progression allowed
Every defensive action & where it happens
Where they win the ball back — by lane
RW
17%
RHS
22%
C
21%
LHS
21%
LW
19%
By third (high / mid / low regain)
Def 55%
Mid 31%
Att 14%
Read
Ranked #22 of 23 for press intensity, this is a high press. Note the tension: 55% of their raw defensive actions still occur in their own third — a possession effect, not a passive one. Dominating the ball (21% high-press share) means that when they do defend, it is often deep. Of recoveries, 47% are won high up the pitch.
Every pressing metric, ranked in the league
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#22/23
lower = more intense; rank is what matters
Defensive actions / game
143
#14/23
High-press share
21%
#15/23
def actions won high up
Defensive-action height (0–100)
35.3
#21/23
avg pitch height of actions
High recoveries / game
18.5
#20/23
ball won in mid+att third
High-recovery share
47%
#23/23
share of recoveries won high
Opponent completion allowed
83%
#20/23
lower = more disruptive
Opponent progressive allowed
9.0%
#18/23
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 Wolves win it back with a defensive action?
Counted over the 1190 opposition possessions that Wolves 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 Wolves step in. Wolves win it after 7.1 opponent passes in the first half-hour and after 4.1 after the hour mark (sooner).
PASSES THEY GET
5.5passes
median 3 · before Wolves win it
TIME THEY KEEP IT
16sec
median 8s · 31.3 wins per game
How many passes the opponent got in before the ball was won
42%
26%
14%
18%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
7.1
21s
15–30′
7.2
23s
30–45+′
5.7
16s
45–60′
5.1
15s
60–75′
4.0
13s
75–90+′
4.1
12s
average passesaverage seconds
Phases of play
Every moment of the match read twice — what Wolves were doing with the
ball, and what the opposition were doing about it at that exact moment. Built from the
real action sequence in time order, so one possession is split across the phases it
genuinely passed through rather than labelled once by where it began.
6,542phase segments analysed
5.0smedian time before the opposition engage
2.0their passes per defensive action when pressed
#16of 20 for share of defending spent pressing high
High press 6.5%Mid block 9.7%Low block 54.2%Unopposed 29.7%
What opponents do against Wolves
The mirror image, and the part no other section can show: whether teams come
out to press Wolves or sit in and invite them on. A side that is always faced with a low
block is playing a different sport from one that is pressed every week.
High press 11.2%Mid block 13.4%Low block 46.5%Unopposed 28.8%
Which phases actually produce
Phase
Segments
Avg length
Avg actions
Ends in shot
Turned over
Build-up
2,336
4.0s
2.7
0.3%
55.8%
Progression
2,694
6.0s
3.6
0.4%
50.6%
Chance creation
1,415
4.9s
3.0
24.0%
53.6%
Attacking transition
97
7.0s
4.3
5.2%
42.3%
Most common routes through the phases
How a possession actually travelled, in order. The chain is the tactical signature — a move that climbs all three thirds is a different thing from one that never leaves the first.
Route
Times
Ends in shot
build_up
1,025
0.4%
progression
735
0.1%
build_up → progression
399
0.0%
chance_creation
334
35.9%
progression → chance_creation
295
29.2%
progression → build_up
185
0.0%
Attitude in each third
Every other style number here says how much Wolves 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 Wolves 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) 5,050 passes
nothing unusual in this zone
Width of use-0.0320.94
Wide-channel share-0.120.30
Side bias+0.562.27
Pass length+0.5824.80
Long-ball share+0.600.29
Forward gain per pass+0.6014.04
Backward-pass share+0.020.20
Deep-pass share+0.560.34
Completion-0.600.74
Seconds between passes+0.163.42
Side used
left 30% · central 45% · right 24%
Progression (middle third) 7,310 passes
nothing unusual in this zone
Width of use+0.3126.24
Wide-channel share+0.070.41
Side bias+0.592.41
Pass length-0.2616.86
Long-ball share+0.090.10
Forward gain per pass-0.193.31
Backward-pass share+0.390.35
Deep-pass share+0.040.16
Completion-0.330.82
Seconds between passes-0.033.18
Side used
left 39% · central 28% · right 33%
Final third 3,033 passes
nothing unusual in this zone
Width of use-0.0331.53
Wide-channel share-0.110.58
Side bias+0.06-0.07
Pass length+0.6916.53
Long-ball share+0.700.13
Forward gain per pass+0.281.42
Backward-pass share-0.290.37
Deep-pass share+0.420.10
