Combining every season — this one is larger, give it a moment.
Team reportPremier League 2026
Tottenham
A possession-dominant side, aggressive high-press out of possession, with a moderate attack. Currently #14 of 20 on 9 points (2-3-3, 8:10). 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 Tottenham and how good was the season?
Read. Tottenham currently sit #14 of 20 on 9 points (2W-3D-3L, 1.12 pts/game), scoring 8 and conceding 10 for a goal difference of -2.
On the ball they are a possession-dominant team (62% field tilt, #3 in the league); off it they defend with a aggressive high-press (#2 of 20 for press intensity).
Across the 8 matches of event data, they create 1.01 xG/game (#19) and concede 1.29 (#10), a net of -0.28 xG/game — the underlying performance matches the actual goal difference.
Season Context — the league table
The question
Where did Tottenham actually finish, and against whom?
The authoritative final table (full season), centred on Tottenham.
#
team
P
W
D
L
GF
GA
GD
Pts
11
Everton
8
2
4
2
8
9
-1
10
12
Ipswich
8
3
1
4
12
16
-4
10
13
Bournemouth
8
2
3
3
12
13
-1
9
14
Tottenham
8
2
3
3
8
10
-2
9
15
Fulham
8
2
2
4
8
10
-2
8
16
Man Utd
8
1
4
3
10
13
-3
7
17
Crystal Palace
8
2
1
5
9
19
-10
7
Match Momentum — when they create and concede danger
The question
Across a whole game, which periods does Tottenham 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
Tottenham tend to fade after a strong start — first-half net xG +0.48, second-half -2.69. Their strongest period is 16-30' (net +0.35 xG); their most dangerous period to face is 46-60' (net -1.58 xG against them).
xG Race — the season, match by match
Expected goals piling up over 8 matches: Tottenham 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 (-2.2 xG). Hover a point for that match.
TottenhamOpponents against Tottenham
Tactical DNA
The football question
In five genes, what is Tottenham'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.
88
Build-Up
possession control from the back
58
Progression
moving the ball upfield with threat
37
Creation
manufacturing chances
88
Press
winning the ball high & hard
50
Solidity
resisting the opponent's threat
Genetic identity
Tottenham are defined most by their Build-Up DNA (88/100 · elite at possession control from the back), and least by their Creation DNA (37/100). 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
74%
Possession
57%
Field tilt
65%
Disrupt
Central progression ~proxy
39%
Dribble tendency
96%
Progressive passes
35%
Finish
Final-third patience
74%
Shot quality
17%
Open-play xG
9%
Press
Forward press ~proxy
74%
High regains
74%
Pressing efficiency ~proxy
57%
Defend
Def-mid press ~proxy
74%
High line ~proxy
57%
PAdj defensive actions
91%
Tottenham is defined most by Defend (74% in the league) and weakest on Finish (33%). 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 Tottenham actually control, relative to the rest of the league?
Field tilt = share of final-third touches between the two sides across Tottenham'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; Tottenham highlighted. Hover any dot for the team.
Vision AI Model Insight
Tottenham post 62% field tilt, ranking #3 of 20 in the league — a genuinely territory-dominant side, and their 1.12 points/game sits below 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 Tottenham has the ball, how much of that time is spent building, progressing and creating?
The share of Tottenham'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
24.0%
Progression
47.7%
Chance creation
26.6%
Attacking transition
1.7%
Vision AI Model Insight
Progression is where Tottenham spends most of its time on the ball (48% 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
Hecke ↔ Ven
154p · 0.2xT
Robertson ↔ Ven
118p · -0.8xT
Hecke ↔ Kinsky
101p · -0.7xT
Hecke ↔ Gray
91p · -1.2xT
Tonali ↔ Hecke
89p · 0.3xT
Ven ↔ Kinsky
88p · -0.2xT
Robertson ↔ Tonali
82p · -0.1xT
Hecke ↔ Fernandes
81p · 0.4xT
Tonali ↔ Ven
79p · -0.3xT
Tonali ↔ Fernandes
62p · 0.1xT
Vision AI Model Insight
Busiest connection: Jan Paul van Hecke ↔ Micky van de Ven (154 passes). Most dangerous per pass: Mohammed Kudus ↔ Archie Gray (0.03 xT/pass over 35 passes) — the combination that hurts opponents most when it fires, not just the one used most.
