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Team reportPremier League 2026

Chelsea

A balanced-territory side, aggressive high-press out of possession, with a moderate attack. Currently #5 of 20 on 13 points (4-1-3, 17:15). Tactical profile below is built from 8 matches of ball-by-ball event data, contextualised against the league.

balanced-territoryaggressive high-pressmoderate attack
L
L
L
L
D
W
L
W
W
L
D
L
W
W
L
13
points
#5 of 20 · 8 games
17:15
goals
+2 GD
4-1-3
record
W-D-L
1.62
points/game
#5 in the league
1.18
xG for/game
rank #12
1.33
xG against/game
rank #11
#15
press rank
of 20 · most aggressive
47%
field tilt
#14
Chelsea
League average
Expected goals for / game
1.181.33
Expected goals against / game
1.331.33
Field tilt
47%50%
Pass retention
80%80%
Overview

The team

Who they are and how the season went.

Who Are They? — Identity

The question
In one read, what kind of team is Chelsea and how good was the season?
Read. Chelsea currently sit #5 of 20 on 13 points (4W-1D-3L, 1.62 pts/game), scoring 17 and conceding 15 for a goal difference of +2. On the ball they are a balanced-territory team (47% field tilt, #14 in the league); off it they defend with a aggressive high-press (#15 of 20 for press intensity). Across the 8 matches of event data, they create 1.18 xG/game (#12) and concede 1.33 (#11), a net of -0.15 xG/game — the underlying performance matches the actual goal difference.

Season Context — the league table

The question
Where did Chelsea actually finish, and against whom?

The authoritative final table (full season), centred on Chelsea.

#teamPWDLGFGAGDPts
2Arsenal8701135+821
3Brighton8512237+1616
4Liverpool8431118+315
5Chelsea84131715+213
6Brentford8251149+511
7Leeds825196+311
8Newcastle83231214-211

Match Momentum — when they create and concede danger

The question
Across a whole game, which periods does Chelsea 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)
even0-15'16-30'31-45'46-60'61-75'76-90+'+1.16-1.06
Vision AI Model Insight
Chelsea tend to fade after a strong start — first-half net xG +0.34, second-half -1.54. Their strongest period is 0-15' (net +1.16 xG); their most dangerous period to face is 16-30' (net -1.06 xG against them).

xG Race — the season, match by match

Expected goals piling up over 8 matches: Chelsea 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 (-1.2 xG). Hover a point for that match.

048119.410.6Fulham (A) 3-2 · xG 1.85 to 1.25Brighton (H) 4-3 · xG 2.63 to 1.08Arsenal (A) 1-2 · xG 0.59 to 1.88Hull City (H) 2-2 · xG 0.79 to 1.51Brentford (A) 0-3 · xG 0.79 to 1.94Bournemouth (H) 5-1 · xG 0.81 to 1.70Everton (A) 2-0 · xG 1.44 to 0.49Tottenham (H) 0-2 · xG 0.51 to 0.74M1M2M3M4M5M6M7M8
ChelseaOpponents against Chelsea

Tactical DNA

The football question
In five genes, what is Chelsea'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
42
Progression
moving the ball upfield with threat
47
Creation
manufacturing chances
28
Press
winning the ball high & hard
45
Solidity
resisting the opponent's threat
Genetic identity
Chelsea are defined most by their Creation DNA (47/100 · average at manufacturing chances), and least by their Press DNA (28/100). 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).

Possess74Disrupt51Finish70Press55Defend61
Possess
Deep buildup
61%
Possession
83%
Field tilt
78%
Disrupt
Central progression ~proxy
91%
Dribble tendency
13%
Progressive passes
48%
Finish
Final-third patience
61%
Shot quality
74%
Open-play xG
74%
Press
Forward press ~proxy
35%
High regains
52%
Pressing efficiency ~proxy
78%
Defend
Def-mid press ~proxy
70%
High line ~proxy
65%
PAdj defensive actions
48%
Chelsea is defined most by Possess (74% in the league) and weakest on Disrupt (51%). 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.

Keeping the ball6 sections

Field Tilt — Territorial Control

The question
How much of the pitch does Chelsea actually control, relative to the rest of the league?

Field tilt = share of final-third touches between the two sides across Chelsea'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; Chelsea highlighted. Hover any dot for the team.

