Combining every season — this one is larger, give it a moment.
Team reportPremier League 2025/26
Chelsea
A possession-dominant side, aggressive high-press out of possession, with a high-volume attack. Finished #10 of 20 on 52 points (14-10-14, 58:52). 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 Chelsea and how good was the season?
Read. Chelsea finished #10 of 20 on 52 points (14W-10D-14L, 1.37 pts/game), scoring 58 and conceding 52 for a goal difference of +6.
On the ball they are a possession-dominant team (56% field tilt, #4 in the league); off it they defend with a aggressive high-press (#12 of 20 for press intensity).
Across the 38 matches of event data, they create 1.61 xG/game (#4) and concede 1.23 (#4), a net of +0.38 xG/game — the underlying performance diverges from 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.
#
team
P
W
D
L
GF
GA
GD
Pts
7
Sunderland
38
14
12
12
42
48
-6
54
8
Brighton
38
14
11
13
52
46
+6
53
9
Brentford
38
14
11
13
55
52
+3
53
10
Chelsea
38
14
10
14
58
52
+6
52
11
Fulham
38
15
7
16
47
51
-4
52
12
Newcastle
38
14
7
17
53
55
-2
49
13
Everton
38
13
10
15
47
50
-3
49
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)
Vision AI Model Insight
Chelsea tend to come alive after the break — first-half net xG +4.49, second-half +9.86. Their strongest period is 46-60' (net +5.66 xG); their most dangerous period to face is 0-15' (net -0.30 xG against them).
xG Race — the season, match by match
Expected goals piling up over 38 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 (+14.4 xG). Hover a point for that match.
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.
85
Build-Up
possession control from the back
60
Progression
moving the ball upfield with threat
65
Creation
manufacturing chances
60
Press
winning the ball high & hard
80
Solidity
resisting the opponent's threat
Genetic identity
Chelsea are defined most by their Build-Up DNA (85/100 · elite at possession control from the back), and least by their Progression DNA (60/100). 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
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.
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.
Vision AI Model Insight
Chelsea post 56% field tilt, ranking #4 of 20 in the league — a genuinely territory-dominant side, and their 1.37 points/game sits above the league-average return — territory is genuinely converting into results.
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.
Phase
Share of the time
Build-up
27.4%
Progression
46.9%
Chance creation
24.1%
Attacking transition
1.6%
Vision AI Model Insight
Progression is where Chelsea spends most of its time on the ball (47% 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
Fernández ↔ Caicedo
498p · 15.5xT
Chalobah ↔ Sánchez
454p · 11.8xT
Chalobah ↔ Caicedo
416p · 10.0xT
Chalobah ↔ Cucurella
387p · 10.4xT
Cucurella ↔ Fernández
366p · 18.1xT
Chalobah ↔ James
334p · 8.7xT
Chalobah ↔ Fofana
306p · 2.8xT
Chalobah ↔ Fernández
297p · 12.2xT
Fofana ↔ Caicedo
297p · 7.8xT
James ↔ Caicedo
296p · 7.6xT
Vision AI Model Insight
Busiest connection: Enzo Fernández ↔ Moisés Caicedo (498 passes). Most dangerous per pass: Pedro Neto ↔ Enzo Fernández (0.07 xT/pass over 198 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 380 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
22015
ACTIONS
Carries
the ball moved with a player
5162
ACTIONS
Vision AI Model Insight
Their single busiest zone is the centre of the middle third (13.7% of all actions). 67% of actions run through the inside lanes (centre + both half-spaces) versus 33% down the two wings — an inside-dominant, half-space build. By third: 29% defensive, 49% 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 left (37%). The bars under the pitch put the league’s share beside theirs.
Chelsea
37%26%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. Chelsea made 1691 this season (44.5 a match); 29% 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
492
12.9
29%
29%
Left half-space
257
6.8
15%
16%
Centre
231
6.1
14%
12%
Right half-space
228
6.0
13%
14%
Right wing
483
12.7
29%
29%
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.
