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Team reportSerie A 2025/26

Pisa

A territorially-dominated side, aggressive high-press out of possession, with a moderate attack. Finished #20 of 20 on 18 points (2-12-24, 26:71). Tactical profile below is built from 38 matches of ball-by-ball event data, contextualised against the league.

territorially-dominatedaggressive high-pressmoderate attack
D
L
L
L
L
W
L
L
L
L
L
L
L
L
L
18
points
#20 of 20 · 38 games
26:71
goals
-45 GD
2-12-24
record
W-D-L
0.47
points/game
#20 in the league
1.02
xG for/game
rank #18
1.44
xG against/game
rank #19
#16
press rank
of 23 · most aggressive
39%
field tilt
#21
Pisa
League average
Expected goals for / game
1.021.27
Expected goals against / game
1.441.32
Field tilt
39%50%
Pass retention
75%81%
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 Pisa and how good was the season?
Read. Pisa finished #20 of 20 on 18 points (2W-12D-24L, 0.47 pts/game), scoring 26 and conceding 71 for a goal difference of -45. On the ball they are a territorially-dominated team (39% field tilt, #21 in the league); off it they defend with a aggressive high-press (#16 of 23 for press intensity). Across the 38 matches of event data, they create 1.02 xG/game (#18) and concede 1.44 (#19), a net of -0.43 xG/game — the underlying performance diverges from the actual goal difference.

Season Context — the league table

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

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

#teamPWDLGFGAGDPts
17Lecce38108202850-2238
18Cremonese38810203257-2534
19Hellas Verona38312232561-3621
20Pisa38212242671-4518

Match Momentum — when they create and concede danger

The question
Across a whole game, which periods does Pisa 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+'-5.34
Vision AI Model Insight
Pisa tend to fade after a strong start — first-half net xG -6.34, second-half -9.91. Their strongest period is 0-15' (net -0.34 xG); their most dangerous period to face is 31-45' (net -5.34 xG against them).

xG Race — the season, match by match

Expected goals piling up over 38 matches: Pisa 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 (-16.2 xG). Hover a point for that match.

020395938.654.8Atalanta (A) 1-1 · xG 0.82 to 1.66Roma (H) 0-1 · xG 0.47 to 0.74Udinese (H) 0-1 · xG 0.70 to 1.14Napoli (A) 2-3 · xG 2.53 to 1.67Fiorentina (H) 0-0 · xG 0.88 to 1.00Bologna (A) 0-4 · xG 0.08 to 2.69Hellas Verona (H) 0-0 · xG 0.58 to 1.19AC Milan (A) 2-2 · xG 1.39 to 1.58Lazio (H) 0-0 · xG 0.78 to 0.54Torino (A) 2-2 · xG 1.85 to 1.23Cremonese (H) 1-0 · xG 0.94 to 0.94Sassuolo (A) 2-2 · xG 1.51 to 1.90Inter (H) 0-2 · xG 1.76 to 1.77Parma Calcio 1913 (H) 0-1 · xG 1.67 to 1.23Lecce (A) 0-1 · xG 0.36 to 2.04Cagliari (A) 2-2 · xG 2.28 to 1.03Juventus (H) 0-2 · xG 0.52 to 1.75Genoa (A) 1-1 · xG 0.82 to 0.68Como (H) 0-3 · xG 1.59 to 1.73Udinese (A) 2-2 · xG 1.59 to 1.94Atalanta (H) 1-1 · xG 1.30 to 1.64Inter (A) 2-6 · xG 0.32 to 4.95Sassuolo (H) 1-3 · xG 0.85 to 1.86Hellas Verona (A) 0-0 · xG 0.79 to 0.07AC Milan (H) 1-2 · xG 0.53 to 1.67Fiorentina (A) 0-1 · xG 0.35 to 0.97Bologna (H) 0-1 · xG 0.33 to 0.52Juventus (A) 0-4 · xG 0.41 to 2.42Cagliari (H) 3-1 · xG 2.32 to 1.26Como (A) 0-5 · xG 0.48 to 1.63Torino (H) 0-1 · xG 0.45 to 1.63Roma (A) 0-3 · xG 0.83 to 1.25Genoa (H) 1-2 · xG 1.08 to 1.71Parma Calcio 1913 (A) 0-1 · xG 1.27 to 1.31Lecce (H) 1-2 · xG 2.31 to 0.84Cremonese (A) 0-3 · xG 0.00 to 0.71Napoli (H) 0-3 · xG 0.79 to 0.92Lazio (A) 1-2 · xG 1.02 to 1.02M1M5M9M13M17M21M25M29M33M37
PisaOpponents against Pisa

Tactical DNA

The football question
In five genes, what is Pisa'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.

11
Build-Up
possession control from the back
24
Progression
moving the ball upfield with threat
19
Creation
manufacturing chances
20
Press
winning the ball high & hard
17
Solidity
resisting the opponent's threat
Genetic identity
Pisa are defined most by their Progression DNA (24/100 · limited at moving the ball upfield with threat), and least by their Build-Up DNA (11/100). Percentiles vs the 23 teams in the league.

The Playstyle Wheel

Fifteen style metrics across five domains — Possess, Disrupt, Finish, Press, Defend — each a percentile against the rest of the league. Metrics marked ~proxy use the closest available event-data signal where the standard definition needs data this feed doesn't carry (explicit pressure events, offside/keeper-sweep incidents).

Possess9Disrupt29Finish38Press36Defend26
Possess
Deep buildup
13%
Possession
4%
Field tilt
9%
Disrupt
Central progression ~proxy
0%
Dribble tendency
65%
Progressive passes
22%
Finish
Final-third patience
43%
Shot quality
52%
Open-play xG
17%
Press
Forward press ~proxy
30%
High regains
35%
Pressing efficiency ~proxy
43%
Defend
Def-mid press ~proxy
9%
High line ~proxy
43%
PAdj defensive actions
26%
Pisa is defined most by Finish (38% in the league) and weakest on Possess (9%). 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 Pisa actually control, relative to the rest of the league?