Arsenal: 64% tilt, 2.62 pts/gMan City: 66% tilt, 2.62 pts/gBrighton: 61% tilt, 2.00 pts/gLiverpool: 56% tilt, 1.88 pts/gChelsea: 47% tilt, 1.62 pts/gBrentford: 47% tilt, 1.38 pts/gNewcastle: 48% tilt, 1.38 pts/gLeeds: 43% tilt, 1.38 pts/gAston Villa: 40% tilt, 1.38 pts/gIpswich: 43% tilt, 1.25 pts/gHull City: 33% tilt, 1.25 pts/gEverton: 49% tilt, 1.25 pts/gTottenham: 62% tilt, 1.12 pts/gBournemouth: 50% tilt, 1.12 pts/gFulham: 54% tilt, 1.00 pts/gMan Utd: 60% tilt, 0.88 pts/gCrystal Palace: 39% tilt, 0.88 pts/gCoventry: 39% tilt, 0.75 pts/gNottingham Forest: 48% tilt, 0.75 pts/gSunderland: 48% tilt, 0.62 pts/gChelsea33%44%55%66%0.61.32.02.6FIELD TILT %POINTS / GAME
Vision AI Model Insight
Chelsea post 47% field tilt, ranking #14 of 20 in the league — below the league mid-point, spending more of the game defending their own territory than dictating it, and their 1.62 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 Chelsea has the ball, how much of that time is spent building, progressing and creating?

The share of Chelsea'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.

Build-up: 23.0% of the time on the ball23%Progression: 50.6% of the time on the ball51%Chance creation: 24.2% of the time on the ball24%Attacking transition: 2.1% of the time on the ball51%PROGRESSION
PhaseShare of the time
Build-up23.0%
Progression50.6%
Chance creation24.2%
Attacking transition2.1%
Vision AI Model Insight
Progression is where Chelsea spends most of its time on the ball (51% 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.

Maxence Lacroix ↔ Levi Colwill: 67 passes, -0.3 combined xTMaxence Lacroix ↔ Josh Acheampong: 58 passes, 0.2 combined xTEmiliano Martínez ↔ Levi Colwill: 50 passes, -0.2 combined xTLevi Colwill ↔ Pep Chavarría: 49 passes, -0.3 combined xTEmiliano Martínez ↔ Maxence Lacroix: 45 passes, -0.4 combined xTLevi Colwill ↔ Jorrel Hato: 44 passes, -0.5 combined xTPedro Neto ↔ Cole Palmer: 42 passes, 0.5 combined xTCole Palmer ↔ Josh Acheampong: 35 passes, 0.5 combined xTMorgan Rogers ↔ Pep Chavarría: 32 passes, 1.1 combined xTMorgan Rogers ↔ Jorrel Hato: 32 passes, 0.7 combined xTCole Palmer ↔ Maxence Lacroix: 29 passes, 1.1 combined xTMorgan Rogers ↔ João Pedro: 28 passes, 1.1 combined xTMorgan Rogers ↔ Cole Palmer: 28 passes, 1.2 combined xTPedro Neto ↔ Josh Acheampong: 25 passes, 0.6 combined xTMorgan Rogers ↔ Levi Colwill: 25 passes, 1.0 combined xTJoão Pedro ↔ Cole Palmer: 20 passes, 1.0 combined xTCole Palmer ↔ Roméo Lavia: 19 passes, 0.2 combined xTMaxence Lacroix ↔ Roméo Lavia: 19 passes, -0.1 combined xTJoão Pedro ↔ Jorrel Hato: 16 passes, 0.6 combined xTPedro Neto ↔ Morgan Rogers: 15 passes, 0.6 combined xTPedro Neto ↔ João Pedro: 15 passes, 0.7 combined xTCole Palmer ↔ Levi Colwill: 13 passes, -0.0 combined xTEmiliano Martínez ↔ Morgan Rogers: 12 passes, 1.2 combined xTLevi Colwill ↔ Roméo Lavia: 12 passes, 0.0 combined xTMorgan Rogers — isolate this player's connectionsRogersCole Palmer — isolate this player's connectionsPalmerMaxence Lacroix — isolate this player's connectionsLacroixPedro Neto — isolate this player's connectionsNetoTyrique George — isolate this player's connectionsGeorgeJosh Acheampong — isolate this player's connectionsAcheampongLiam Delap — isolate this player's connectionsDelapLevi Colwill — isolate this player's connectionsColwillEnzo Fernández — isolate this player's connectionsFernándezJoão Pedro — isolate this player's connectionsPedroEmiliano Martínez — isolate this player's connectionsMartínezRoméo Lavia — isolate this player's connectionsLaviaPep Chavarría — isolate this player's connectionsChavarríaJorrel Hato — isolate this player's connectionsHatoEstêvão — isolate this player's connectionsEstêvãoAndrey Santos — isolate this player's connectionsSantos
width = passes between the paircolour = xT per pass: low → high

Top 10 combinations

Lacroix ↔ Colwill
67p · -0.3xT
Lacroix ↔ Acheampong
58p · 0.2xT
Martínez ↔ Colwill
50p · -0.2xT
Colwill ↔ Chavarría
49p · -0.3xT
Martínez ↔ Lacroix
45p · -0.4xT
Colwill ↔ Hato
44p · -0.5xT
Neto ↔ Palmer
42p · 0.5xT
Palmer ↔ Acheampong
35p · 0.5xT
Rogers ↔ Chavarría
32p · 1.1xT
Rogers ↔ Hato
32p · 0.7xT
Vision AI Model Insight
Busiest connection: Maxence Lacroix ↔ Levi Colwill (67 passes). Most dangerous per pass: João Pedro ↔ Cole Palmer (0.05 xT/pass over 20 passes) — the combination that hurts opponents most when it fires, not just the one used most.