6548build-up passes attempted
84%completed
23%reach the middle third
1%go long into the final third
Pass types
3000CB · 55%
257Long Wide · 5%
36Long Central · 1%
1607Wide → FB · 29%
583Central · 11%
Sequence success — build-up
1112build-up sequences this season
10%succeed — reach the next third or a shot
42%turned over with nothing to show
CB3000 · 55%
Long Wide257 · 5%
Long Central36 · 1%
Wide → FB1607 · 29%
Central583 · 11%
◆ Vision AI Insight
Chelsea build predominantly down the left, with Sánchez as the fulcrum (1023 build-up passes). Only 25% 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 (448×).
Pass Explorer
Every pass of Chelsea’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. 1583 progressive passes across 38 matches (41.7/game).
Origin lane
Right wing
28%
Right half-space
15%
Central
17%
Left half-space
13%
Left wing
27%
Progression is right-dominant: 43% right side · 17% central · 40% left.
Who generates it
Pedro Neto
177 · 11%
Enzo Fernández
171 · 11%
Marc Cucurella
150 · 9%
Robert Sánchez
148 · 9%
Reece James
140 · 9%
Top two — Pedro Neto & Enzo Fernández — account for 22% of all progression.
PROGRESSION
41.7 prog passes/game
mostly right
→
PENETRATION
27% reach final third
20% reach the box
→
THREAT
the other 73% is recycled
progress ≠ danger
What came of it.27% of progressive passes reached the final third and 20% the box; the rest was recycled in front of the block. 22% 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 wingbalanced
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.
10694progression passes attempted
90%completed
13%break into the final third
11%go backward into the defensive third
Pass types
4507CB · 47%
201Long Wide · 2%
51Long Central · 1%
3519Wide → FB · 36%
1377Central · 14%
Sequence success — progression
1838progression sequences this season
25%succeed — reach the next third or a shot
39%turned over with nothing to show
CB4507 · 47%
Long Wide201 · 2%
Long Central51 · 1%
Wide → FB3519 · 36%
Central1377 · 14%
◆ Vision AI Insight
Chelsea progress predominantly down the right, with Caicedo the busiest midfield outlet (1233 progression passes). Only 13% of passes that start in midfield actually break the final-third line — the rest (75%) is recycled back into midfield or (11%) played backward, the hallmark of a side that carries progression itself through midfield rather than just passing through it. Their most-repeated pattern is C mid → LHS mid (631×) — 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)
11%
Progressive
1%
Neutral / sideways
48%
Safe / backward
40%
12% of passes are progressive-or-better; the remaining 88% keep possession without advancing it. A control-first side — safe in build-up, reliant on a few players for the incision.
Where does Chelsea actually progress the ball with their feet, not a pass?
1098 progressive carries this season across the whole squad (22% of 5102 tracked carries total), grouped into up to 4 spatial patterns by where each carry starts and ends together — not just its origin zone. Median 14m, longest 56m. Most active carrier: Neto (135). Filter to one player below.
First (314)Second (306)Third (303)Fourth (175)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.