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

Inter: 66% tilt, 2.38 pts/gRoma: 61% tilt, 2.00 pts/gComo: 62% tilt, 1.94 pts/gNapoli: 57% tilt, 1.91 pts/gAC Milan: 51% tilt, 1.87 pts/gJuventus: 63% tilt, 1.77 pts/gFrosinone: 50% tilt, 1.75 pts/gLazio: 51% tilt, 1.68 pts/gAtalanta: 55% tilt, 1.50 pts/gBologna: 54% tilt, 1.43 pts/gTorino: 44% tilt, 1.23 pts/gSassuolo: 44% tilt, 1.21 pts/gCagliari: 38% tilt, 1.19 pts/gUdinese: 48% tilt, 1.17 pts/gGenoa: 45% tilt, 1.09 pts/gParma Calcio 1913: 39% tilt, 1.06 pts/gLecce: 46% tilt, 1.02 pts/gFiorentina: 48% tilt, 1.00 pts/gCremonese: 40% tilt, 0.89 pts/gMonza: 47% tilt, 0.78 pts/gHellas Verona: 44% tilt, 0.55 pts/gPisa: 39% tilt, 0.47 pts/gVenezia: 51% tilt, 0.33 pts/gPisa38%47%57%66%0.31.01.72.4FIELD TILT %POINTS / GAME
Vision AI Model Insight
Pisa post 39% field tilt, ranking #21 of 23 in the league — below the league mid-point, spending more of the game defending their own territory than dictating it, and their 0.47 points/game sits below the league-average return — territory is genuinely converting into results.

Where the time goes — how long each phase lasts

The question
When Pisa has the ball, how much of that time is spent building, progressing and creating?

The share of Pisa'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: 29.3% of the time on the ball29%Progression: 45.9% of the time on the ball46%Chance creation: 22.1% of the time on the ball22%Attacking transition: 2.7% of the time on the ball46%PROGRESSION
PhaseShare of the time
Build-up29.3%
Progression45.9%
Chance creation22.1%
Attacking transition2.7%
Vision AI Model Insight
Progression is where Pisa spends most of its time on the ball (46% of its time on the ball).

Passing Network

The question
Who actually plays with whom — the real on-pitch connections, not just average positions?

Every pass linked to its likely receiver (the next same-team touch), aggregated into player-pair combinations at each player's average position. Line width = how often the pair combines; colour = threat generated per pass (blue low → red high). Hover a line for the pair, or click a player to isolate their connections.

Antonio Caracciolo ↔ Simone Canestrelli: 322 passes, 5.6 combined xTAntonio Caracciolo ↔ Michel Aebischer: 279 passes, 9.3 combined xTMichel Aebischer ↔ Simone Canestrelli: 274 passes, 10.2 combined xTMichel Aebischer ↔ Samuele Angori: 212 passes, 9.9 combined xTSimone Canestrelli ↔ Samuele Angori: 209 passes, 11.7 combined xTAdrian Semper ↔ Simone Canestrelli: 185 passes, 7.1 combined xTAntonio Caracciolo ↔ Arturo Calabresi: 162 passes, 3.3 combined xTMichel Aebischer ↔ Mehdi Léris: 155 passes, 10.3 combined xTMichel Aebischer ↔ Ebenezer Akinsanmiro: 151 passes, 6.0 combined xTAntonio Caracciolo ↔ Adrian Semper: 143 passes, 4.3 combined xTArturo Calabresi ↔ Michel Aebischer: 141 passes, 5.7 combined xTMichel Aebischer ↔ Idrissa Touré: 139 passes, 7.5 combined xTMichel Aebischer ↔ Marius Marin: 112 passes, 4.3 combined xTAdrian Semper ↔ Michel Aebischer: 105 passes, 8.4 combined xTIdrissa Touré ↔ Simone Canestrelli: 97 passes, 7.8 combined xTMattéo Tramoni ↔ Samuele Angori: 95 passes, 8.9 combined xTMehdi Léris ↔ Simone Canestrelli: 90 passes, 7.8 combined xTArturo Calabresi ↔ Idrissa Touré: 85 passes, 5.3 combined xTMichel Aebischer ↔ Mattéo Tramoni: 81 passes, 3.8 combined xTStefano Moreo ↔ Mehdi Léris: 80 passes, 5.7 combined xTArturo Calabresi ↔ Simone Canestrelli: 79 passes, 2.5 combined xTSimone Canestrelli ↔ Ebenezer Akinsanmiro: 78 passes, 4.3 combined xTAntonio Caracciolo ↔ Idrissa Touré: 75 passes, 3.3 combined xTArturo Calabresi ↔ Mehdi Léris: 71 passes, 4.1 combined xTSimone Canestrelli — isolate this player's connectionsCanestrellSamuele Angori — isolate this player's connectionsAngoriAntonio Caracciolo — isolate this player's connectionsCaraccioloMichel Aebischer — isolate this player's connectionsAebischerStefano Moreo — isolate this player's connectionsMoreoMehdi Léris — isolate this player's connectionsLérisM'Bala Nzola — isolate this player's connectionsNzolaEbenezer Akinsanmiro — isolate this player's connectionsAkinsanmirIdrissa Touré — isolate this player's connectionsTouréMattéo Tramoni — isolate this player's connectionsTramoniArturo Calabresi — isolate this player's connectionsCalabresiAdrian Semper — isolate this player's connectionsSemperHenrik Meister — isolate this player's connectionsMeisterGabriele Piccinini — isolate this player's connectionsPiccininiMarius Marin — isolate this player's connectionsMarinJuan Cuadrado — isolate this player's connectionsCuadrado
width = passes between the paircolour = xT per pass: low → high

Top 10 combinations

Caracciolo ↔ Canestrelli
322p · 5.6xT
Caracciolo ↔ Aebischer
279p · 9.3xT
Aebischer ↔ Canestrelli
274p · 10.2xT
Aebischer ↔ Angori
212p · 9.9xT
Canestrelli ↔ Angori
209p · 11.7xT
Semper ↔ Canestrelli
185p · 7.1xT
Caracciolo ↔ Calabresi
162p · 3.3xT
Aebischer ↔ Léris
155p · 10.3xT
Aebischer ↔ Akinsanmiro
151p · 6.0xT
Caracciolo ↔ Semper
143p · 4.3xT
Vision AI Model Insight
Busiest connection: Antonio Caracciolo ↔ Simone Canestrelli (322 passes). Most dangerous per pass: Mattéo Tramoni ↔ Samuele Angori (0.09 xT/pass over 95 passes) — the combination that hurts opponents most when it fires, not just the one used most.