Touch Density — where Chelsea 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 Chelsea 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
3593
ACTIONS
4%4%12%5%4%8%9%14%10%9%6%4%3%3%6%PASSES
Carries
the ball moved with a player
874
ACTIONS
2%4%10%5%3%10%9%13%12%9%8%3%2%4%6%CARRIES
Vision AI Model Insight
Their single busiest zone is the centre of the middle third (14.0% of all actions). 63% of actions run through the inside lanes (centre + both half-spaces) versus 37% down the two wings — an inside-dominant, half-space build. By third: 27% defensive, 51% middle, 22% 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. Chelsea leaned on their right (37%). The bars under the pitch put the league’s share beside theirs.

37%Left side26%Middle37%Right sideATTACKING →
Chelsea
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. Chelsea made 300 this season (37.5 a match); 34% 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.

Left wing: 86 entries (29%)8629%LEFT WINGLeft half-space: 36 entries (12%)3612%LEFT HALF-SPACECentre: 40 entries (13%)4013%CENTRERight half-space: 36 entries (12%)3612%RIGHT HALF-SPACERight wing: 102 entries (34%)10234%RIGHT WINGATTACKING ↑
ChannelEntriesPer matchShareLeague share
Left wing8610.829%31%
Left half-space364.512%16%
Centre405.013%13%
Right half-space364.512%14%
Right wing10212.834%27%
Build-up2 sections

Build-Up — the pass map

The football question
How does Chelsea 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.

981build-up passes attempted
73%completed
27%reach the middle third
3%go long into the final third

Pass types

277CB · 39%
57Long Wide · 8%
14Long Central · 2%
262Wide → FB · 37%
106Central · 15%

Sequence success — build-up

84build-up sequences this season
12%succeed — reach the next third or a shot
37%turned over with nothing to show
CB277 · 39%
left: 149 passes, most from Colwill149right: 128 passes, most from Lacroix128
Long Wide57 · 8%
left: 37 passes, most from Martínez37right: 20 passes, most from Martínez20
Long Central14 · 2%
right: 9 passes, most from Martínez9left: 5 passes, most from Martínez5
Wide → FB262 · 37%
right: 140 passes, most from Martínez140left: 122 passes, most from Martínez122
Central106 · 15%
left: 61 passes, most from Martínez61right: 45 passes, most from Martínez45
◆ Vision AI Insight
Chelsea build predominantly down the left, with Colwill as the fulcrum (130 build-up passes). Only 30% 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 (54×).

Pass Explorer

Every pass of Chelsea’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.

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. 321 progressive passes across 8 matches (40.1/game).

Origin lane

Right wing
27%
Right half-space
11%
Central
22%
Left half-space
11%
Left wing
29%
Progression is left-dominant: 38% right side · 22% central · 40% left.

Who generates it

Emiliano Martínez
37 · 12%
Cole Palmer
27 · 8%
Morgan Rogers
26 · 8%
Pedro Neto
25 · 8%
Pep Chavarría
23 · 7%
Top two — Emiliano Martínez & Cole Palmer — account for 20% of all progression.
PROGRESSION
40.1 prog passes/game
mostly left
→
PENETRATION
24% reach final third
24% reach the box
→
THREAT
the other 76% is recycled
progress ≠ danger
What came of it. 24% of progressive passes reached the final third and 24% the box; the rest was recycled in front of the block. 20% 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
Centralbalanced
Left half-spacebalanced
Left wingbalanced

Progression — the pass map

The football question
Once Chelsea 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.

1810progression passes attempted
86%completed
14%break into the final third
9%go backward into the defensive third

Pass types

443CB · 28%
62Long Wide · 4%
8Long Central · 1%
746Wide → FB · 48%
303Central · 19%

Sequence success — progression

127progression sequences this season
29%succeed — reach the next third or a shot
43%turned over with nothing to show
CB443 · 28%
left: 233 passes, most from Colwill233right: 210 passes, most from Lacroix210
Long Wide62 · 4%
left: 35 passes, most from Chavarría35right: 27 passes, most from James27
Long Central8 · 1%
left: 6 passes, most from Palmer6right: 2 passes, most from James2
Wide → FB746 · 48%
right: 375 passes, most from Acheampong375left: 371 passes, most from Henderson371
Central303 · 19%
right: 156 passes, most from James156left: 147 passes, most from Palmer147
◆ Vision AI Insight
Chelsea progress predominantly down the left, with Colwill the busiest midfield outlet (179 progression passes). Only 14% of passes that start in midfield actually break the final-third line — the rest (77%) is recycled back into midfield or (9%) 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 (86×) — 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.