509shots
61.2xG
0.12xG per shot
43big chances (0.3+ xG)
57goals
#5key passes / game in the league
#11box entries / game in the league
Kind of attack, then how the shot was set up
Shots
Goals
xG
xG / shot
Attacking transition24% of shots
122
8
13.02
0.11
Cross
9
1
1.66
0.18
Cut-back
9
2
1.87
0.21
Pass into the box / short pass
67
3
6.62
0.10
Own creation (carry, dribble, rebound)
37
2
2.86
0.08
Settled attack54% of shots
274
30
31.70
0.12
Cross
39
2
4.59
0.12
Cut-back
26
6
3.15
0.12
Long ball
13
2
1.32
0.10
Pass into the box / short pass
140
13
15.48
0.11
Own creation (carry, dribble, rebound)
56
7
7.16
0.13
Set piece22% of shots
113
19
16.53
0.15
Corner
83
9
9.58
0.12
Free kick
22
3
0.61
0.03
Penalty
8
7
6.35
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
Pedro Neto
43
5.15
6
Enzo Fernández
49
4.56
2
Alejandro Garnacho
28
3.73
4
Malo Gusto
27
3.36
3
Marc Cucurella
28
3.00
3
Who takes them
Player
Shots
xG
Goals
João Pedro
73
12.49
15
Enzo Fernández
72
10.03
10
Cole Palmer
64
9.83
10
Pedro Neto
46
4.60
5
Liam Delap
36
4.51
1
Vision AI Model Insight
The biggest source of Chelsea's chances, by xG, is settled attack (31.7 of 61.2 xG, 274 of 509 shots), mostly pass into the box / short pass. 122 of the 509 shots (24%) came within 15 seconds of winning the ball. 113 of the 509 shots came from set pieces; 57 goals from 61.2 xG (-4.2).
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
67CB · 19%
14Long Wide · 4%
13Long Central · 4%
103Wide → FB · 30%
152Central · 44%
Sequence success — final-third
1295final-third sequences this season
26%succeed — produce a shot
35%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
CB67 · 19%
Long Wide14 · 4%
Long Central13 · 4%
Wide → FB103 · 30%
Central152 · 44%
Who creates — volume × threat
64Fernández
52Neto
33Cucurella
26Gusto
26Garnacho
25James
24Palmer
349key passes #5 of 20
13.1box entries/game #11 of 20
35.7final-3rd entries/game #6 of 20
1.61xG/game #4 of 20
57goals #5 of 20
-4.2finishing vs xG #17 of 20
◆ Vision AI Insight
Chelsea lean left — 37% of their key passes come from that flank and its half-space (129 of 349), most often from Cucurella (28). They funnel chances into the c of the final third, most often via LHS att → C att (51 key passes, most often from Garnacho). Fernández is the primary source overall. They generate 1.61 xG a game and finish it wasteful — they under-perform their xG, led by João Pedro (15G), Enzo Fernández (10G), Cole Palmer (10G).
Crosses & Cutbacks — squad-wide
The question
How does Chelsea deliver into the box from wide areas, and what comes of it?
417 crosses: 149 found a team-mate (36%), 49 led to a shot (12%). 118 cut-backs: 57 found a team-mate (48%), 40 led to a shot (34%). 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?
417 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. 24% come from one territory, the advanced right wing; 35 come from the half-spaces and 0 from the centre.
Territory
Crosses
Share
Led to a shot
advanced · right wing
102
24%
13
byline · left wing
85
20%
10
byline · right wing
84
20%
7
advanced · left wing
79
19%
7
deep · right wing
17
4%
4
deep · left wing
15
4%
1
advanced · right half-space
11
3%
3
byline · right half-space
9
2%
2
advanced · left half-space
6
1%
0
byline · left half-space
5
1%
2
deep · right half-space
4
1%
0
Finishing3 sections
Shot Map
509 shots for 61.2 xG (1.61/game, #4 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.
High-volume attack. 13.4 shots/game at 0.120 xG/shot. Box entries 13/game (#11). Threat created 11.1 xT/game (#6).
Goal Sequences
The move behind each of Chelsea’s 58 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 — 58 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
38 · 67%
Set pieces
12 · 21%
Penalties
7 · 12%
By finish
Right foot
23 · 40%
Left foot
24 · 42%
Header
10 · 18%
Vision AI Model Insight
57 goals scored. 67% come in open play; set pieces add 21%. 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
1125
Failed forward pass
746
Failed take-on
444
Lost in the defensive third
1065
Their weakest zone is the left half-space in the attacking third: 28% of 924 pass attempts there are lost (258 times). Separately, 35% 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 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.
