AI DNA Similarity

Best Alternatives to Stefano Lilipaly

Players most similar to Stefano Lilipaly (Attacker, N/A) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Playing Style Analysis

A Forward in KNVB Beker. 22 assists/90). The three most similar players to Stefano Lilipaly by playing style are:

  • Delano Ladan(97% match)A Forward in KNVB Beker. 20 goals/90).
  • Gavin Vlijter(97% match)A Forward in KNVB Beker. 30 assists/90).
  • Martijn Berden(95% match)A Forward in KNVB Beker. 70 big chances/90).

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

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Comparison Base
Stefano Lilipaly
AttackerIndonesiaN/A
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Similar Players — Ranked by DNA Similarity

Ask AI: Why are these players similar?

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 Stefano Lilipaly.

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Frequently Asked Questions

Who are the best alternatives to Stefano Lilipaly?
The top alternatives to Stefano Lilipaly based on AI DNA playing style analysis include: Delano Ladan, Gavin Vlijter, Martijn Berden, David Garden, Huseyin Dogan. 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 Stefano Lilipaly in 2026?
Players with a similar profile to Stefano Lilipaly in 2026 include Delano Ladan (N/A), Gavin Vlijter (N/A), Martijn Berden (N/A). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Stefano Lilipaly play and who plays similarly?
Stefano Lilipaly plays as a Attacker. Players with a comparable positional profile include Delano Ladan (Netherlands, N/A); Gavin Vlijter (Netherlands, N/A); Martijn Berden (Netherlands, N/A); David Garden (Netherlands, N/A).
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.