Carries Map — progressive ball-carries, squad-wide

The question
Where does Chelsea actually progress the ball with their feet, not a pass?

193 progressive carries this season across the whole squad (24% of 794 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 44m. Most active carrier: Palmer (31). Filter to one player below.

First (59)Second (47)Third (46)Fourth (41)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 Chelsea'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.

107shots
9.4xG
0.09xG per shot
5big chances (0.3+ xG)
17goals
#10key passes / game in the league
#7box entries / game in the league
Attacking transition: 31 shots (29%)29%31Settled attack: 55 shots (51%)51%55Set piece: 21 shots (20%)20%21107SHOTS
Kind of attack, then how the shot was set upShotsGoalsxGxG / shot
Attacking transition 29% of shots3152.830.09
Cut-back510.430.09
Long ball100.080.08
Pass into the box / short pass2042.060.10
Own creation (carry, dribble, rebound)500.250.05
Settled attack 51% of shots55104.830.09
Cross620.650.11
Cut-back621.010.17
Long ball200.220.11
Pass into the box / short pass2741.920.07
Own creation (carry, dribble, rebound)1421.030.07
Set piece 20% of shots2121.750.08
Corner1411.590.11
Free kick710.160.02

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

PlayerSet-upsxG createdGoals
Cole Palmer181.763
Morgan Rogers101.301
Pedro Neto110.931
João Pedro70.553
Jorrel Hato40.422

Who takes them

PlayerShotsxGGoals
João Pedro162.255
Cole Palmer251.912
Morgan Rogers201.564
Pedro Neto141.353
Roméo Lavia20.771
Vision AI Model Insight
The biggest source of Chelsea's chances, by xG, is settled attack (4.8 of 9.4 xG, 55 of 107 shots), mostly pass into the box / short pass. 31 of the 107 shots (29%) came within 15 seconds of winning the ball. 21 of the 107 shots came from set pieces; 17 goals from 9.4 xG (+7.6).

Chance Creation — the pass map

The football question
How does Chelsea 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

3CB · 4%
6Long Wide · 8%
3Long Central · 4%
32Wide → FB · 42%
32Central · 42%

Sequence success — final-third

58final-third sequences this season
33%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
CB3 · 4%
left: 3 passes, most from Colwill3
Long Wide6 · 8%
right: 4 passes, most from Martínez4left: 2 passes, most from Martínez2
Long Central3 · 4%
right: 2 passes, most from Henderson2
Wide → FB32 · 42%
left: 16 passes, most from Pedro16right: 16 passes, most from Palmer16
Central32 · 42%
right: 18 passes, most from Neto18left: 14 passes, most from Neto14

Who creates — volume × threat

17Palmer
12Rogers
12Neto
7Pedro
6Henderson
4Hato
3Welbeck
76key passes #10 of 20
14.4box entries/game #7 of 20
29.1final-3rd entries/game #14 of 20
1.18xG/game #12 of 20
17goals #3 of 20
+7.6finishing vs xG #2 of 20
◆ Vision AI Insight
Chelsea lean right — 37% of their key passes come from that flank and its half-space (28 of 76), most often from Neto (10). They funnel chances into the c of the final third, most often via RHS att → C att (13 key passes, most often from Neto). Palmer is the primary source overall. They generate 1.18 xG a game and finish it clinical — they beat their xG, led by João Pedro (5G), Morgan Rogers (4G), Pedro Neto (3G).

Crosses & Cutbacks — squad-wide

The question
How does Chelsea deliver into the box from wide areas, and what comes of it?

110 crosses: 44 found a team-mate (40%), 6 led to a shot (5%). 36 cut-backs: 18 found a team-mate (50%), 11 led to a shot (31%). 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 Chelsea actually cross from?

110 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. 26% come from one territory, the advanced right wing; 6 come from the half-spaces and 0 from the centre.

deep right wing: 6 crosses (5%)5%6deep right half-space: 0 crosses (0%)deep centre: 0 crosses (0%)deep left half-space: 0 crosses (0%)deep left wing: 5 crosses (5%)5%5advanced right wing: 29 crosses (26%)26%29advanced right half-space: 3 crosses (3%)3%3advanced centre: 0 crosses (0%)advanced left half-space: 0 crosses (0%)advanced left wing: 21 crosses (19%)19%21byline right wing: 25 crosses (23%)23%25byline right half-space: 1 crosses (1%)1%1byline centre: 0 crosses (0%)byline left half-space: 2 crosses (2%)2%2byline left wing: 18 crosses (16%)16%18
TerritoryCrossesShareLed to a shot
advanced · right wing2926%2
byline · right wing2523%0
advanced · left wing2119%1
byline · left wing1816%3
deep · right wing65%0
deep · left wing55%0
advanced · right half-space33%0
byline · left half-space22%0
byline · right half-space11%0
Finishing3 sections

Shot Map

107 shots for 9.4 xG (1.18/game, #12 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.