52
Pressing Index /100 · average
Intensity40
PPDA, high-press share, high-zone actions
Height70
how high up the pitch they defend
Effectiveness62
high recoveries & high-regain share
Disruption35
opponent completion & progression allowed
Every defensive action & where it happens
Where they win the ball back — by lane
RW
18%
RHS
21%
C
26%
LHS
18%
LW
17%
By third (high / mid / low regain)
Def 49%
Mid 37%
Att 14%
Read
Ranked #12 of 20 for press intensity, this is a high press. 51% of defensive actions happen in the middle & attacking thirds, at an average height of 38. Of recoveries, 57% are won high up the pitch.
Every pressing metric, ranked in the league
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#12/20
lower = more intense; rank is what matters
Defensive actions / game
125
#17/20
High-press share
21%
#7/20
def actions won high up
Defensive-action height (0–100)
37.9
#6/20
avg pitch height of actions
High recoveries / game
22.3
#10/20
ball won in mid+att third
High-recovery share
57%
#5/20
share of recoveries won high
Opponent completion allowed
80%
#10/20
lower = more disruptive
Opponent progressive allowed
8.8%
#16/20
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 Chelsea win it back with a defensive action?
Counted over the 1196 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 5.6 opponent passes in the first half-hour and after 3.8 after the hour mark (sooner).
PASSES THEY GET
4.5passes
median 3 · before Chelsea win it
TIME THEY KEEP IT
13sec
median 6s · 31.5 wins per game
How many passes the opponent got in before the ball was won
46%
27%
14%
12%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
5.8
17s
15–30′
5.4
15s
30–45+′
5.0
14s
45–60′
3.9
11s
60–75′
3.9
11s
75–90+′
3.6
11s
average passesaverage seconds
Phases of play
Every moment of the match read twice — what Chelsea 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.
7,147phase segments analysed
11.0smedian time before the opposition engage
2.0their passes per defensive action when pressed
#6of 20 for share of defending spent pressing high
High press 9.0%Mid block 15.9%Low block 46.4%Unopposed 28.7%
What opponents do against Chelsea
The mirror image, and the part no other section can show: whether teams come
out to press Chelsea 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 6.1%Mid block 12.2%Low block 53.2%Unopposed 28.5%
Which phases actually produce
Phase
Segments
Avg length
Avg actions
Ends in shot
Turned over
Build-up
2,311
6.0s
3.6
0.1%
48.9%
Progression
2,960
8.0s
4.8
0.6%
38.5%
Chance creation
1,771
6.8s
3.8
26.1%
48.2%
Attacking transition
105
7.6s
4.8
6.7%
33.3%
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
821
0.1%
progression
581
0.0%
chance_creation
349
41.0%
progression → chance_creation
302
28.8%
build_up → progression
283
0.0%
build_up → progression → chance_creation
205
29.8%
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) 6,548 passes
narrow, central, played inside
Width of use-0.9818.98
Wide-channel share-0.910.25
Side bias-0.27-0.85
Pass length-0.5320.78
Long-ball share-0.560.17
Forward gain per pass-0.579.00
Backward-pass share+0.730.23
Deep-pass share-0.670.22
Completion+0.470.83
Seconds between passes-0.453.05
Side used
left 24% · central 50% · right 26%
Progression (middle third) 10,694 passes
narrow, central, played inside, circulating
Width of use-1.3222.77
Wide-channel share-1.250.32
Side bias-0.11-0.36
Pass length-0.8616.03
Long-ball share-0.870.07
Forward gain per pass-0.932.19
Backward-pass share+0.660.36
Deep-pass share-0.880.12
Completion+0.850.90
Seconds between passes-0.502.97
Side used
left 31% · central 37% · right 32%
Final third 4,773 passes
nothing unusual in this zone
Width of use-0.8029.64
Wide-channel share-0.740.52
Side bias+0.200.92
Pass length-0.3414.94
Long-ball share-0.790.06
Forward gain per pass-0.560.29
Backward-pass share+0.640.43
Deep-pass share-0.290.08
Completion+0.750.76
Seconds between passes-0.283.18
Side used
left 42% · central 19% · right 39%
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
#8 of 20 · shot within 12s of a regain in own half
Press→shot /game2.91
#9 of 20 · shot within 8s of a high regain
Speed to final third after regain13.7s
#17 of 20 · lower = faster break
Sequence success — attacking transition
117attacking transition sequences this season
18%succeed — produce a shot
38%turned over with nothing to show
Sequence success — defensive transition
362defensive transition sequences this season
67%succeed — reach the next third or a shot
9%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 (#8), and a high regain converts to a shot within 8 seconds 2.91 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 Chelsea? 1448 opponent progressive passes and 415 shots faced (46.9 xG against) across the season.