Touch Density — where Tottenham 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 Tottenham 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
4735
ACTIONS
Carries
the ball moved with a player
1196
ACTIONS
Vision AI Model Insight
Their single busiest zone is the centre of the middle third (14.0% of all actions). 66% of actions run through the inside lanes (centre + both half-spaces) versus 34% down the two wings — an inside-dominant, half-space build. By third: 25% defensive, 52% middle, 24% 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. Tottenham leaned on their left (37%). The bars under the pitch put the league’s share beside theirs.
Tottenham
37%26%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. Tottenham made 407 this season (50.9 a match); 33% 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
133
16.6
33%
31%
Left half-space
52
6.5
13%
16%
Centre
57
7.1
14%
13%
Right half-space
55
6.9
14%
14%
Right wing
110
13.8
27%
27%
Build-up2 sections
Build-Up — the pass map
The football question
How does Tottenham 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.
1157build-up passes attempted
84%completed
23%reach the middle third
1%go long into the final third
Pass types
424CB · 44%
42Long Wide · 4%
3Long Central · 0%
386Wide → FB · 40%
117Central · 12%
Sequence success — build-up
136build-up sequences this season
14%succeed — reach the next third or a shot
31%turned over with nothing to show
CB424 · 44%
Long Wide42 · 4%
Long Central3 · 0%
Wide → FB386 · 40%
Central117 · 12%
◆ Vision AI Insight
Tottenham build predominantly down the left, with Kinsky as the fulcrum (220 build-up passes). Only 24% 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 (75×).
Pass Explorer
Every pass of Tottenham’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. 340 progressive passes across 8 matches (42.5/game).
Origin lane
Right wing
26%
Right half-space
16%
Central
11%
Left half-space
16%
Left wing
31%
Progression is left-dominant: 42% right side · 11% central · 47% left.
Who generates it
Jan Paul van Hecke
60 · 18%
Andy Robertson
33 · 10%
Sandro Tonali
30 · 9%
Mathys Tel
25 · 7%
Sávio
23 · 7%
Top two — Jan Paul van Hecke & Andy Robertson — account for 27% of all progression.
PROGRESSION
42.5 prog passes/game
mostly left
→
PENETRATION
26% reach final third
22% reach the box
→
THREAT
the other 74% is recycled
progress ≠ danger
What came of it.26% of progressive passes reached the final third and 22% the box; the rest was recycled in front of the block. 27% 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 wingefficient
Right half-spacebalanced
Centralsterile
Left half-spacebalanced
Left wingefficient
Progression — the pass map
The football question
Once Tottenham 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.
2413progression passes attempted
91%completed
14%break into the final third
8%go backward into the defensive third
Pass types
767CB · 35%
43Long Wide · 2%
9Long Central · 0%
1036Wide → FB · 47%
330Central · 15%
Sequence success — progression
234progression sequences this season
30%succeed — reach the next third or a shot
40%turned over with nothing to show
CB767 · 35%
Long Wide43 · 2%
Long Central9 · 0%
Wide → FB1036 · 47%
Central330 · 15%
◆ Vision AI Insight
Tottenham progress predominantly down the left, with Hecke the busiest midfield outlet (387 progression passes). Only 14% of passes that start in midfield actually break the final-third line — the rest (78%) is recycled back into midfield or (8%) 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 → C mid (146×) — 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)
12%
Progressive
1%
Neutral / sideways
47%
Safe / backward
40%
13% of passes are progressive-or-better; the remaining 87% keep possession without advancing it. A control-first side — safe in build-up, reliant on a few players for the incision.
Where does Tottenham actually progress the ball with their feet, not a pass?
265 progressive carries this season across the whole squad (23% of 1149 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 68m. Most active carrier: Hecke (78). Filter to one player below.
First (77)Second (73)Third (64)Fourth (51)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 Tottenham'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.