Touch Density — where Pisa had the ball

Every touch across 468 matches. Brighter means more touches.

Which Zones Do They Attack Through?

The question
Across the whole pitch, where does Pisa 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
13862
ACTIONS
5%6%13%5%5%10%9%11%9%9%6%3%2%3%5%PASSES
Carries
the ball moved with a player
3069
ACTIONS
3%5%11%7%3%10%8%10%11%10%7%3%2%3%6%CARRIES
Vision AI Model Insight
Their single busiest zone is the centre of the defensive third (13.0% of all actions). 60% of actions run through the inside lanes (centre + both half-spaces) versus 40% down the two wings — an inside-dominant, half-space build. By third: 33% defensive, 47% middle, 20% attacking.

Attack Sides

Which channel they attacked through this season: the share of every action made in the opponent’s half, down the left, through the middle and down the right. Pisa leaned on their right (42%). The bars under the pitch put the league’s share beside theirs.

36%Left side22%Middle42%Right sideATTACKING →
Pisa
36%22%42%
League
38%23%39%

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. Pisa made 1120 this season (29.5 a match); 32% 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: 296 entries (26%)29626%LEFT WINGLeft half-space: 153 entries (14%)15314%LEFT HALF-SPACECentre: 120 entries (11%)12011%CENTRERight half-space: 198 entries (18%)19818%RIGHT HALF-SPACERight wing: 353 entries (32%)35332%RIGHT WINGATTACKING ↑
ChannelEntriesPer matchShareLeague share
Left wing2967.826%28%
Left half-space1534.014%15%
Centre1203.211%13%
Right half-space1985.218%15%
Right wing3539.332%28%
Build-up2 sections

Build-Up — the pass map

The football question
How does Pisa 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.

4716build-up passes attempted
75%completed
30%reach the middle third
3%go long into the final third

Pass types

1606CB · 45%
358Long Wide · 10%
74Long Central · 2%
1110Wide → FB · 31%
387Central · 11%

Sequence success — build-up

1014build-up sequences this season
7%succeed — reach the next third or a shot
46%turned over with nothing to show
CB1606 · 45%
right: 877 passes, most from Caracciolo877left: 729 passes, most from Canestrelli729
Long Wide358 · 10%
right: 246 passes, most from Semper246left: 112 passes, most from Semper112
Long Central74 · 2%
right: 51 passes, most from Semper51left: 23 passes, most from Semper23
Wide → FB1110 · 31%
left: 574 passes, most from Semper574right: 536 passes, most from Semper536
Central387 · 11%
right: 200 passes, most from Aebischer200left: 187 passes, most from Aebischer187
◆ Vision AI Insight
Pisa build predominantly down the left, with Caracciolo as the fulcrum (564 build-up passes). Only 33% 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 → RHS def (230×).

Pass Explorer

Every pass of Pisa’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.

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. 1492 progressive passes across 38 matches (39.3/game).

Origin lane

Right wing
30%
Right half-space
12%
Central
21%
Left half-space
10%
Left wing
28%
Progression is right-dominant: 42% right side · 21% central · 37% left.

Who generates it

Samuele Angori
176 · 12%
Adrian Semper
174 · 12%
Mehdi Léris
123 · 8%
Simone Canestrelli
114 · 8%
Michel Aebischer
106 · 7%
Top two — Samuele Angori & Adrian Semper — account for 23% of all progression.
PROGRESSION
39.3 prog passes/game
mostly right
→
PENETRATION
29% reach final third
21% reach the box
→
THREAT
the other 71% is recycled
progress ≠ danger
What came of it. 29% of progressive passes reached the final third and 21% the box; the rest was recycled in front of the block. 23% of the progression ran through two players, mostly down the right side.

Which lanes actually create the threat

Share of passes played from each lane against its share of the threat (xT) they add. Efficient = more threat than volume, sterile = lots of the ball, little damage.

Per lane — volume vs threat
Right wingefficient
Right half-spacebalanced
Centralbalanced
Left half-spacesterile
Left wingefficient

Progression — the pass map

The football question
Once Pisa 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.

6492progression passes attempted
80%completed
16%break into the final third
14%go backward into the defensive third

Pass types

1762CB · 34%
245Long Wide · 5%
44Long Central · 1%
2366Wide → FB · 46%
778Central · 15%

Sequence success — progression

1743progression sequences this season
25%succeed — reach the next third or a shot
46%turned over with nothing to show
CB1762 · 34%
left: 915 passes, most from Canestrelli915right: 847 passes, most from Canestrelli847
Long Wide245 · 5%
left: 124 passes, most from Angori124right: 121 passes, most from Aebischer121
Long Central44 · 1%
right: 24 passes, most from Marin24left: 20 passes, most from Angori20
Wide → FB2366 · 46%
right: 1205 passes, most from Aebischer1205left: 1161 passes, most from Aebischer1161
Central778 · 15%
left: 393 passes, most from Aebischer393right: 385 passes, most from Aebischer385
◆ Vision AI Insight
Pisa progress predominantly down the left, with Aebischer the busiest midfield outlet (834 progression passes). Only 16% of passes that start in midfield actually break the final-third line — the rest (70%) is recycled back into midfield or (14%) played backward, the hallmark of a side that carries progression itself through midfield rather than just passing through it. Their most-repeated pattern is LHS mid → LW mid (265×) — 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)
15%
Progressive
1%
Neutral / sideways
52%
Safe / backward
32%
15% of passes are progressive-or-better; the remaining 85% 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 Pisa actually progress the ball with their feet, not a pass?

686 progressive carries this season across the whole squad (23% of 2928 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 63m. Most active carrier: Aebischer (84). Filter to one player below.

First (217)Second (203)Third (135)Fourth (131)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 Pisa'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.