Opponent threat by lane (attacking into Chelsea)
Right wing
28%
Right half-space
16%
Central
20%
Left half-space
12%
Left wing
25%
Where the danger came from. Opponents generated most of their threat through the right wing (28% 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
Every opponent key pass against them (298 total)
By situation
Open play
38 · 75%
Set pieces
11 · 22%
Penalties
2 · 4%
Vision AI Model Insight
51 goals conceded. 75% come in open play; set pieces account for only 22%, so dead-ball defending is not the main issue. 25% are headers — aerial defending is not where the damage comes from. Their supply line runs mainly through the right half-space 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
38 g · 44.7 xG
Set pieces
12 g · 10.2 xG
Penalties
7 g
21% of goals come from set pieces (12 of 57), worth 10.2 xG from 105 shots. Set pieces are a secondary threat; the danger is in open play.
Where do Chelsea's corners go?
226 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 (105)
Delivered into the box 96%Short 4%
Left side (121)
Delivered into the box 92%Short 8%
Where opponents attack Chelsea from set pieces
62 shots from opponent corners against Chelsea, 11 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 (29)
Shot 29Goals 9
Left flag (33)
Shot 33Goals 2
Tactical Strengths & Weaknesses
Strengths (top-third vs league)
pass retention % — 85.33 (90th pct)
xG created /game — 1.61 (80th pct)
xG conceded /game — 1.23 (80th pct)
field tilt % — 55.79 (80th pct)
threat created /game — 11.06 (70th pct)
Weaknesses (bottom-third vs league)
none stand out
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
87%
Liverpool
67%
Aston Villa
67%
Brighton
63%
Arsenal
59%
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 type
goals for/g
goals against/g
points/g
vs high-pressing opponents (12m)
1.08
1.25
1.33
vs mid-block opponents (12m)
1.50
1.50
1.33
vs low-block opponents (14m)
1.93
1.36
1.43
Behaviour — how Chelsea actually play against each
opponent type
retention
directness (long balls)
prog. passes/g
xT/g
box entries/g
vs high-pressing opponents
82%
13%
36
9.2
9.2
vs mid-block opponents
85%
9%
43
11.1
12.2
vs low-block opponents
87%
8%
46
12.6
17.2
Vision AI Model Insight
Facing a high press, retention drops to 82% vs 87% against a low block (5pt lower under pressure), and directness (long-ball share) sits at 13% when pressed vs 8% 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 possession-dominant with a aggressive high-press.
Their attack creates 1.61 xG a game and they concede 1.23 a game.
[Event-data based — no tracking, so off-ball structure and marking scheme need video.]
For each of ten match numbers, the one match this season that departed furthest from 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 38 matches this season
PPDAat Brighton · 0-3 · 2026-04-2112.5avg 6.3
Passesvs Arsenal · 1-1 · 2025-11-30315avg 579
Pass accuracyvs Arsenal · 1-1 · 2025-11-3078%avg 85%
Final-third entriesvs West Ham · 3-2 · 2026-01-3176avg 44
Defensive actionsat Tottenham · 1-0 · 2025-11-01169avg 125
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 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).