114shots
8.1xG
0.07xG per shot
2big chances (0.3+ xG)
7goals
#12key passes / game in the league
#10box entries / game in the league
Kind of attack, then how the shot was set up
Shots
Goals
xG
xG / shot
Attacking transition23% of shots
26
3
2.15
0.08
Cross
3
0
0.20
0.07
Cut-back
2
0
0.42
0.21
Long ball
1
0
0.04
0.04
Pass into the box / short pass
11
2
0.66
0.06
Own creation (carry, dribble, rebound)
9
1
0.83
0.09
Settled attack51% of shots
58
4
4.29
0.07
Cross
10
1
0.86
0.09
Cut-back
2
0
0.26
0.13
Long ball
3
0
0.29
0.10
Pass into the box / short pass
27
2
1.64
0.06
Own creation (carry, dribble, rebound)
16
1
1.24
0.08
Set piece26% of shots
30
0
1.68
0.06
Corner
24
0
1.53
0.06
Free kick
6
0
0.15
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
Sávio
8
0.74
0
Andy Robertson
8
0.66
2
Sandro Tonali
8
0.62
1
Mohammed Kudus
6
0.53
2
Jan Paul van Hecke
6
0.29
0
Who takes them
Player
Shots
xG
Goals
Dominic Solanke
12
1.42
1
Sávio
8
0.76
0
Omar Marmoush
12
0.73
1
Micky van de Ven
4
0.72
0
Conor Gallagher
6
0.71
1
Vision AI Model Insight
The biggest source of Tottenham's chances, by xG, is settled attack (4.3 of 8.1 xG, 58 of 114 shots), mostly pass into the box / short pass. 26 of the 114 shots (23%) came within 15 seconds of winning the ball. 30 of the 114 shots came from set pieces; 7 goals from 8.1 xG (-1.1).
Chance Creation — the pass map
The football question
How does Tottenham 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
9CB · 13%
6Long Wide · 8%
4Long Central · 6%
29Wide → FB · 41%
23Central · 32%
Sequence success — final-third
218final-third sequences this season
22%succeed — produce a shot
34%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
CB9 · 13%
Long Wide6 · 8%
Long Central4 · 6%
Wide → FB29 · 41%
Central23 · 32%
Who creates — volume × threat
11Sávio
10Robertson
7Hecke
7Fernandes
7Tonali
6Kudus
5Tel
71key passes #12 of 20
13.2box entries/game #10 of 20
40.4final-3rd entries/game #5 of 20
1.01xG/game #19 of 20
7goals #15 of 20
-1.1finishing vs xG #11 of 20
◆ Vision AI Insight
Tottenham lean left — 49% of their key passes come from that flank and its half-space (35 of 71), most often from Robertson (9). They funnel chances into the c of the final third, most often via LHS att → C att (15 key passes, most often from Robertson). Sávio is the primary source overall. They generate 1.01 xG a game and finish it wasteful — they under-perform their xG, led by Dominic Solanke (1G), James Maddison (1G), Jan Paul van Hecke (1G).
Crosses & Cutbacks — squad-wide
The question
How does Tottenham deliver into the box from wide areas, and what comes of it?
128 crosses: 47 found a team-mate (37%), 14 led to a shot (11%). 28 cut-backs: 8 found a team-mate (29%), 4 led to a shot (14%). 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 Tottenham actually cross from?
128 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. 23% come from one territory, the byline left wing; 15 come from the half-spaces and 0 from the centre.
Territory
Crosses
Share
Led to a shot
byline · left wing
30
23%
4
byline · right wing
29
23%
6
advanced · left wing
25
20%
1
advanced · right wing
20
16%
1
deep · right wing
6
5%
0
byline · right half-space
6
5%
0
advanced · right half-space
4
3%
1
deep · left wing
3
2%
0
advanced · left half-space
3
2%
1
byline · left half-space
2
2%
0
Finishing3 sections
Shot Map
114 shots for 8.1 xG (1.01/game, #19 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.
Moderate attack. 14.2 shots/game at 0.071 xG/shot. Box entries 13/game (#10). Threat created 11.5 xT/game (#8).
Goal Sequences
The move behind each of Tottenham’s 8 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 — 8 goals traced.