364shots
38.6xG
0.11xG per shot
23big chances (0.3+ xG)
25goals
#22key passes / game in the league
#16box entries / game in the league
Attacking transition: 94 shots (26%)26%94Settled attack: 197 shots (54%)54%197Set piece: 73 shots (20%)20%73364SHOTS
Kind of attack, then how the shot was set upShotsGoalsxGxG / shot
Attacking transition 26% of shots94610.070.11
Cross1411.920.14
Cut-back520.860.17
Long ball711.050.15
Pass into the box / short pass3713.310.09
Own creation (carry, dribble, rebound)3112.940.09
Settled attack 54% of shots197918.050.09
Cross5225.490.11
Cut-back1521.500.10
Long ball1000.750.07
Pass into the box / short pass7116.540.09
Own creation (carry, dribble, rebound)4943.780.08
Set piece 20% of shots731010.450.14
Corner6244.790.08
Free kick400.110.03
Penalty765.560.79

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

PlayerSet-upsxG createdGoals
Idrissa Touré183.480
Mattéo Tramoni212.552
Samuele Angori242.311
Stefano Moreo222.191
Mehdi Léris222.153

Who takes them

PlayerShotsxGGoals
M'Bala Nzola285.573
Stefano Moreo525.457
Henrik Meister203.602
Mattéo Tramoni282.822
Idrissa Touré292.531
Vision AI Model Insight
The biggest source of Pisa's chances, by xG, is settled attack (18.1 of 38.6 xG, 197 of 364 shots), mostly pass into the box / short pass. 94 of the 364 shots (26%) came within 15 seconds of winning the ball. 73 of the 364 shots came from set pieces; 25 goals from 38.6 xG (-13.6).

Chance Creation — the pass map

The football question
How does Pisa 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

18CB · 7%
14Long Wide · 6%
39Long Central · 15%
64Wide → FB · 25%
119Central · 47%

Sequence success — final-third

952final-third sequences this season
19%succeed — produce a shot
45%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
CB18 · 7%
right: 11 passes, most from Canestrelli11left: 7 passes, most from Canestrelli7
Long Wide14 · 6%
left: 8 passes, most from Moreo8right: 6 passes, most from Aebischer6
Long Central39 · 15%
right: 21 passes, most from Cuadrado21left: 18 passes, most from Angori18
Wide → FB64 · 25%
left: 38 passes, most from Aebischer38right: 26 passes, most from Nzola26
Central119 · 47%
left: 63 passes, most from Angori63right: 56 passes, most from Aebischer56

Who creates — volume × threat

38Angori
32Aebischer
28Tramoni
27Léris
22Moreo
19Touré
15Akinsanmiro
254key passes #22 of 23
10.7box entries/game #16 of 23
24.5final-3rd entries/game #23 of 23
1.02xG/game #18 of 23
25goals #19 of 23
-13.6finishing vs xG #23 of 23
◆ Vision AI Insight
Pisa lean left — 41% of their key passes come from that flank and its half-space (105 of 254), most often from Angori (28). They funnel chances into the c of the final third, most often via LW att → C att (45 key passes, most often from Angori). Angori is the primary source overall. They generate 1.02 xG a game and finish it wasteful — they under-perform their xG, led by Stefano Moreo (7G), M'Bala Nzola (3G), Mattéo Tramoni (2G).

Crosses & Cutbacks — squad-wide

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

602 crosses: 193 found a team-mate (32%), 72 led to a shot (12%). 79 cut-backs: 33 found a team-mate (42%), 23 led to a shot (29%). 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 Pisa actually cross from?

602 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. 22% come from one territory, the advanced left wing; 9 come from the half-spaces and 0 from the centre.

deep right wing: 47 crosses (8%)8%47deep right half-space: 1 crosses (0%)0%1deep centre: 0 crosses (0%)deep left half-space: 1 crosses (0%)0%1deep left wing: 42 crosses (7%)7%42advanced right wing: 132 crosses (22%)22%132advanced right half-space: 2 crosses (0%)0%2advanced centre: 0 crosses (0%)advanced left half-space: 2 crosses (0%)0%2advanced left wing: 134 crosses (22%)22%134byline right wing: 131 crosses (22%)22%131byline right half-space: 0 crosses (0%)byline centre: 0 crosses (0%)byline left half-space: 3 crosses (0%)0%3byline left wing: 107 crosses (18%)18%107
TerritoryCrossesShareLed to a shot
advanced · left wing13422%22
advanced · right wing13222%12
byline · right wing13122%16
byline · left wing10718%16
deep · right wing478%1
deep · left wing427%3
byline · left half-space30%0
advanced · right half-space20%1
advanced · left half-space20%1
deep · right half-space10%0
deep · left half-space10%0
Finishing3 sections

Shot Map

364 shots for 38.6 xG (1.02/game, #18 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.

Moderate attack. 9.6 shots/game at 0.106 xG/shot. Box entries 11/game (#16). Threat created 9.0 xT/game (#17).

Goal Sequences

The move behind each of Pisa’s 26 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 — 26 goals traced.

How Do They Score?

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

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

By situation

Open play
15 · 60%
Set pieces
4 · 16%
Penalties
6 · 24%

By finish

Right foot
14 · 56%
Left foot
6 · 24%
Header
5 · 20%
Vision AI Model Insight
25 goals scored. 60% come in open play; set pieces add 16%. 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

217 of 655 pass attempts lost here (33%).33%217/655Caracciolo166 of 803 pass attempts lost here (21%).21%166/803Semper416 of 1862 pass attempts lost here (22%).22%416/1862Semper126 of 712 pass attempts lost here (18%).18%126/712Canestrell256 of 684 pass attempts lost here (37%).37%256/684Angori351 of 1392 pass attempts lost here (25%).25%351/1392Touré246 of 1200 pass attempts lost here (20%).20%246/1200Touré240 of 1533 pass attempts lost here (16%).16%240/1533Aebischer180 of 1182 pass attempts lost here (15%).15%180/1182Marin280 of 1185 pass attempts lost here (24%).24%280/1185Angori377 of 874 pass attempts lost here (43%).43%377/874Touré165 of 397 pass attempts lost here (42%).42%165/397Touré91 of 282 pass attempts lost here (32%).32%91/282Moreo134 of 373 pass attempts lost here (36%).36%134/373Angori271 of 728 pass attempts lost here (37%).37%271/728Angoriattacking →

How they lose it

Incomplete pass — attacking third
1038
Failed forward pass
769
Failed take-on
325
Lost in the defensive third
1181
Their weakest zone is the right wing in the attacking third: 43% of 874 pass attempts there are lost (377 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.

Pressing3 sections

Pressing — the full picture

The football question
How hard does Pisa 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.