+16.3on-ball value, 12 matches
Where the value came from
Passes7038 actions+8.00
Carries1671 actions-1.97
Dribbles204 actions-0.34
Shots153 actions+7.13
Ball wins636 actions+3.50
Most on-ball value (OBV)
Actions
OBV
per 100
Cole Palmer
659
+3.55
+0.54
Enzo Fernández
1020
+1.98
+0.19
Pedro Neto
455
+1.64
+0.36
Trevoh Chalobah
623
+1.26
+0.20
João Pedro
318
+1.09
+0.34
Alejandro Garnacho
313
+1.01
+0.32
Malo Gusto
694
+0.97
+0.14
Robert Sánchez
508
+0.58
+0.11
Biggest single moves
OBV
1 Cole Palmer · shot
+0.732
2 Malo Gusto · pass
+0.603
3 Trevoh Chalobah · pass
+0.559
4 Alejandro Garnacho · pass
+0.481
5 João Pedro · shot
+0.406
6 Mamadou Sarr · shot
+0.362
Set Pieces by Player
Who delivers Chelsea’s dead balls and who finishes them this season. 226 corners taken, most by Reece James (81); 105 shots from corners and free kicks, 12 goals, led by João Pedro (3 goals, 2.79 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
Enzo Fernández
55
40 (73%)
17
1
1.16
8
2/2
Reece James
81
62 (77%)
9
2
0.32
7
—
Pedro Neto
71
57 (80%)
3
1
0.13
1
—
Cole Palmer
4
2 (50%)
9
—
0.30
6
5/6
João Pedro
—
—
12
3
2.79
—
—
Trevoh Chalobah
—
—
11
3
1.30
—
—
Moisés Caicedo
—
—
8
1
0.58
—
—
Marc Cucurella
1
0 (0%)
7
—
0.46
—
—
Wesley Fofana
—
—
7
—
0.63
—
—
Estêvão
14
8 (57%)
1
—
0.03
—
—
Alejandro Garnacho
—
—
4
—
0.27
—
—
Jamie Gittens
—
—
3
—
0.11
—
—
Josh Acheampong
—
—
2
—
0.10
—
—
Tosin Adarabioyo
—
—
2
—
0.08
—
—
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
Enzo Fernández
M
3314
1.74
93
52.3
Pedro Neto
M
2775
1.69
78
30.7
Marc Cucurella
D
2980
1.00
35
32.2
Reece James
D
2122
1.06
40
35.0
João Pedro
F
2789
0.74
40
10.7
Malo Gusto
D
2393
0.98
36
28.9
Alejandro Garnacho
M
1350
1.73
36
8.1
Cole Palmer
M
2060
1.05
36
18.7
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)
Estêvão
W
905
8 (50%)
6 (33%)
Defensive Volume Leaders
Who does the defensive work — tackles, interceptions, recoveries.
player
pos
min
def actions
tackles
intercept
recoveries
Trevoh Chalobah
D
3040
523
35
35
119
Moisés Caicedo
M
3028
438
87
59
177
Marc Cucurella
D
2980
431
55
32
113
Wesley Fofana
D
1880
384
19
40
81
Enzo Fernández
M
3314
366
53
21
140
Malo Gusto
D
2393
325
53
31
122
Reece James
D
2122
299
48
33
102
João Pedro
F
2789
298
27
7
72
Full Squad Data
Every player with 100+ minutes (25 players).