How Do They Score?
The question
Where do the goals actually come from — open play, dead balls, which foot?
Every goal Tottenham scored across the loaded matches, broken down by how it was created and finished.
By situation
Open play
7 · 100%
Set pieces
0 · 0%
Penalties
0 · 0%
By finish
Right foot
4 · 57%
Left foot
1 · 14%
Header
2 · 29%
Vision AI Model Insight
7 goals scored. 100% come in open play; set pieces add 0%. 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
282
Failed forward pass
191
Failed take-on
115
Lost in the defensive third
185
Their weakest zone is the right half-space in the attacking third: 29% of 203 pass attempts there are lost (59 times). Separately, 41% 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 Tottenham 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.
78
Pressing Index /100 · strong
Intensity83
PPDA, high-press share, high-zone actions
Height85
how high up the pitch they defend
Effectiveness92
high recoveries & high-regain share
Disruption42
opponent completion & progression allowed
Every defensive action & where it happens
Where they win the ball back — by lane
RW
20%
RHS
19%
C
24%
LHS
19%
LW
17%
By third (high / mid / low regain)
Def 41%
Mid 42%
Att 17%
Read
Ranked #2 of 20 for press intensity, this is a high press. 59% of defensive actions happen in the middle & attacking thirds, at an average height of 41. Of recoveries, 61% are won high up the pitch.
Every pressing metric, ranked in the league
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#2/20
lower = more intense; rank is what matters
Defensive actions / game
155
#12/20
High-press share
26%
#3/20
def actions won high up
Defensive-action height (0–100)
41.1
#3/20
avg pitch height of actions
High recoveries / game
26.9
#1/20
ball won in mid+att third
High-recovery share
61%
#2/20
share of recoveries won high
Opponent completion allowed
79%
#10/20
lower = more disruptive
Opponent progressive allowed
9.1%
#13/20
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 Tottenham win it back with a defensive action?
Counted over the 243 opposition possessions that Tottenham 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 Tottenham step in. Tottenham win it after 4.8 opponent passes in the first half-hour and after 3.6 after the hour mark (sooner).
PASSES THEY GET
4.1passes
median 3 · before Tottenham win it
TIME THEY KEEP IT
12sec
median 7s · 30.4 wins per game
How many passes the opponent got in before the ball was won
43%
33%
14%
10%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
4.2
11s
15–30′
5.5
18s
30–45+′
4.2
11s
45–60′
3.4
10s
60–75′
4.0
11s
75–90+′
3.5
9s
average passesaverage seconds
Attitude in each third
Every other style number here says how much Tottenham 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 Tottenham 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) 1,157 passes
nothing unusual in this zone
Width of use-0.7119.53
Wide-channel share-0.600.27
Side bias+0.040.32
Pass length-0.7719.92
Long-ball share-0.810.15
Forward gain per pass-0.718.37
Backward-pass share+0.740.23
Deep-pass share-0.770.21
Completion+0.590.84
Seconds between passes-0.133.25
Side used
left 27% · central 48% · right 25%
Progression (middle third) 2,413 passes
played inside, narrow, central, shorter passing
Width of use-1.2222.98
Wide-channel share-1.290.31
Side bias-0.03-0.06
Pass length-1.1715.60
Long-ball share-1.060.06
Forward gain per pass-0.602.70
Backward-pass share-0.160.33
Deep-pass share-0.840.12
Completion+0.950.90
Seconds between passes-0.732.88
Side used
left 32% · central 37% · right 32%
Final third 1,165 passes
nothing unusual in this zone
Width of use-0.4230.58
Wide-channel share-0.230.57
Side bias+0.362.06
Pass length-0.4114.83
Long-ball share-0.280.09
Forward gain per pass-0.480.41
Backward-pass share+0.290.41
Deep-pass share-0.620.07
Completion+0.580.74
Seconds between passes-0.033.38
Side used
left 44% · central 17% · right 39%
Reference measured across roughly 3,500 match-teams
in this competition, per third, so the comparison is with Tottenham'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 Tottenham 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 share17%
#8 of 20 · reaches final third in ≤3 passes
Patient build-up share25%
#10 of 20 · spells of 6+ passes
Tempo (possession length)18.5s
#12 of 20 · shorter = faster tempo
Passes per possession4.4
#10 of 20
Counter-attack shots /game0.52
#19 of 20 · shot within 12s of a regain in own half
Press→shot /game2.93
#9 of 20 · shot within 8s of a high regain
Speed to final third after regain12.1s
#6 of 20 · lower = faster break
Sequence success — attacking transition
14attacking transition sequences this season
0%succeed — produce a shot
64%turned over with nothing to show
Sequence success — defensive transition
56defensive transition sequences this season
71%succeed — reach the next third or a shot
5%turned over with nothing to show
Vision AI Model Insight
Tottenham are balanced between tempo and patience: 17% of possessions reach the final third in 3 passes or fewer (#8 of 20), against 25% built patiently over 6+ passes (#10 of 20). Winning the ball back leads to a shot inside 12 seconds 0.52 times a game (#19), and a high regain converts to a shot within 8 seconds 2.93 times a game (#9 of 20) — a modest but real source of chances.
