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AI DNA Similarity

Best Alternatives to André Onana

Players most similar to André Onana (Goalkeeper, €15.0M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to André Onana

  1. 1.Ederson87% DNA match·Fenerbahçe€13.0M
  2. 2.Ivo Grbic87% DNA match·Fatih Karagümrük€3.5M
  3. 3.David Raya86% DNA match·Arsenal€35.0M

Ranked by AI DNA similarity — 768 dimensions across playing style, pressing intensity, and tactical fit.

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Intelligence Verdict

Key PassesTop 4%
???Bottom 0%

Onana is the rare goalkeeper who functions less like a last line of defence and more like an...

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Playing Style Analysis

Sweeper-Keeper

Onana is the rare goalkeeper who functions less like a last line of defence and more like an auxiliary midfielder wearing gloves. His 80.3% pass accuracy places him in the top 5% of Super Lig goalkeepers, but the more revealing number is his 3.81 passes into the final third per 90—he isn't just recycling possession, he's actively advancing it. The counterintuitive detail: his 0.10 key passes and 0.05 assists per 90 both rank top 5% in the league, meaning this goalkeeper is, statistically, a chance creator. The three most similar players to André Onana by playing style are:

  • Ederson(87% match)Ederson has carved out a reputation in the Super Lig as one of its most technically assured goalkeepers, a shot-stopper who doubles as a genuine first-pass option in a league where that combination is rarer than it sounds. His 79.9% pass accuracy places him in the top 5% among league peers—not a trivial achievement when defensive pressure in the Super Lig can be relentless and unforgiving. The counterintuitive detail here is his passes-per-90 sitting below average: he's not spraying the ball constantly, but when he does pass, he's almost always right.
  • Ivo Grbic(87% match)Grbic is a statistical anomaly in the Super Lig, operating less like a traditional shot-stopper and more like a deep-lying libero who happens to wear gloves. While his 1,980 minutes prove his durability, it is his 73.8% pass accuracy—placing him in the elite top 5% of the league—that defines his profile as a modern distributor. He doesn't just clear the ball; he initiates, evidenced by his 2.58 passes into the final third per 90.
  • David Raya(86% match)A Reliable Keeper. Statistically, he stands out as dominant in aerial duels (96% success) and keeps goals out effectively (0.72 conceded/90).

Transfer Intelligence

Ederson delivers 87% of the same playing style, at 13% lower cost (€13.0M vs €15.0M), and is 32 years old.

Similarity is calculated using per-90 performance data across multiple playing style dimensions. How Player DNA matching works →

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Comparison Base
André Onana
GoalkeeperCameroon€15.0M
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Similar Players — Ranked by DNA Similarity

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Our 768-dimension Player DNA model matches playing style, physical profile, pressing intensity, and tactical fit. Ask the AI to explain exactly what makes these players statistically similar to André Onana.

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Frequently Asked Questions

Who are the best alternatives to André Onana?
The top alternatives to André Onana based on AI DNA playing style analysis include: Ederson, Ivo Grbic, David Raya, Unai Simón, Joan García. 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 André Onana in 2026?
Players with a similar profile to André Onana in 2026 include Ederson (€13.0M), Ivo Grbic (€3.5M), David Raya (€35.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does André Onana play and who plays similarly?
André Onana plays as a Goalkeeper. Players with a comparable positional profile include Ederson (Brazil, €13.0M); Ivo Grbic (Croatia, €3.5M); David Raya (Spain, €35.0M); Unai Simón (Spain, €25.0M).
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.