Completion-0.600.65
Seconds between passes+0.453.74
Side used
left 42% · central 16% · right 42%
Reference measured across roughly 3,500 match-teams
in this competition, per third, so the comparison is with Wolves'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 Wolves 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 share18%
#8 of 23 · reaches final third in ≤3 passes
Patient build-up share22%
#17 of 23 · spells of 6+ passes
Tempo (possession length)15.9s
#7 of 23 · shorter = faster tempo
Passes per possession3.9
#19 of 23
Counter-attack shots /game0.92
#14 of 23 · shot within 12s of a regain in own half
Press→shot /game2.50
#20 of 23 · shot within 8s of a high regain
Speed to final third after regain11.9s
#6 of 23 · lower = faster break
Sequence success — attacking transition
106attacking transition sequences this season
16%succeed — produce a shot
57%turned over with nothing to show
Sequence success — defensive transition
378defensive transition sequences this season
55%succeed — reach the next third or a shot
13%turned over with nothing to show
Vision AI Model Insight
Wolves are a direct, fast-break side: 18% of possessions reach the final third in 3 passes or fewer (#8 of 23), against 22% built patiently over 6+ passes (#17 of 23). Winning the ball back leads to a shot inside 12 seconds 0.92 times a game (#14), and a high regain converts to a shot within 8 seconds 2.50 times a game (#20 of 23) — 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 Wolves? 1841 opponent progressive passes and 529 shots faced (56.2 xG against) across the season.
Opponent threat by lane (attacking into Wolves)
Right wing
31%
Right half-space
12%
Central
14%
Left half-space
14%
Left wing
30%
Where the danger came from. Opponents generated most of their threat through the right wing (31% of the danger conceded).
How Do They Concede?
The question
Where do the goals against Wolves actually come from — open play, dead balls, and who supplies them?
Every goal Wolves conceded across the loaded matches — the opponent's shots in Wolves'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 (379 total)
By situation
Open play
49 · 77%
Set pieces
10 · 16%
Penalties
5 · 8%
Vision AI Model Insight
64 goals conceded. 77% come in open play; set pieces account for only 16%, so dead-ball defending is not the main issue. 19% are headers — aerial defending is not where the damage comes from. Their supply line runs mainly through the right wing of the attacking third (18% 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
17 g · 23.8 xG
Set pieces
6 g · 7.7 xG
Penalties
3 g
23% of goals come from set pieces (6 of 26), worth 7.7 xG from 87 shots. Set pieces are a secondary threat; the danger is in open play.
Where do Wolves's corners go?
129 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 (66)
Delivered into the box 98%Short 2%
Left side (63)
Delivered into the box 98%Short 2%
Where opponents attack Wolves from set pieces
96 shots from opponent corners against Wolves, 9 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 (47)
Shot 47Goals 3
Left flag (49)
Shot 49Goals 6
Tactical Strengths & Weaknesses
Strengths (top-third vs league)
none stand out
Weaknesses (bottom-third vs league)
xG created /game — 0.93 (4th pct)
press intensity (PPDA) — 7.22 (4th pct)
shots /game — 9.74 (4th pct)
field tilt % — 38.98 (9th pct)
xG conceded /game — 1.48 (13th 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.
West Ham
85%
Burnley
83%
Coventry
76%
Hull City
71%
Sunderland
70%
Performance vs Opponent Styles
The question
Not just how Wolves 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 Wolves's own passing behaviour to the result.
Results
opponent type
goals for/g
goals against/g
points/g
vs high-pressing opponents (12m)
0.83
1.42
0.75
vs mid-block opponents (12m)
0.67
1.33
0.75
vs low-block opponents (14m)
0.64
2.50
0.14
Behaviour — how Wolves actually play against each
opponent type
retention
directness (long balls)
prog. passes/g
xT/g
box entries/g
vs high-pressing opponents
73%
20%
34
8.1
9.5
vs mid-block opponents
76%
18%
36
8.7
11.8
vs low-block opponents
80%
14%
43
10.5
13.2
Vision AI Model Insight
Facing a high press, retention drops to 73% vs 80% against a low block (7pt lower under pressure), and directness (long-ball share) sits at 20% when pressed vs 14% 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. Wolves are territorially-dominated with a aggressive high-press.