45
Pressing Index
/100 · average
Intensity54
PPDA, high-press share, high-zone actions
Height43
how high up the pitch they defend
Effectiveness41
high recoveries & high-regain share
Disruption39
opponent completion & progression allowed

Every defensive action & where it happens

Where they win the ball back — by lane

RW
20%
RHS
19%
C
23%
LHS
20%
LW
18%
By third (high / mid / low regain)
Def 51%
Mid 35%
Att 14%
Read
Ranked #16 of 23 for press intensity, this is a high press. Note the tension: 51% of their raw defensive actions still occur in their own third — a possession effect, not a passive one. Dominating the ball (21% high-press share) means that when they do defend, it is often deep. Of recoveries, 53% are won high up the pitch.

Every pressing metric, ranked in the league

metricvaluerankmeaning
PPDA (opp passes per def action)—#16/23lower = more intense; rank is what matters
Defensive actions / game139#6/23
High-press share21%#8/23def actions won high up
Defensive-action height (0–100)37.2#13/23avg pitch height of actions
High recoveries / game20.3#14/23ball won in mid+att third
High-recovery share53%#13/23share of recoveries won high
Opponent completion allowed83%#18/23lower = more disruptive
Opponent progressive allowed9.1%#10/23of 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 Pisa win it back with a defensive action?

Counted over the 1193 opposition possessions that Pisa 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 Pisa step in. Pisa win it after 6.0 opponent passes in the first half-hour and after 4.6 after the hour mark (sooner).

PASSES THEY GET
5.5passes
median 3 · before Pisa win it
TIME THEY KEEP IT
16sec
median 8s · 31.4 wins per game
How many passes the opponent got in before the ball was won
41%
25%
17%
18%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
6.5
19s
15–30′
5.4
16s
30–45+′
6.5
19s
45–60′
5.5
16s
60–75′
5.6
17s
75–90+′
3.8
11s
average passesaverage seconds

Phases of play

Every moment of the match read twice — what Pisa were doing with the ball, and what the opposition were doing about it at that exact moment. Built from the real action sequence in time order, so one possession is split across the phases it genuinely passed through rather than labelled once by where it began.

6,313phase segments analysed
5.0smedian time before the opposition engage
2.0their passes per defensive action when pressed
#18of 20 for share of defending spent pressing high

With the ball

Share of possession time spent in each phase.

Build-up 29.3%Progression 45.9%Chance creation 22.1%Attacking transition 2.7%

Without the ball

How Pisa defend when the opponent has it.

High press 4.9%Mid block 11.0%Low block 53.8%Unopposed 30.2%

What opponents do against Pisa

The mirror image, and the part no other section can show: whether teams come out to press Pisa or sit in and invite them on. A side that is always faced with a low block is playing a different sport from one that is pressed every week.

High press 11.1%Mid block 12.6%Low block 46.0%Unopposed 30.3%

Which phases actually produce

PhaseSegmentsAvg lengthAvg actionsEnds in shotTurned over
Build-up2,1953.9s2.60.2%53.8%
Progression2,6105.1s3.20.5%54.5%
Chance creation1,3894.6s2.823.4%61.8%
Attacking transition1196.7s4.17.6%41.2%

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.

RouteTimesEnds in shot
build_up9760.1%
progression8100.5%
build_up → progression4010.0%
chance_creation39832.2%
progression → chance_creation34023.2%
build_up → progression → chance_creation17424.1%

Attitude in each third

Every other style number here says how much Pisa 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 Pisa 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) 4,716 passes

nothing unusual in this zone

Width of use-0.2320.53
Wide-channel share-0.140.30
Side bias-0.16-0.43
Pass length+0.6024.86
Long-ball share+0.820.31
Forward gain per pass+0.6814.40
Backward-pass share-0.600.17
Deep-pass share+0.800.36
Completion-0.660.74
Seconds between passes-0.023.32
Side used

left 26% · central 47% · right 28%

Progression (middle third) 6,492 passes

more direct, attacking the next line, riskier

Width of use-0.0225.53
Wide-channel share+0.150.41
Side bias-0.52-1.99
Pass length+0.6218.09
Long-ball share+0.970.14
Forward gain per pass+0.884.94
Backward-pass share-0.420.31
Deep-pass share+0.820.20
Completion-0.870.79
Seconds between passes+0.093.24
Side used

left 32% · central 31% · right 37%

Final third 2,654 passes

attacking the next line, riskier, trying to break lines

Width of use+0.5132.86
Wide-channel share+0.330.61
Side bias-0.36-2.99
Pass length+0.8716.82
Long-ball share+1.060.15
Forward gain per pass+1.202.63
Backward-pass share-0.830.33
Deep-pass share+1.070.13
Completion-1.100.61
Seconds between passes+0.353.66
Side used

left 39% · central 14% · right 47%

Reference measured across roughly 3,500 match-teams in this competition, per third, so the comparison is with Pisa'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 Pisa 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 share20%
#2 of 23 · reaches final third in ≤3 passes
Patient build-up share20%
#20 of 23 · spells of 6+ passes
Tempo (possession length)14.5s
#4 of 23 · shorter = faster tempo
Passes per possession3.6
#20 of 23
Counter-attack shots /game0.76
#22 of 23 · shot within 12s of a regain in own half
Press→shot /game2.58
#17 of 23 · shot within 8s of a high regain
Speed to final third after regain11.4s
#5 of 23 · lower = faster break

Sequence success — attacking transition

119attacking transition sequences this season
20%succeed — produce a shot
43%turned over with nothing to show

Sequence success — defensive transition

352defensive transition sequences this season
59%succeed — reach the next third or a shot
12%turned over with nothing to show
Vision AI Model Insight
Pisa are a direct, fast-break side: 20% of possessions reach the final third in 3 passes or fewer (#2 of 23), against 20% built patiently over 6+ passes (#20 of 23). Winning the ball back leads to a shot inside 12 seconds 0.76 times a game (#22), and a high regain converts to a shot within 8 seconds 2.58 times a game (#17 of 23) — not yet a major source of chances.
Defending the box2 sections

Vulnerability Map — how opponents hurt them

The report flipped: where do opponents create their threat against Pisa? 1880 opponent progressive passes and 548 shots faced (54.8 xG against) across the season.

Opponent threat by lane (attacking into Pisa)

Right wing
33%
Right half-space
14%
Central
15%
Left half-space
13%
Left wing
26%
Where the danger came from. Opponents generated most of their threat through the right wing (33% of the danger conceded).