Moderate attack. 13.4 shots/game at 0.088 xG/shot. Box entries 14/game (#7). Threat created 9.8 xT/game (#12).

Goal Sequences

The move behind each of Chelsea’s 17 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 — 17 goals traced.

How Do They Score?

The question
Where do the goals actually come from — open play, dead balls, which foot?

Every goal Chelsea scored across the loaded matches, broken down by how it was created and finished.

By situation

Open play
15 · 88%
Set pieces
2 · 12%
Penalties
0 · 0%

By finish

Right foot
11 · 65%
Left foot
6 · 35%
Header
0 · 0%
Vision AI Model Insight
17 goals scored. 88% come in open play; set pieces add 12%. 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

49 of 132 pass attempts lost here (37%).37%49/132Acheampong26 of 127 pass attempts lost here (20%).20%26/127Lacroix113 of 422 pass attempts lost here (27%).27%113/422Martínez23 of 172 pass attempts lost here (13%).13%23/172Lacroix54 of 128 pass attempts lost here (42%).42%54/128Chavarría53 of 293 pass attempts lost here (18%).18%53/293Acheampong41 of 338 pass attempts lost here (12%).12%41/338Pedro60 of 512 pass attempts lost here (12%).12%60/512Henderson45 of 357 pass attempts lost here (13%).13%45/357Rogers49 of 310 pass attempts lost here (16%).16%49/310Chavarría49 of 224 pass attempts lost here (22%).22%49/224Neto45 of 148 pass attempts lost here (30%).30%45/148Neto20 of 99 pass attempts lost here (20%).20%20/99Palmer37 of 111 pass attempts lost here (33%).33%37/111Rogers69 of 220 pass attempts lost here (31%).31%69/220Rogersattacking →

How they lose it

Incomplete pass — attacking third
220
Failed forward pass
131
Failed take-on
70
Lost in the defensive third
265
Their weakest zone is the left wing in the defensive third: 42% of 128 pass attempts there are lost (54 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.

Pressing2 sections

Pressing — the full picture

The football question
How hard does Chelsea 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.

44
Pressing Index
/100 · average
Intensity30
PPDA, high-press share, high-zone actions
Height50
how high up the pitch they defend
Effectiveness50
high recoveries & high-regain share
Disruption50
opponent completion & progression allowed

Every defensive action & where it happens

Where they win the ball back — by lane

RW
26%
RHS
16%
C
21%
LHS
15%
LW
22%
By third (high / mid / low regain)
Def 47%
Mid 38%
Att 14%
Read
Ranked #15 of 20 for press intensity, this is a high press. 53% of defensive actions happen in the middle & attacking thirds, at an average height of 39. Of recoveries, 56% are won high up the pitch.

Every pressing metric, ranked in the league

metricvaluerankmeaning
PPDA (opp passes per def action)—#15/20lower = more intense; rank is what matters
Defensive actions / game151#17/20
High-press share23%#12/20def actions won high up
Defensive-action height (0–100)38.8#10/20avg pitch height of actions
High recoveries / game20.1#13/20ball won in mid+att third
High-recovery share56%#7/20share of recoveries won high
Opponent completion allowed82%#14/20lower = more disruptive
Opponent progressive allowed8.4%#6/20of 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 Chelsea win it back with a defensive action?

Counted over the 227 opposition possessions that Chelsea 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 Chelsea step in. Chelsea win it after 5.5 opponent passes in the first half-hour and after 4.8 after the hour mark (sooner).

PASSES THEY GET
5.3passes
median 3 · before Chelsea win it
TIME THEY KEEP IT
15sec
median 9s · 28.4 wins per game
How many passes the opponent got in before the ball was won
36%
27%
21%
16%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
4.7
14s
15–30′
6.6
18s
30–45+′
6.1
17s
45–60′
5.2
14s
60–75′
4.9
12s
75–90+′
4.7
12s
average passesaverage seconds

Attitude in each third

Every other style number here says how much Chelsea 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 Chelsea 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) 981 passes

riskier

Width of use-0.7519.44
Wide-channel share-0.340.28
Side bias+0.170.78
Pass length+0.6925.18
Long-ball share+0.650.29
Forward gain per pass+0.7314.62
Backward-pass share-0.160.19
Deep-pass share+0.480.33
Completion-0.920.72
Seconds between passes+0.083.38
Side used

left 26% · central 50% · right 24%

Progression (middle third) 1,810 passes

narrow, central

Width of use-1.0023.45
Wide-channel share-0.750.35
Side bias+0.291.21
Pass length-0.5716.43
Long-ball share-0.190.09
Forward gain per pass-0.053.52
Backward-pass share-0.220.32
Deep-pass share-0.170.15
Completion+0.020.84
Seconds between passes-0.443.00
Side used