Their attack creates 0.93 xG a game and they concede 1.48 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 Wolves’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.
Threat from passingvs Leeds · 1-3 · 2025-09-209.61avg 5.02
PPDAat Man City · 0-2 · 2026-01-2414.8avg 7.2
Field tiltvs Burnley · 2-3 · 2025-10-2665%avg 39%
Passesvs Everton · 2-3 · 2025-08-30574avg 405
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 Wolves’s last 12 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).
+15.0on-ball value, 12 matches
Where the value came from
Passes4469 actions+8.54
Carries953 actions-1.36
Dribbles203 actions-0.27
Shots127 actions+4.54
Ball wins713 actions+3.58
Most on-ball value (OBV)
Actions
OBV
per 100
Mateus Mané
432
+2.52
+0.58
Adam Armstrong
210
+1.81
+0.86
André
704
+1.36
+0.19
Hugo Bueno
596
+1.22
+0.20
João Gomes
604
+1.06
+0.18
Santiago Bueno
573
+1.03
+0.18
Jackson Tchatchoua
283
+0.79
+0.28
Hwang Hee-Chan
128
+0.74
+0.58
Biggest single moves
OBV
1 Adam Armstrong · shot
+0.754
2 Tolu Arokodare · shot
+0.715
3 Adam Armstrong · pass
+0.427
4 Mateus Mané · pass
+0.426
5 Jackson Tchatchoua · pass
+0.417
6 Santiago Bueno · pass
+0.389
Set Pieces by Player
Who delivers Wolves’s dead balls and who finishes them this season. 129 corners taken, most by Hugo Bueno (45); 87 shots from corners and free kicks, 6 goals, led by Santiago Bueno (3 goals, 1.60 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
Hugo Bueno
45
41 (91%)
3
1
0.11
1
—
Mateus Mané
29
28 (97%)
6
—
0.19
3
—
Tolu Arokodare
—
—
12
1
1.77
1
0/1
Yerson Mosquera
—
—
12
—
1.06
—
—
André
19
18 (95%)
3
—
0.12
1
—
Jhon Arias
16
14 (88%)
4
—
0.13
3
—
Hwang Hee-Chan
6
6 (100%)
5
—
0.16
2
1/1
Ladislav Krejcí
—
—
7
1
0.37
—
—
Emmanuel Agbadou
—
—
6
—
0.44
1
—
João Gomes
—
—
6
—
0.25
3
—
Santiago Bueno
—
—
5
3
1.60
—
—
Jørgen Strand Larsen
—
—
2
—
0.15
—
1/2
Adam Armstrong
—
—
3
—
0.28
—
1/1
Jean-Ricner Bellegarde
5
5 (100%)
2
—
0.06
2
—
Top Performers
Ranked by Vision AI Score — the same tactical behavioural evaluation each player's own report page shows (min 300 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
Hugo Bueno
M
2539
1.31
66
28.3
Mateus Mané
M
1946
1.34
52
18.4
Jackson Tchatchoua
M
2005
0.85
26
14.5
Jean-Ricner Bellegarde
M
1319
0.82
32
13.0
Yerson Mosquera
D
2261
0.36
30
24.8
André
M
2946
0.55
19
26.0
Ladislav Krejcí
D
2521
0.29
26
31.1
Hwang Hee-Chan
F
1512
0.83
19
9.3
Defensive Volume Leaders
Who does the defensive work — tackles, interceptions, recoveries.
player
pos
min
def actions
tackles
intercept
recoveries
André
M
2946
461
82
30
212
Yerson Mosquera
D
2261
443
62
30
93
Ladislav Krejcí
D
2521
401
32
32
79
Hugo Bueno
M
2539
398
67
38
115
Santiago Bueno
D
2729
387
50
27
68
Tolu Arokodare
F
1595
281
7
5
26
Jackson Tchatchoua
M
2005
255
29
15
62
Toti Gomes
D
1564
240
23
14
53
Full Squad Data
Every player with 100+ minutes (25 players).