How Do They Concede?

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

Every goal Pisa conceded across the loaded matches — the opponent's shots in Pisa'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 (385 total)

1%2%3%5%3%1%14%18%21%15%17%Gianluca ScamaccaRaoul BellanovaMario PasalicNicola ZalewskiMario PasalicMario PasalicDaniel MaldiniNicola ZalewskiCharles De KetelaereNicola ZalewskiDaniel MaldiniRaoul BellanovaRaoul BellanovaEvan FergusonMario HermosoEvan NdickaEvan FergusonArtem DovbykArtem DovbykPaulo DybalaManu KonéHassane KamaraKeinan DavisArthur AttaIker BravoJesper KarlströmHassane KamaraHassane KamaraAdam BuksaSuat SerdarMartin FreseGiovaneSuat SerdarFallou ChamMartin FreseNicolás ValentiniSuat SerdarYellu SantiagoYunus MusahLorenzo BernasconiNicola ZalewskiCharles De KetelaereMarten de RoonDavide ZappacostaCharles De KetelaereKamaldeen SulemanaMatteo PolitanoLeonardo SpinazzolaMatteo PolitanoGiovanni Di LorenzoBilly GilmourMatteo PolitanoKevin De BruyneMatteo PolitanoStanislav LobotkaLeonardo SpinazzolaStanislav LobotkaScott McTominayCarlos AugustoLuis HenriqueFrancesco Pio EspositoPiotr ZielinskiPiotr ZielinskiHenrikh MkhitaryanLuis HenriqueFederico DimarcoPetar SucicFederico DimarcoAlessandro BastoniCarlos AugustoFederico DimarcoHenrikh MkhitaryanFederico DimarcoLautaro MartínezLautaro MartínezHenrikh MkhitaryanMarcus ThuramFederico DimarcoFederico DimarcoJacopo FazziniJacopo FazziniDodôAlbert GudmundssonRobin GosensMarin PongracicRoberto PiccoliJhon LucumíRiccardo OrsoliniNicolò CambiaghiThijs DallingaThijs DallingaNikola MoroNicolò CambiaghiNikola MoroThijs DallingaJuan MirandaFederico BernardeschiFederico BernardeschiFederico BernardeschiFederico BernardeschiFederico BernardeschiNadir ZorteaRemo FreulerNadir ZorteaKristian ThorstvedtArmand LaurientéDomenico BerardiKristian ThorstvedtAndrea PinamontiNemanja MaticDomenico BerardiArmand LaurientéJosh DoigIsmaël KonéKristian ThorstvedtKristian ThorstvedtCristian VolpatoAntoine BernedeMarco PalestraAdam ObertIbrahim SulemanaGianluca GaetanoAlberto DossenaLeonardo PavolettiAdam ObertMichael FolorunshoMichael FolorunshoSamuele RicciYoussouf FofanaAlexis SaelemaekersLuka ModricLuka ModricSantiago GiménezYoussouf FofanaDavide BartesaghiYoussouf FofanaYoussouf FofanaAlexis SaelemaekersAlexis SaelemaekersDavide BartesaghiDavide BartesaghiRafael LeãoStrahinja PavlovicLuka ModricLuka ModricRafael LeãoAssane DiaoMáximo PerroneNico PazTasos DouvikasMartin BaturinaNico PazMartin BaturinaMartin BaturinaAlberto MorenoNicolas KühnAlessio RomagnoliBoulaye DiaMattia ZaccagniGustav IsaksenToma BasicMattéo GuendouziPedroPedroDanilo CataldiNikola VlasicGiovanni SimeoneGvidas GineitisNikola VlasicChé AdamsGiovanni SimeoneEnzo EbosseEnzo EbosseValentino LazaroIvan IlicNikola VlasicMarcus PedersenArdian IsmajliCesare CasadeiCesare CasadeiIvan IlicIvan IlicAdrien TamèzeMarcus PedersenLorenzo PellegriniDevyne RenschMatías SouléDonyell MalenStephan El ShaarawyWarren BondoFranco VázquezJari VandeputteJari VandeputteMikayil FayeJari VandeputteFranco VázquezFranco VázquezJari VandeputteJari VandeputteLeo ØstigårdAlex AmorimAlex AmorimTommaso BaldanziTommaso BaldanziAarón MartínJeff EkhatorJunior MessiasPatrizio MasiniAlieu FaderaKristian ThorstvedtAlieu FaderaNemanja MaticIsmaël KonéKristian ThorstvedtArmand LaurientéDomenico BerardiNemanja MaticArmand LaurientéArmand LaurientéDomenico BerardiKristian ThorstvedtWoyo CoulibalyKristian ThorstvedtCristian VolpatoCristian VolpatoAdrián BernabéAdrián BernabéEmanuele ValeriEmanuele ValeriGabriel StrefezzaMateo PellegrinoOliver SørensenNesta ElphegeChristian OrdóñezNicolò BarellaHakan ÇalhanogluPetar SucicNicolò BarellaHakan ÇalhanogluNicolò BarellaLautaro MartínezFederico DimarcoLautaro MartínezNicolò BarellaFrancesco Pio EspositoNicolò BarellaLautaro MartínezAndy DioufAdrián BernabéAdrian BenedyczakAdrian BenedyczakLautaro ValentiChristian OrdóñezEmanuele ValeriMedon BerishaLassana CoulibalyYlber RamadaniRiccardo SottilRiccardo SottilYlber RamadaniRiccardo SottilFrancesco CamardaFrancesco CamardaRiccardo SottilRiccardo SottilMohamed KabaRiccardo SottilRiccardo SottilRiccardo SottilLameck BandaTete MorenteMarco PalestraGianluca GaetanoSebastiano EspositoSemih KiliçsoySebastiano EspositoGianluca GaetanoGianluca GaetanoGabriele ZappaSebastiano EspositoGianluca GaetanoGianluca GaetanoSebastiano EspositoManuel LocatelliAndrea CambiasoWeston McKennieLoïs OpendaAndrea CambiasoKhéphren ThuramKenan YildizKhéphren ThuramAarón MartínNicola LealiJohan VásquezBrooke Norton-CuffyLeo ØstigårdAarón MartínAarón MartínJohan VásquezAarón MartínAarón MartínTasos DouvikasLucas da CunhaLucas da CunhaLucas da CunhaLucas da CunhaMáximo PerroneMáximo PerroneMaxence CaqueretMërgim VojvodaJesús RodríguezNicolas KühnHassane KamaraJurgen EkkelenkampHassane KamaraNicolò ZanioloOumar SoletOumar SoletNicolò BertolaZachary AthekameZachary AthekameSamuele RicciSamuele RicciFabiano ParisiJack HarrisonNicolò FagioliManor SolomonMarco BrescianiniRoberto PiccoliRoberto PiccoliNikola MoroNadir ZorteaRemo FreulerNikola MoroRemo FreulerJonathan DavidWeston McKennieKhéphren ThuramBremerManuel LocatelliKenan YildizKenan YildizAndrea CambiasoAndrea CambiasoPierre KaluluAndrea CambiasoPierre KaluluWeston McKennieKenan YildizMattia PerinKenan YildizKhéphren ThuramKenan YildizFrancisco ConceiçãoManuel LocatelliJamil SiebertLameck BandaWalid CheddiraSantiago PierottiJari VandeputteJari VandeputteJari VandeputteFilippo TerraccianoSebastiano LupertoAlessio ZerbinLeonardo SpinazzolaEljif ElmasAmir RrahmaniAmir RrahmaniRasmus HøjlundScott McTominayEljif ElmasKevin De BruynePasquale MazzocchiPedroMatteo CancellieriFisayo Dele-BashiruReda BelahyaneTijjani NoslinPedroAdam MarusicFisayo Dele-BashiruPedroToma BasicTijjani Noslin← own goal