Defending the box2 sections
Vulnerability Map — how opponents hurt them
The report flipped: where do opponents create their threat against Tottenham? 279 opponent progressive passes and 107 shots faced (10.3 xG against) across the season.
Opponent threat by lane (attacking into Tottenham)
Right wing
30%
Right half-space
10%
Central
22%
Left half-space
12%
Left wing
26%
Where the danger came from. Opponents generated most of their threat through the right wing (30% of the danger conceded).
How Do They Concede?
The question
Where do the goals against Tottenham actually come from — open play, dead balls, and who supplies them?
Every goal Tottenham conceded across the loaded matches — the opponent's shots in Tottenham'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 (79 total)
By situation
Open play
8 · 80%
Set pieces
2 · 20%
Penalties
0 · 0%
Vision AI Model Insight
10 goals conceded. 80% come in open play; set pieces account for only 20%, so dead-ball defending is not the main issue. 0% are headers — aerial defending is not where the damage comes from. Their supply line runs mainly through the right wing of the attacking third (23% 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
7 g · 6.4 xG
Set pieces
0 g · 1.7 xG
Penalties
0 g
0% of goals come from set pieces (0 of 7), worth 1.7 xG from 30 shots. Set pieces are a secondary threat; the danger is in open play.
Where do Tottenham's corners go?
52 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 (23)
Delivered into the box 87%Short 13%
Left side (29)
Delivered into the box 97%Short 3%
Where opponents attack Tottenham from set pieces
19 shots from opponent corners against Tottenham, 2 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 (11)
Shot 11Goals 1
Left flag (8)
Shot 8Goals 1
Tactical Strengths & Weaknesses
Strengths (top-third vs league)
press intensity (PPDA) — 3.86 (90th pct)
pass retention % — 85.32 (90th pct)
field tilt % — 62.25 (85th pct)
Weaknesses (bottom-third vs league)
xG created /game — 1.01 (5th pct)
Style Archetype & Similar Teams
Team archetype: High-press possession side. Their closest stylistic matches in the league (cosine similarity on style metrics) — sides that play a similar way.
Man City
56%
Arsenal
47%
Fulham
42%
Brighton
41%
Liverpool
39%
Performance vs Opponent Styles
The question
Not just how Tottenham 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 Tottenham's own passing behaviour to the result.
Results
opponent type
goals for/g
goals against/g
points/g
vs high-pressing opponents (2m)
0.50
2.00
0.50
vs mid-block opponents (2m)
1.50
0.50
2.00
vs low-block opponents (4m)
1.00
1.25
1.00
Behaviour — how Tottenham actually play against each
opponent type
retention
directness (long balls)
prog. passes/g
xT/g
box entries/g
vs high-pressing opponents
84%
8%
42
10.5
12.0
vs mid-block opponents
84%
11%
36
9.5
10.5
vs low-block opponents
86%
8%
46
13.1
15.2
Vision AI Model Insight
Facing a high press, retention drops to 84% vs 86% against a low block (3pt lower under pressure), and directness (long-ball share) sits at 8% when pressed vs 8% against a low block — their passing style barely changes regardless of how the opponent sets up, for better or worse.