player
pos
apps
min
goals
xG
key P
prog
def
xT
André
M
35
2946
1
0.6
18
85
461
26.0
Santiago Bueno
D
29
2729
3
2.0
9
59
387
19.8
Hugo Bueno
M
35
2539
1
0.9
37
126
398
28.3
Ladislav Krejcí
D
28
2521
2
1.8
8
112
401
31.1
Yerson Mosquera
D
27
2261
0
2.9
9
119
443
24.8
José Sá
G
23
2214
0
0.0
1
125
88
27.1
Jackson Tchatchoua
M
31
2005
0
0.1
19
86
255
14.5
Mateus Mané
M
27
1946
3
2.3
29
73
218
18.4
Tolu Arokodare
F
33
1595
3
5.7
9
7
281
2.0
Toti Gomes
D
19
1564
0
0.8
6
57
240
15.0
Hwang Hee-Chan
F
26
1512
2
2.1
14
41
128
9.3
Jean-Ricner Bellegarde
M
26
1319
1
1.2
12
39
151
13.0
Emmanuel Agbadou
D
14
1242
0
0.6
4
72
232
18.1
Jhon Arias
F
23
1194
1
2.3
11
27
197
7.4
Adam Armstrong
F
14
1192
2
2.8
5
6
69
2.0
Sam Johnstone
G
12
1154
0
0.0
0
104
38
19.9
David Møller Wolfe
M
22
1136
0
0.1
8
56
107
9.3
Rodrigo Gomes
M
22
1054
3
1.9
3
27
125
4.6
Marshall Munetsi
M
13
931
1
1.6
3
15
160
3.4
Matt Doherty
D
11
806
0
0.1
3
35
133
7.0
Angel Gomes
M
12
491
0
0.2
6
16
42
5.1
Ki-Jana Hoever
M
8
437
0
0.0
4
36
74
5.9
Pedro Lima
M
9
299
0
0.7
3
12
48
2.0
Dan Bentley
G
3
292
0
0.0
0
10
8
2.5
Fer López
M
9
284
0
0.3
3
9
21
3.1
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.
André
Ball-winner / destroyer · Midfielder
minutes2946
goals · xG1 · 0.6
prog passes /902.6
key passes /900.55
def actions /9014.1
pass accuracy89%
Santiago Bueno
Stopper / marker · Defender
minutes2729
goals · xG3 · 2.0
prog passes /901.9
key passes /900.30
def actions /9012.8
pass accuracy86%
Hugo Bueno
Ball-winner / destroyer · Midfielder
minutes2539
goals · xG1 · 0.9
prog passes /904.5
key passes /901.31
def actions /9014.1
pass accuracy75%
Ladislav Krejcí
Stopper / marker · Defender
minutes2521
goals · xG2 · 1.8
prog passes /904.0
key passes /900.29
def actions /9014.3
pass accuracy81%
Yerson Mosquera
Ball-playing defender · Defender
minutes2261
goals · xG0 · 2.9
prog passes /904.7
key passes /900.36
def actions /9017.6
pass accuracy74%
José Sá
Goalkeeper · Goalkeeper
minutes2214
goals · xG0 · 0.0
prog passes /905.1
key passes /900.04
def actions /903.6
pass accuracy65%
Jackson Tchatchoua
Ball-winner / destroyer · Midfielder
minutes2005
goals · xG0 · 0.1
prog passes /903.9
key passes /900.85
def actions /9011.4
pass accuracy70%
Mateus Mané
Ball-winner / destroyer · Midfielder
minutes1946
goals · xG3 · 2.3
prog passes /903.4
key passes /901.34
def actions /9010.1
pass accuracy69%
Tolu Arokodare
Ball-winner / destroyer · Midfielder
minutes1595
goals · xG3 · 5.7
prog passes /900.4
key passes /900.51
def actions /9015.9
pass accuracy60%
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 (4th pct)
Sign a higher-volume goalscorer or chance creator. The data flags this because the attack under-produces xG (0.93, 4th percentile vs the league).
Need — bottom-third: press intensity (PPDA) (4th pct)
Sign a high-energy presser to lift defensive intensity. The data flags this because the press is passive (7.22, 4th percentile vs the league).
Need — bottom-third: shots /game (4th pct)
Sign a shot-taking forward to raise shot volume. The data flags this because shot volume is low (9.74, 4th percentile vs the league).