left 33% · central 37% · right 29%

Final third 802 passes

nothing unusual in this zone

Width of use-0.3230.82
Wide-channel share-0.290.56
Side bias-0.10-1.20
Pass length-0.2915.03
Long-ball share-0.430.08
Forward gain per pass-0.280.67
Backward-pass share+0.410.41
Deep-pass share-0.010.09
Completion+0.190.71
Seconds between passes-0.033.38
Side used

left 40% · central 15% · right 45%

Reference measured across roughly 3,500 match-teams in this competition, per third, so the comparison is with Chelsea'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 Chelsea 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 share15%
#18 of 20 · reaches final third in ≤3 passes
Patient build-up share33%
#2 of 20 · spells of 6+ passes
Tempo (possession length)21.5s
#18 of 20 · shorter = faster tempo
Passes per possession5.6
#2 of 20
Counter-attack shots /game1.13
#9 of 20 · shot within 12s of a regain in own half
Press→shot /game2.91
#10 of 20 · shot within 8s of a high regain
Speed to final third after regain13.7s
#16 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

29defensive transition sequences this season
66%succeed — reach the next third or a shot
14%turned over with nothing to show
Vision AI Model Insight
Chelsea are a patient build-up side: 15% of possessions reach the final third in 3 passes or fewer (#18 of 20), against 33% built patiently over 6+ passes (#2 of 20). Winning the ball back leads to a shot inside 12 seconds 1.13 times a game (#9), and a high regain converts to a shot within 8 seconds 2.91 times a game (#10 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 Chelsea? 353 opponent progressive passes and 101 shots faced (10.6 xG against) across the season.

Opponent threat by lane (attacking into Chelsea)

Right wing
20%
Right half-space
15%
Central
17%
Left half-space
14%
Left wing
35%
Where the danger came from. Opponents generated most of their threat through the left wing (35% of the danger conceded).

How Do They Concede?

The question
Where do the goals against Chelsea actually come from — open play, dead balls, and who supplies them?

Every goal Chelsea conceded across the loaded matches — the opponent's shots in Chelsea'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

← opponent shot at own goal

Every opponent key pass against them (69 total)

3%1%1%3%3%3%7%19%16%17%26%Sander BergeJosh KingTimothy CastagneTimothy CastagneOscar BobbSander BergeAntonee RobinsonJorge CuencaJorge CuencaRodrigo MunizCharalampos KostoulasDiego GómezMalick YalcouyéPascal GroßFerdi KadiogluOlivier BoscagliPascal GroßIgor ThiagoKeane Lewis-PotterJaidon AnthonyJannik SchusterKristoffer AjerMikkel DamsgaardYehor YarmoliukMaxim De CuyperMartin ØdegaardMartin ØdegaardMartin ØdegaardChristos TzolisKai HavertzMyles Lewis-SkellyDeclan RiceChristos TzolisKai HavertzChristos TzolisBukayo SakaChristos TzolisMartin ØdegaardKai HavertzMarcus TavernierRegan SlaterRyan GilesMohamed BelloumiRegan SlaterOliver McBurnieSorba ThomasLewie CoyleRyan GilesMohamed-Ali ChoAdrien TruffertAdrien TruffertRyan ChristieBen Gannon-DoakBen Gannon-DoakKiernan Dewsbury-HallJack GrealishJames TarkowskiVitalii MykolenkoVitalii MykolenkoKiernan Dewsbury-HallMathys TelAndy RobertsonMathys TelSandro TonaliMateus FernandesMateus FernandesJan Paul van HeckeMohammed KudusPedro Porro← own goal

By situation

Open play
10 · 77%
Set pieces
3 · 23%
Penalties
0 · 0%
Vision AI Model Insight
13 goals conceded. 77% come in open play; set pieces account for only 23%, so dead-ball defending is not the main issue. 23% are headers — aerial defending is not where the damage comes from. Their supply line runs mainly through the left wing of the attacking third (26% of key passes faced) — that is the zone to shut off.
Context

Shape and tactics

Set pieces, strengths and weaknesses, and who they resemble.

Premium
Tactical Phase Read
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.
Subscribe →

Set-Piece Threat

How much of the goal threat is manufactured from dead balls — a route to goal that bypasses their open-play mechanism entirely.

Open play
15 g · 7.7 xG
Set pieces
2 g · 1.8 xG
Penalties
0 g
12% of goals come from set pieces (2 of 17), worth 1.8 xG from 21 shots. Set pieces are a secondary threat; the danger is in open play.

Where do Chelsea's corners go?

