RisingTransfers
⬡ AI DNA · Live season
Genoa

Best Alternatives to Daniele Sommariva

GoalkeeperItaly28y€100KFull profile →
Sweeper-KeeperSmall Sample
0.00G/90
0.00A/90

Based on 270 min · 2020/2021 · Serie A · PARTIAL

Ranked by AI DNA similarity — playing style, pressing, and tactical fit. Below: the closest replacements with per-90 stats and form.

DNA matchPrioritizes Serie A & top-tier peers

Top 3 alternatives to Daniele Sommariva

  1. Mile Svilar80% match

    Roma · Serie A · €35.0M

  2. Mike Maignan80% match

    AC Milan · Serie A · €25.0M

  3. Marco Carnesecchi80% match

    Atalanta · Serie A · €25.0M

Ranked by RT Player DNA + league/value credibility. Scroll for full list, per-90 stats, and share cards.

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Playing Style Analysis

Sweeper-KeeperSmall Sample

Daniele Sommariva is a Sweeper-Keeper. Proactive goalkeeper who plays as a spare outfield player. Statistically, he stands out as dominant in aerial duels (100% success). (Limited sample: 270 mins) The three closest DNA matches to Daniele Sommariva:

  • Mile Svilar(80% match) Svilar has quietly become one of the most tactically complete goalkeepers in Serie A, a sweeper-keeper whose value extends well beyond shot-stopping into genuine ball-winning territory. His tackles won rate sits in the top 5% of league goalkeepers—a figure that sounds anomalous until you watch him read situations before they become crises, ranking in the top 20% for game-reading.
  • Mike Maignan(80% match) A Traditional Keeper. Statistically, he stands out as dominant in aerial duels (100% success).
  • Marco Carnesecchi(80% match) Carnesecchi has quietly built one of Serie A's most complete goalkeeping profiles without ever dominating a single headline statistic—which is, paradoxically, exactly what makes him interesting. His 30.9 passes per 90 sits above the league average, and a 68.6% accuracy rate suggests he's attempting ambitious distribution rather than recycling safe laterals—his passes into the final third rank above average, meaning he's actively functioning as a build-up participant rather than a reset button.

Similarity uses per-90 performance across playing-style dimensions. How Player DNA matching works →

Comparison Base
Daniele Sommariva
GoalkeeperItalySerie A€100K

Similar Players — Ranked by DNA Similarity

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Top 8 shown first · 16 total ranked by DNA + league fit.

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Download RT stat cards for Daniele Sommariva vs Mile Svilar, Mike Maignan, and Marco Carnesecchi. Post to X or WhatsApp — DNA + season totals from RT.

Daniele Sommariva RT player card

Source · Daniele Sommariva

Mile Svilar RT player card

Alt · Mile Svilar

Mike Maignan RT player card

Alt · Mike Maignan

Marco Carnesecchi RT player card

Alt · Marco Carnesecchi

Want a deeper take? Scroll to Ask RT below — same shortlist, multi-turn chat.

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Same DNA shortlist as above — budget fit, who to avoid, or why the top match works.

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Frequently Asked Questions

Who are the best alternatives to Daniele Sommariva?
The top alternatives to Daniele Sommariva based on AI DNA playing style analysis include: Mile Svilar, Mike Maignan, Marco Carnesecchi, Vanja Milinković-Savić , Zion Suzuki. These players were matched using Rising Transfers' 768-dimension DNA model across playing style, pressing intensity, and tactical fit — not just position or market value.
Which players are similar to Daniele Sommariva in 2026?
Players with a similar profile to Daniele Sommariva in 2026 include Mile Svilar (€35.0M), Mike Maignan (€25.0M), Marco Carnesecchi (€25.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Daniele Sommariva play and who plays similarly?
Daniele Sommariva plays as a Goalkeeper. Players with a comparable positional profile include Mile Svilar (Serbia, €35.0M); Mike Maignan (France, €25.0M); Marco Carnesecchi (Italy, €25.0M); Vanja Milinković-Savić  (Serbia, €20.0M).
How does Rising Transfers find similar players?
Rising Transfers uses a proprietary 768-dimension Player DNA model trained on 3.2 million match events. Each player is represented as a vector across 35+ per-90 metrics including pressing intensity, passing footprint, dribbling profile, and defensive contribution. Similarity is measured using cosine distance — the same technique used in state-of-the-art AI systems — making it the most precise player comparison tool available publicly.