player
pos
apps
min
goals
xG
key P
prog
def
xT
Robert Sánchez
G
35
3346
0
0.0
5
148
90
36.9
Enzo Fernández
M
36
3314
10
10.0
64
171
366
52.3
Trevoh Chalobah
D
34
3040
3
1.5
4
70
523
30.4
Moisés Caicedo
M
33
3028
3
1.6
15
96
438
35.7
Marc Cucurella
D
34
2980
1
1.9
33
150
431
32.2
João Pedro
F
35
2789
15
12.5
23
31
298
10.7
Pedro Neto
M
34
2775
5
4.6
52
177
196
30.7
Malo Gusto
D
34
2393
2
1.2
26
138
325
28.9
Reece James
D
29
2122
2
1.0
25
140
299
35.0
Cole Palmer
M
26
2060
10
9.8
24
73
160
18.7
Wesley Fofana
D
25
1880
0
0.7
2
62
384
22.7
Andrey Santos
M
27
1371
1
1.9
8
37
184
14.0
Alejandro Garnacho
M
24
1350
1
3.0
26
59
103
8.1
Jorrel Hato
D
22
1258
0
0.3
7
50
210
12.6
Liam Delap
F
28
1231
1
4.5
3
5
114
2.0
Estêvão
M
22
905
2
3.0
14
40
88
6.6
Tosin Adarabioyo
D
16
877
0
0.1
1
31
175
13.0
Josh Acheampong
D
17
736
1
0.4
2
19
98
5.8
Jamie Gittens
M
16
562
0
0.8
9
23
48
2.8
Benoît Badiashile
D
8
512
0
0.0
0
14
80
6.6
Roméo Lavia
M
12
441
0
0.2
1
15
46
5.0
Filip Jörgensen
G
5
416
0
0.0
0
11
7
3.1
Marc Guiu
F
8
296
0
1.8
3
0
22
0.2
Levi Colwill
D
3
253
0
0.0
0
13
42
3.9
Mamadou Sarr
D
3
115
0
0.2
0
1
13
0.4
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.
Robert Sánchez
Goalkeeper · Goalkeeper
minutes3346
goals · xG0 · 0.0
prog passes /904.0
key passes /900.13
def actions /902.4
pass accuracy70%
Enzo Fernández
Creative forward · Forward
minutes3314
goals · xG10 · 10.0
prog passes /904.6
key passes /901.74
def actions /909.9
pass accuracy83%
Trevoh Chalobah
Stopper / marker · Defender
minutes3040
goals · xG3 · 1.5
prog passes /902.1
key passes /900.12
def actions /9015.5
pass accuracy93%
Moisés Caicedo
Ball-winner / destroyer · Midfielder
minutes3028
goals · xG3 · 1.6
prog passes /902.9
key passes /900.45
def actions /9013.0
pass accuracy92%
Marc Cucurella
Ball-playing defender · Defender
minutes2980
goals · xG1 · 1.9
prog passes /904.5
key passes /901.00
def actions /9013.0
pass accuracy86%
João Pedro
Poacher / finisher · Forward
minutes2789
goals · xG15 · 12.5
prog passes /901.0
key passes /900.74
def actions /909.6
pass accuracy74%
Pedro Neto
Advanced creator · Attacking mid
minutes2775
goals · xG5 · 4.6
prog passes /905.7
key passes /901.69
def actions /906.4
pass accuracy77%
Malo Gusto
Ball-playing defender · Defender
minutes2393
goals · xG2 · 1.2
prog passes /905.2
key passes /900.98
def actions /9012.2
pass accuracy84%
Reece James
Ball-playing defender · Defender
minutes2122
goals · xG2 · 1.0
prog passes /905.9
key passes /901.06
def actions /9012.7
pass accuracy84%
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 — below-average: press intensity (PPDA) (40th pct)
Sign a high-energy presser to lift defensive intensity. The data flags this because the press is passive (6.26, 40th percentile vs the league).
Need — below-average: box entries /game (45th pct)
Sign a penetrative wide player or runner to create box entries. The data flags this because few entries into the box (13.13, 45th percentile vs the league).
Need — below-average: progression /game (50th pct)
Sign a progressive passer / ball-carrier to break lines. The data flags this because progression is limited (41.66, 50th percentile vs the league).
← BACK TO SEARCHSeason