By situation

Open play
57 · 81%
Set pieces
8 · 11%
Penalties
5 · 7%
Vision AI Model Insight
70 goals conceded. 81% come in open play; set pieces account for only 11%, so dead-ball defending is not the main issue. 13% are headers — aerial defending is not where the damage comes from. Their supply line runs mainly through the centre of the attacking third (21% 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 · 28.1 xG
Set pieces
4 g · 4.9 xG
Penalties
6 g
16% of goals come from set pieces (4 of 25), worth 4.9 xG from 66 shots. Set pieces are a secondary threat; the danger is in open play.

Where do Pisa's corners go?

135 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 (75)

4%20%24%17%8%11%5%1%
Delivered into the box 91%Short 9%

Left side (60)

10%35%12%10%3%13%10%2%
Delivered into the box 95%Short 5%

Where opponents attack Pisa from set pieces

99 shots from opponent corners against Pisa, 7 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 (53)

2%23%11%2%25%25%4%9%
Shot 53Goals 1

Left flag (46)

11%15%24%30%11%9%
Shot 46Goals 6

Tactical Strengths & Weaknesses

Strengths (top-third vs league)
  • none stand out
Weaknesses (bottom-third vs league)
  • shots /game — 9.58 (4th pct)
  • field tilt % — 38.91 (9th pct)
  • pass retention % — 74.64 (13th pct)
  • xG conceded /game — 1.44 (17th pct)
  • xG created /game — 1.02 (22th 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.

Hellas Verona
92%
Lecce
89%
Torino
89%
Cremonese
88%
Parma Calcio 1913
86%

Performance vs Opponent Styles

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

Results

opponent typegoals for/ggoals against/gpoints/g
vs high-pressing opponents (12m)0.582.170.67
vs mid-block opponents (12m)1.251.670.67
vs low-block opponents (14m)0.291.790.14

Behaviour — how Pisa actually play against each

opponent typeretentiondirectness (long balls)prog. passes/gxT/gbox entries/g
vs high-pressing opponents70%23%378.19.7
vs mid-block opponents74%20%389.011.5
vs low-block opponents76%17%429.810.8
Vision AI Model Insight
Facing a high press, retention drops to 70% vs 76% against a low block (6pt lower under pressure), and directness (long-ball share) sits at 23% when pressed vs 17% 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. Pisa are territorially-dominated with a aggressive high-press. Their attack creates 1.02 xG a game and they concede 1.44 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 Pisa’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.

Pisa
across 38 matches this season
Final-third entriesvs Parma Calcio 1913 · 0-1 · 2025-12-0861avg 29
Progressive carriesat Lazio · 1-2 · 2026-05-2383avg 32
Field tiltvs Parma Calcio 1913 · 0-1 · 2025-12-0872%avg 39%
Progressive passesvs Atalanta · 1-1 · 2026-01-1666avg 39
Passesat Lazio · 1-2 · 2026-05-23583avg 365
Pass accuracyat Lazio · 1-2 · 2026-05-2388%avg 74%
PPDAat Fiorentina · 0-1 · 2026-02-2314.4avg 7.0
Threat from passingvs Atalanta · 1-1 · 2026-01-169.90avg 5.68

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 Pisa’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).

+14.4on-ball value, 12 matches
Where the value came from
Passes4537 actions+6.37
Carries924 actions-0.98
Dribbles151 actions+0.45
Shots99 actions+5.00
Ball wins690 actions+3.54
Most on-ball value (OBV)ActionsOBVper 100
Antonio Caracciolo571+2.00+0.35
Stefano Moreo352+1.63+0.46
Michel Aebischer668+1.40+0.21
Samuele Angori592+1.38+0.23
Simone Canestrelli608+0.96+0.16
Filip Stojilkovic92+0.82+0.89
Nícolas172+0.71+0.41
Ebenezer Akinsanmiro307+0.71+0.23
123456
Biggest single movesOBV
1  Stefano Moreo · shot+0.822
2  Antonio Caracciolo · shot+0.573
3  Michel Aebischer · pass+0.395
4  Gabriele Piccinini · shot+0.342
5  Ebenezer Akinsanmiro · take-on+0.328
6  Rosen Bozhinov · shot+0.307

Set Pieces by Player

Who delivers Pisa’s dead balls and who finishes them this season. 135 corners taken, most by Samuele Angori (54); 66 shots from corners and free kicks, 4 goals, led by Antonio Caracciolo (1 goals, 1.30 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
Samuele Angori5448 (89%)2—0.062—
Stefano Moreo——1410.75—2/2
Mattéo Tramoni3124 (77%)310.15—1/1
Mehdi Léris2017 (85%)5—0.19——
Michel Aebischer2016 (80%)3—0.091—
Antonio Caracciolo——911.30——
Simone Canestrelli——810.98——
Idrissa Touré——7—0.45——
Juan Cuadrado77 (100%)2—0.0511/1
Mateus Lusuardi——4—0.13——
M'Bala Nzola——1—0.10—2/3
Giovanni Bonfanti——2—0.13——
Gabriele Piccinini11 (100%)1—0.21——
Isak Vural11 (100%)1—0.02——