Scouting Summary — the team in brief
In brief. Tottenham are possession-dominant with a aggressive high-press.
Their attack creates 1.01 xG a game and they concede 1.29 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 Tottenham’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.
Tottenham
across 8 matches this season
Progressive passesvs Aston Villa · 2-3 · 2026-09-1957avg 42
Field tiltvs Aston Villa · 2-3 · 2026-09-1975%avg 62%
Threat from passingvs Aston Villa · 2-3 · 2026-09-197.35avg 5.06
PPDAvs Coventry · 3-1 · 2026-10-193.1avg 3.9
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 Tottenham’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).
+10.7on-ball value, 8 matches
Where the value came from
Passes4735 actions+3.35
Carries1149 actions+0.54
Dribbles204 actions+1.02
Shots114 actions+3.06
Ball wins498 actions+2.70
Most on-ball value (OBV)
Actions
OBV
per 100
Jan Paul van Hecke
963
+1.86
+0.19
Sávio
267
+1.08
+0.41
Mohammed Kudus
292
+0.92
+0.32
Sandro Tonali
674
+0.83
+0.12
Pedro Porro
194
+0.75
+0.39
Antonín Kinsky
391
+0.73
+0.19
Mathys Tel
248
+0.68
+0.28
Dominic Solanke
113
+0.66
+0.58
Biggest single moves
OBV
1 Pedro Porro · take-on
+0.434
2 Mohammed Kudus · pass
+0.388
3 Sávio · pass
+0.372
4 Jan Paul van Hecke · ball win
+0.364
5 Sávio · shot
+0.325
6 Conor Gallagher · shot
+0.291
Set Pieces by Player
Who delivers Tottenham’s dead balls and who finishes them this season. 52 corners taken, most by Sávio (12); 30 shots from corners and free kicks, 0 goals. “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
Mathys Tel
7
4 (57%)
4
—
0.16
2
—
Mateus Fernandes
10
7 (70%)
2
—
0.06
—
—
Sandro Tonali
3
2 (67%)
4
—
0.15
—
—
Sávio
12
9 (75%)
1
—
0.03
—
—
Mohammed Kudus
5
2 (40%)
3
—
0.11
—
—
Pedro Porro
3
2 (67%)
3
—
0.07
1
—
Omar Marmoush
5
4 (80%)
2
—
0.07
1
—
Jan Paul van Hecke
—
—
3
—
0.19
—
—
Micky van de Ven
—
—
3
—
0.66
—
—
Andy Robertson
4
3 (75%)
1
—
0.01
1
—
James Maddison
3
2 (67%)
1
—
0.02
1
—
Dominic Solanke
—
—
2
—
0.13
—
—
Mikey Moore
—
—
1
—
0.03
—
—
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
Sávio
M
350
2.83
15
4.0
Andy Robertson
D
519
1.73
10
7.9
Mohammed Kudus
M
385
1.40
13
3.6
Sandro Tonali
M
733
0.86
11
8.7
Mateus Fernandes
M
398
1.58
9
6.3
Mathys Tel
M
365
1.23
9
3.8
Jan Paul van Hecke
D
768
0.82
7
17.3
Omar Marmoush
F
535
0.67
7
2.3
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)
Andy Robertson
FB
519
15 (7%)
7 (14%)
Mohammed Kudus
W
385
6 (33%)
3 (67%)
Pedro Porro
FB
240
6 (0%)
1 (100%)
Mathys Tel
W
365
2 (50%)
4 (0%)
Archie Gray
FB
610
1 (100%)
3 (0%)
Omar Marmoush
W
535
2 (50%)
1 (100%)
Destiny Udogie
FB
169
1 (100%)
2 (100%)
Defensive Volume Leaders
Who does the defensive work — tackles, interceptions, recoveries.
