AI DNA Similarity

Best Alternatives to Mads Fenger

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

Playing Style Analysis

A Defender in Superliga. The three most similar players to Mads Fenger by playing style are:

  • Emil Møller(100% match)A Defender in Superliga.
  • Daniel Lønborg Thøgersen(100% match)A Defender in Superliga.
  • E. Bayrak(100% match)A Defender in Superliga.

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

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Comparison Base
Mads Fenger
DefenderDenmarkN/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 Mads Fenger.

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Frequently Asked Questions

Who are the best alternatives to Mads Fenger?
The top alternatives to Mads Fenger based on AI DNA playing style analysis include: Emil Møller, Daniel Lønborg Thøgersen, E. Bayrak, Magnus Lysholm, Marius Elvius. 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 Mads Fenger in 2026?
Players with a similar profile to Mads Fenger in 2026 include Emil Møller (N/A), Daniel Lønborg Thøgersen (N/A), E. Bayrak (N/A). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Mads Fenger play and who plays similarly?
Mads Fenger plays as a Defender. Players with a comparable positional profile include Emil Møller (Denmark, N/A); Daniel Lønborg Thøgersen (Denmark, N/A); E. Bayrak (Denmark, N/A); Magnus Lysholm (Denmark, 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.