Risk levelLow — driven by distribution reliability (100%)
League comparison46% composite among 14 Serie A goalkeepers (450'+)
Vision AI recommendationSolid, balanced profile — fit depends on system more than upside
Playing Style DNA
The question
Across the traits that actually separate goalkeeper profiles, where does Ivan Provedel sit?
Six axes, each a league percentile among Serie A goalkeepers with 450+ minutes — not a raw stat, a rank. Cross-claiming and a literal "command"/communication signal are deliberately excluded: this data source has no claim, punch or audio event to measure them from.
Shot Stopping goals prevented vs xGOT faced
50
Distribution pass completion, league percentile
100
Progression progressive passes/90
29
Sweeping recoveries/90 (outside-box volume proxy)
7
Line Height average starting position (depth)
100
Long-Ball Reliance passing involvement/90
7
VISION AI READ
Ivan Provedel profiles as a Hybrid Keeper (1 matches) — a balanced profile across distribution and sweeping without one standout trait — flexible, not a system specialist.
Vision AI implicationMatches best where the system plays to his top two traits (Distribution, Line Height)
Shot Stopping
The question
When the ball actually gets on target, does Ivan Provedel save it more often than expected?
Every shot faced is individually attributed to him via the source's own goalkeeper-tagging (not approximated from "matches he played"), with real post-shot xG (xGOT) and placement. 0 shots on target across 1 matches.
Where shots landed (on target)
No data yet.
By finish
No data yet.
By situation
No data yet.
VISION AI READ
Not enough shots on target attributed to him yet for a reliable read.
Vision AI implicationRoughly league-average shot-stopping — not a decisive factor either way
Build-Up Behaviour
The question
How does Ivan Provedel actually start attacks — and does he invite or avoid pressure doing it?
Zone-based, not player-identity based: no pass-recipient field is recorded, so "which teammate receives" cannot be answered honestly — "which lane/zone he plays into" can. "Switches play across 3+ lanes" was dropped from this report — it measures 0-2% for every goalkeeper in the league, a structural artefact of starting central and deep, not a trait that separates one keeper from another.
Where his passes end up
Right Wing
6%
Right Half-Space
12%
Centre
44%
Left Half-Space
19%
Left Wing
19%
6%
recycles/retains
0.0%
breaks first line
VISION AI READ
Of 33 completed passes, 6% are sideways/backward relative to his own goal, and 44% of all his output funnels into a single lane — the centre. He breaks the opponent's first line with 0 line-breaking passes (0.0% of his output) — a keeper who progresses directly.
Vision AI implicationNot a build-up outlet — treat him as a safety valve, not a progression source
Distribution Intelligence
The question
What does Ivan Provedel actually do with the ball once he has it — and does that change on restarts?
Preferred kicking foot and exact release timing are not available in this data source (no body-part field on pass events, no sub-second timestamps) — range, target zones and restart behaviour are. Hover any pass on either map for its length and outcome.
Every pass, real start→end
● completed● incomplete
Where passes land
His single busiest destination zone takes 31% of all completed passes — a genuinely concentrated, predictable release point for a team scouting how to press the release.
Pass clusters — long, wide (outside the box), or short
Every pass classified into exactly one of three named clusters — a spatial grouping, not the raw distance split below.
● Long● Wide (outside box)● Short
Pass count by cluster
Long
3
Wide (outside box)
3
Short
27
His release patterns, as one arrow each
Four named release patterns — where each one lands on average, sized by how often he uses it. Hover an arrow for its exact count and completion rate.
Release pattern mix
Long → wide
3 · 100%
Short → wide (outside box)
5 · 100%
Short → CB in box
7 · 100%
Pass length mix
Short (<20m)
48%
Medium (20-40m)
42%
Long (40m+)
9%
Deep restarts vs in-play
Deep restarts (x<10, proxy for goal kicks)
21% long
In-play distribution
16% long
Team fit: Inter plays a patient, short build-up approach in the build-up phase (3% of 3875 team passes from deep go long). Ivan Provedel matches the team's own build-up tendency — his distribution reinforces the same approach, not fighting it.
VISION AI READ
48% of his passes are short (<20m), 9% are long (40m+). On deep restarts specifically he goes long 21% of the time versus 16% in open play — a consistent distribution style regardless of the restart type.
Vision AI implicationPrimarily a short-range distributor
Sweeper Intelligence
The question
How much of the space behind his defence does Ivan Provedel actually cover himself?
Every recorded defensive action (recoveries, clearances, interceptions, tackles, blocks) plotted by real location — the only five event types this source records for any player, goalkeeper included.
Averages a starting position of x=15 on a 0-100 pitch scale (0 = his own goal-line). Of 2 defensive actions this season, 1 (50%) happen outside his own box — a genuine sweeper, regularly defending space in behind his back line.
Vision AI implicationA high line would leave space he does not habitually cover — pair him with a deeper block
Risk Profile
The question
When Ivan Provedel loses the ball, where does it happen and how dangerous is it?
Every incomplete pass, plotted at its real origin — not a turnover count, a turnover map.
3.0%
turnover rate
100%
lost centrally
0
lost past halfway
VISION AI READ
1 of his 33 passes (3.0%) are turned over. 100% of those losses happen in the central lane — the most dangerous channel to give the ball away in — and 0 losses occur at or past the halfway line. Overall distribution risk reads as low (100% on reliability).
Vision AI implicationLow-risk distributor — safe to build directly through him
Pressure Behaviour
The question
Does Ivan Provedel's decision-making actually change when the opponent presses him hard?
His own matches split by how aggressively that specific opponent pressed (PPDA tertiles) — the same technique used for team-level opponent splits elsewhere on this platform.
Insufficient sample.
VISION AI READ
Not enough matches with a reliable opponent-press split loaded yet for this read.
Vision AI implicationn/a
Opponent Intelligence
The question
If I'm coaching against Ivan Provedel, what specifically do I tell my team?
Press his first pass into the centre — it's his clearest outlet; cutting it forces the alternative.
His distribution style barely changes under pressure, so pressing him for a mistake is unlikely to pay off quickly — patience is needed.
Attack the space in behind directly — he rarely steps outside his box (7% sweeping), so a covering sweeper isn't there to bail his defence out.
His weakest trait is Sweeping (7%) — a system that stresses that specific demand exposes him fastest.
Attack centrally when he has the ball — 100% of his turnovers happen in the central lane, the most dangerous place to win it back.