player
pos
min
def actions
tackles
intercept
recoveries
Jan Paul van Hecke
D
768
161
15
8
29
Sandro Tonali
M
733
106
19
6
40
Rodrigo Bentancur
M
417
101
15
6
24
Micky van de Ven
D
601
92
7
3
22
Archie Gray
D
610
85
12
9
24
Mohammed Kudus
M
385
68
7
3
27
Conor Gallagher
M
293
65
11
4
19
Mateus Fernandes
M
398
62
12
4
21
Full Squad Data
Every player with 100+ minutes (19 players).
player
pos
apps
min
goals
xG
key P
prog
def
xT
Jan Paul van Hecke
D
8
768
1
0.4
7
60
161
17.3
Antonín Kinsky
G
8
768
0
0.0
2
12
23
4.7
Sandro Tonali
M
8
733
0
0.5
7
30
106
8.7
Archie Gray
D
7
610
0
0.4
2
21
85
5.6
Micky van de Ven
D
7
601
0
0.7
0
21
92
7.7
Omar Marmoush
F
7
535
1
0.7
4
6
59
2.3
Andy Robertson
D
7
519
0
0.1
10
33
56
7.9
Rodrigo Bentancur
M
6
417
0
0.0
1
4
101
3.2
Dominic Solanke
F
8
416
1
1.4
1
2
59
0.5
Mateus Fernandes
M
8
398
0
0.3
7
22
62
6.3
Mohammed Kudus
M
7
385
0
0.5
6
16
68
3.6
Mathys Tel
M
6
365
1
0.4
5
25
36
3.8
Sávio
M
4
350
0
0.8
11
23
31
4.0
Conor Gallagher
M
6
293
1
0.7
1
4
65
1.8
Pedro Porro
D
4
240
0
0.4
3
13
32
2.3
James Maddison
M
6
221
1
0.2
1
6
37
3.1
Marcos Senesi
D
2
182
0
0.0
2
14
48
3.3
Destiny Udogie
D
4
169
0
0.0
1
15
35
2.8
Lucas Bergvall
M
4
156
0
0.1
0
5
29
1.3
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.
Jan Paul van Hecke
Ball-playing defender · Defender
minutes768
goals · xG1 · 0.4
prog passes /907.0
key passes /900.82
def actions /9018.9
pass accuracy90%
Antonín Kinsky
Goalkeeper · Goalkeeper
minutes768
goals · xG0 · 0.0
prog passes /901.4
key passes /900.23
def actions /902.7
pass accuracy74%
Sandro Tonali
Ball-winner / destroyer · Midfielder
minutes733
goals · xG0 · 0.5
prog passes /903.7
key passes /900.86
def actions /9013.0
pass accuracy89%
Archie Gray
Stopper / marker · Defender
minutes610
goals · xG0 · 0.4
prog passes /903.1
key passes /900.30
def actions /9012.5
pass accuracy83%
Micky van de Ven
Stopper / marker · Defender
minutes601
goals · xG0 · 0.7
prog passes /903.1
key passes /900.00
def actions /9013.8
pass accuracy95%
Omar Marmoush
Ball-winner / destroyer · Midfielder
minutes535
goals · xG1 · 0.7
prog passes /901.0
key passes /900.67
def actions /909.9
pass accuracy87%
Andy Robertson
Ball-playing defender · Defender
minutes519
goals · xG0 · 0.1
prog passes /905.7
key passes /901.73
def actions /909.7
pass accuracy79%
Rodrigo Bentancur
Ball-winner / destroyer · Midfielder
minutes417
goals · xG0 · 0.0
prog passes /900.9
key passes /900.22
def actions /9021.8
pass accuracy89%
Dominic Solanke
Ball-winner / destroyer · Midfielder
minutes416
goals · xG1 · 1.4
prog passes /900.4
key passes /900.22
def actions /9012.8
pass accuracy85%
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 (5th pct)
Sign a higher-volume goalscorer or chance creator. The data flags this because the attack under-produces xG (1.01, 5th percentile vs the league).
Need — below-average: xG conceded /game (50th pct)
Sign a defensive organiser / shot-suppressing centre-back. The data flags this because too much xG is conceded (1.29, 50th percentile vs the league).
Need — below-average: box entries /game (50th pct)
Sign a penetrative wide player or runner to create box entries. The data flags this because few entries into the box (13.25, 50th percentile vs the league).