45 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 (17)

35%6%18%18%12%6%
Delivered into the box 94%Short 6%

Left side (28)

4%7%11%4%46%14%
Delivered into the box 86%Short 14%

Where opponents attack Chelsea from set pieces

12 shots from opponent corners against Chelsea, 3 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 (8)

12%38%38%12%
Shot 8Goals 2

Left flag (4)

25%25%50%
Shot 4Goals 1

Tactical Strengths & Weaknesses

Strengths (top-third vs league)
  • none stand out
Weaknesses (bottom-third vs league)
  • press intensity (PPDA) — 5.61 (25th pct)
  • field tilt % — 46.87 (30th pct)

Style Archetype & Similar Teams

Team archetype: Direct / counter high-press. Their closest stylistic matches in the league (cosine similarity on style metrics) — sides that play a similar way.

Aston Villa
69%
Coventry
62%
Everton
57%
Hull City
51%
Ipswich
47%

Performance vs Opponent Styles

The question
Not just how Chelsea 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 Chelsea's own passing behaviour to the result.

Results

opponent typegoals for/ggoals against/gpoints/g
vs high-pressing opponents (2m)2.002.501.50
vs mid-block opponents (2m)2.002.001.50
vs low-block opponents (4m)2.251.501.75

Behaviour — how Chelsea actually play against each

opponent typeretentiondirectness (long balls)prog. passes/gxT/gbox entries/g
vs high-pressing opponents69%20%306.914.0
vs mid-block opponents80%16%4210.214.0
vs low-block opponents82%11%4411.114.8
Vision AI Model Insight
Facing a high press, retention drops to 69% vs 82% against a low block (13pt lower under pressure), and directness (long-ball share) sits at 20% when pressed vs 11% 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. Chelsea are balanced-territory with a aggressive high-press. Their attack creates 1.18 xG a game and they concede 1.33 a game. [Event-data based — no tracking, so off-ball structure and marking scheme need video.]
Squad

Players

Who drives the numbers.

Against Their Own Norm

For each of ten match numbers, the one match this season that departed furthest from Chelsea’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.

Chelsea
across 8 matches this season
Threat from passingvs Hull City · 2-2 · 2026-09-1210.25avg 5.09
Expected goalsvs Brighton · 4-3 · 2026-08-302.63avg 1.18
Pass accuracyvs Brighton · 4-3 · 2026-08-3066%avg 78%
Progressive passesvs Hull City · 2-2 · 2026-09-1266avg 40
Defensive actionsvs Hull City · 2-2 · 2026-09-12126avg 151
Progressive carriesvs Hull City · 2-2 · 2026-09-1267avg 40
PPDAvs Brighton · 4-3 · 2026-08-309.3avg 5.6
Field tiltvs Hull City · 2-2 · 2026-09-1270%avg 47%

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 Chelsea’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).

+11.3on-ball value, 8 matches
Where the value came from
Passes3593 actions+3.34
Carries794 actions+1.53
Dribbles128 actions-0.13
Shots107 actions+4.07
Ball wins433 actions+2.47
Most on-ball value (OBV)ActionsOBVper 100
Pedro Neto342+2.26+0.66
Cole Palmer442+2.16+0.49
Morgan Rogers385+1.63+0.42
João Pedro207+1.38+0.67
Maxence Lacroix429+0.85+0.20
Emiliano Martínez249+0.84+0.34
Roméo Lavia183+0.45+0.25
Levi Colwill475+0.40+0.09
123456
Biggest single movesOBV
1  João Pedro · shot+0.295
2  Pep Chavarría · pass+0.270
3  João Pedro · shot+0.266
4  Pedro Neto · pass+0.261
5  Cole Palmer · shot+0.253
6  Cole Palmer · ball win+0.244

Set Pieces by Player

Who delivers Chelsea’s dead balls and who finishes them this season. 45 corners taken, most by Jordan Henderson (15); 21 shots from corners and free kicks, 2 goals, led by Roméo Lavia (1 goals, 0.77 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.

PlayerCorners takenInto the boxSet-piece shotsGoalsxGFree-kick shotsPenalties
Reece James1010 (100%)5—0.123—
Jordan Henderson1511 (73%)210.052—
Pedro Neto138 (62%)——0.00——
Cole Palmer10 (0%)2—0.041—
Morgan Rogers10 (0%)2—0.051—
Malo Gusto33 (100%)1—0.03——
Roméo Lavia——210.77——
João Pedro——2—0.24——
Jorrel Hato——1—0.03——
Valentín Barco——1—0.04——
Geovany Quenda——1—0.03——
Moisés Caicedo——1—0.04——
Maxence Lacroix——1—0.30——
Estêvão21 (50%)——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.