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.

playerposminVision AI Scorecapability blocks
Juan CuadradoCM5787.4Progression 79%Ball-Carrying 78%Chance Creation 71%Successful Dribble 66%
Raúl AlbiolCB5205.8Breaking Pressure 71%Ball-Carrying 70%Progression 60%Aerial & Box Defending 50%
Rosen BozhinovCB6995.4Ball-Carrying 93%Progression 72%Ball-Winning 42%Aerial & Box Defending 10%
Mehdi LérisCM19814.6Chance Creation 54%Progression 51%Successful Dribble 47%Ball-Carrying 34%
Idrissa TouréCM24504.4Successful Dribble 64%Ball-Carrying 52%Chance Creation 38%Progression 24%
Samuele AngoriCM26954.4Chance Creation 60%Progression 52%Successful Dribble 39%Ball-Carrying 26%
Simone CanestrelliCB33554.0Breaking Pressure 66%Ball-Winning 38%Progression 36%Aerial & Box Defending 33%
Antonio CaraccioloCB32143.7Ball-Winning 61%Breaking Pressure 60%Aerial & Box Defending 38%Progression 18%
Adrian SemperGK23583.7Shot-Stopping 37%
Giovanni BonfantiFB5143.7Ball-Winning 74%Breaking Pressure 41%Wide Delivery 23%Ball-Carrying 10%

Top Creators

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

playerposminkey P /90box entriesxT
Samuele AngoriM26951.276434.0
Mehdi LérisM19811.234822.0
Mattéo TramoniM16521.534215.5
Michel AebischerM30210.953833.3
Idrissa TouréM24500.703417.3
Stefano MoreoF24020.822810.8
Juan CuadradoM5782.022512.5
Simone CanestrelliD33550.211830.5

Defensive Volume Leaders

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

playerposmindef actionstacklesinterceptrecoveries
Antonio CaraccioloD32145837150115
Simone CanestrelliD3355546403397
Idrissa TouréM24505344323111
Michel AebischerM30213946537169
Stefano MoreoF2402376172278
Mehdi LérisM19813616722106
Samuele AngoriM26953204622101
Arturo CalabresiD1568239191957

Full Squad Data

Every player with 100+ minutes (29 players).

playerposappsmingoalsxGkey PprogdefxT
Simone CanestrelliD37335511.7811454630.5
Antonio CaraccioloD35321422.107858322.1
Michel AebischerM35302110.83210639433.3
Samuele AngoriM35269501.43817632034.0
Idrissa TouréM32245012.51910053417.3
Stefano MoreoF37240275.4223537610.8
Adrian SemperG25235800.001744632.5
Mehdi LérisM34198121.82712336122.0
Mattéo TramoniM27165222.8286519115.5
Henrik MeisterF28158823.6361351.5
Arturo CalabresiD26156800.337623916.8
Marius MarinM23131800.114419311.9
Gabriele PiccininiM2595902.17131163.0
Malthe HøjholtM1789000.4214914.1
Isak VuralM1578900.6311773.8
Rosen BozhinovD969900.3433979.0
Filip StojilkovicF1368202.013250.8
Simone ScuffetG766500.00591410.8
Felipe LoyolaM1159410.6311713.0
Juan CuadradoM1957811.113577812.5
NícolasG656300.0041138.0
Rafiu DurosinmiF1252510.834691.1
Raúl AlbiolD852000.13211116.5
Giovanni BonfantiD951400.4330856.6
Francesco CoppolaD748800.1031706.3
Mateus LusuardiD320500.106341.8
LorranM916710.617221.3
Samuel Iling-JuniorM512200.015100.8
Daniel DenoonD212200.002140.2

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.

Simone Canestrelli
Stopper / marker · Defender
minutes3355
goals · xG1 · 1.7
prog passes /903.1
key passes /900.21
def actions /9014.6
pass accuracy78%
Antonio Caracciolo
Stopper / marker · Defender
minutes3214
goals · xG2 · 2.1
prog passes /902.2
key passes /900.00
def actions /9016.3
pass accuracy84%
Michel Aebischer
Ball-winner / destroyer · Midfielder
minutes3021
goals · xG1 · 0.8
prog passes /903.2
key passes /900.95
def actions /9011.7
pass accuracy84%
Samuele Angori
Box-to-box midfielder · Midfielder
minutes2695
goals · xG0 · 1.4
prog passes /905.9
key passes /901.27
def actions /9010.7
pass accuracy66%
Idrissa Touré
Ball-winner / destroyer · Midfielder
minutes2450
goals · xG1 · 2.5
prog passes /903.7
key passes /900.70
def actions /9019.6
pass accuracy62%
Stefano Moreo
Ball-winner / destroyer · Midfielder
minutes2402
goals · xG7 · 5.4
prog passes /901.3
key passes /900.82
def actions /9014.1
pass accuracy57%
Adrian Semper
Goalkeeper · Goalkeeper
minutes2358
goals · xG0 · 0.0
prog passes /906.6
key passes /900.00
def actions /901.8
pass accuracy65%
Mehdi Léris
Box-to-box midfielder · Midfielder
minutes1981
goals · xG2 · 1.8
prog passes /905.6
key passes /901.23
def actions /9016.4
pass accuracy66%
Mattéo Tramoni
Ball-winner / destroyer · Midfielder
minutes1652
goals · xG2 · 2.8
prog passes /903.5
key passes /901.53
def actions /9010.4
pass accuracy66%

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: shots /game (4th pct)
Sign a shot-taking forward to raise shot volume. The data flags this because shot volume is low (9.58, 4th percentile vs the league).
Need — bottom-third: field tilt % (9th pct)
Sign a possession-controlling midfielder to win the territorial battle. The data flags this because they get pinned back (38.91, 9th percentile vs the league).
Need — bottom-third: pass retention % (13th pct)
Sign a secure ball-retaining midfielder. The data flags this because possession is turned over cheaply (74.64, 13th percentile vs the league).
Vision AI · building report