playerposminVision AI Scorecapability blocks
Emiliano MartínezGK5507.0Shot-Stopping 70%
João PedroST5417.0Ball-Carrying 75%Finishing 69%Box Threat 59%Aerial Threat 0%
Maxence LacroixCB6856.4Ball-Winning 61%Ball-Carrying 49%Aerial & Box Defending 42%Progression 26%
Cole PalmerAM7505.5Box Threat 95%Ball-Carrying 64%Chance Creation 37%
Pedro NetoW6075.2Ball-Carrying 84%Chance Creation 50%Wide Delivery 42%Box Threat 31%
Morgan RogersW7373.9Box Threat 60%Chance Creation 38%Ball-Carrying 38%Wide Delivery 21%
Levi ColwillCB6523.5Progression 54%Ball-Carrying 46%Ball-Winning 44%Aerial & Box Defending 24%
Josh AcheampongFB5373.3Ball-Winning 44%Ball-Carrying 34%Wide Delivery 22%

Top Creators

Chance creation leaders — key passes /90 and box entries.

playerposminkey P /90box entriesxT
Cole PalmerM7502.04187.6
Pedro NetoM6071.78185.2
Morgan RogersM7371.47175.0
João PedroF5411.1682.2
Jordan HendersonM3911.3884.2
Jorrel HatoM4180.8673.5
Reece JamesM3120.5875.7
Malo GustoM2900.3163.5

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.

playerposmincrosses (completed)cutbacks (completed)
Pedro NetoW60710 (20%)10 (60%)
Morgan RogersW7375 (40%)10 (40%)
Josh AcheampongFB5375 (60%)2 (0%)
EstêvãoW852 (50%)1 (100%)

Defensive Volume Leaders

Who does the defensive work — tackles, interceptions, recoveries.

playerposmindef actionstacklesinterceptrecoveries
Maxence LacroixD68513213919
Morgan RogersM73712712140
Levi ColwillD6521107425
Pedro NetoM6079114528
João PedroF541842011
Josh AcheampongD5378217216
Wesley FofanaD3168271111
Cole PalmerM7508010231

Full Squad Data

Every player with 100+ minutes (20 players).

playerposappsmingoalsxGkey PprogdefxT
Cole PalmerM875021.91727807.6
Morgan RogersM873741.612261275.0
Maxence LacroixD868500.41151325.2
Levi ColwillD765200.01211106.6
Pedro NetoM860731.31225915.2
Emiliano MartínezG655000.0337107.5
João PedroF654152.379842.2
Josh AcheampongD753700.0015823.5
Jorrel HatoM641800.0418643.5
Jordan HendersonM439120.3616364.2
Pep ChavarríaM633100.0223483.9
Wesley FofanaD431600.0116824.2
Reece JamesM431200.3222385.7
Roméo LaviaM629510.825562.0
Malo GustoM529000.1116413.5
Moisés CaicedoM427300.114451.6
Danny WelbeckF613200.030100.4
Valentín BarcoM312700.103221.4
Mike PendersG212600.00641.3
Geovany QuendaM411200.003180.6

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.

Cole Palmer
Advanced creator · Attacking mid
minutes750
goals · xG2 · 1.9
prog passes /903.2
key passes /902.04
def actions /909.6
pass accuracy80%
Morgan Rogers
Ball-winner / destroyer · Midfielder
minutes737
goals · xG4 · 1.6
prog passes /903.2
key passes /901.47
def actions /9015.5
pass accuracy75%
Maxence Lacroix
Stopper / marker · Defender
minutes685
goals · xG0 · 0.4
prog passes /902.0
key passes /900.13
def actions /9017.3
pass accuracy90%
Levi Colwill
Stopper / marker · Defender
minutes652
goals · xG0 · 0.0
prog passes /902.9
key passes /900.14
def actions /9015.2
pass accuracy88%
Pedro Neto
Ball-winner / destroyer · Midfielder
minutes607
goals · xG3 · 1.3
prog passes /903.7
key passes /901.78
def actions /9013.5
pass accuracy75%
Emiliano Martínez
Goalkeeper · Goalkeeper
minutes550
goals · xG0 · 0.0
prog passes /906.1
key passes /900.49
def actions /901.6
pass accuracy64%
João Pedro
Ball-winner / destroyer · Midfielder
minutes541
goals · xG5 · 2.3
prog passes /901.5
key passes /901.16
def actions /9014.0
pass accuracy67%
Josh Acheampong
Stopper / marker · Defender
minutes537
goals · xG0 · 0.0
prog passes /902.5
key passes /900.00
def actions /9013.7
pass accuracy73%
Jorrel Hato
Ball-winner / destroyer · Midfielder
minutes418
goals · xG0 · 0.0
prog passes /903.9
key passes /900.86
def actions /9013.8
pass accuracy78%

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: press intensity (PPDA) (25th pct)
Sign a high-energy presser to lift defensive intensity. The data flags this because the press is passive (5.61, 25th percentile vs the league).
Need — bottom-third: field tilt % (30th pct)
Sign a possession-controlling midfielder to win the territorial battle. The data flags this because they get pinned back (46.87, 30th percentile vs the league).
Need — below-average: xG created /game (40th pct)
Sign a higher-volume goalscorer or chance creator. The data flags this because the attack under-produces xG (1.18, 40th percentile vs the league).
Vision AI · building report