⬡ AI DNA · Live season

B 93· 2025/2026 · Superliga · FULL

Best Alternatives to Mikkel Wohlgemuth

MidfielderDenmark31yN/AFull profile →

Metronome

0.63KP/90

0.39G/90

0.08A/90

Based on 2,213 min · 2025/2026 · Superliga · FULL

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

DNA matchPrioritizes Superliga & top-tier peers

Top 3 alternatives to Mikkel Wohlgemuth

Magnus Saaby98% match
Kolding IF · Superliga
Frederik Grube98% match
Aarhus Fremad · Superliga
Justin Janssen97% match
Nordsjælland · Superliga

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

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Intelligence Verdict

GoalsTop 7%

“A Metronome....”

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Playing Style Analysis

Metronome

A Metronome. Statistically, he stands out as a proven goalscorer (0.45 goals/90), meticulous in distribution (88% pass accuracy), wins the physical battle (56% duel success), heavily involved in possession (79 passes/90), penetrates with forward passing (10.1 final-third passes/90), central to possession (93 touches/90), uses long balls frequently (6.3/90) and active off the ball (3.0 press score/90), contributing to defensive transitions. The three closest DNA matches to Mikkel Wohlgemuth:

Magnus Saaby(98% match) — A Metronome. Statistically, he stands out as a capable chance creator (1.2 key passes/90), reads the game exceptionally (1.9 interceptions/90), meticulous in distribution (87% pass accuracy), heavily involved in possession (63 passes/90), penetrates with forward passing (9.2 final-third passes/90), central to possession (79 touches/90) and active off the ball (2.6 press score/90), contributing to defensive transitions.
Frederik Grube(98% match) — A Metronome. Statistically, he stands out as meticulous in distribution (89% pass accuracy), heavily involved in possession (64 passes/90), penetrates with forward passing (8.1 final-third passes/90), central to possession (77 touches/90) and active off the ball (2.3 press score/90), contributing to defensive transitions.
Justin Janssen(97% match) — A Metronome. Statistically, he stands out as an elite creator (1.5 key passes/90), a reliable supplier (0.16 assists/90), an aggressive ball-winner (2.8 tackles/90), reads the game exceptionally (1.6 interceptions/90), meticulous in distribution (89% pass accuracy), heavily involved in possession (76 passes/90), penetrates with forward passing (10.8 final-third passes/90), central to possession (93 touches/90), switches play with precision (7.8 long balls/90, 66% accuracy) and active off the ball (2.9 press score/90), contributing to defensive transitions.

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

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Similar Players — Ranked by DNA Similarity

Magnus Saaby

Kolding IF · Superliga

vs Wohlgemuth: 8y younger

98% match

N/A

Compare Wohlgemuth vs Saaby →

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Frequently Asked Questions

Who are the best alternatives to Mikkel Wohlgemuth?▼

The top alternatives to Mikkel Wohlgemuth based on AI DNA playing style analysis include: Magnus Saaby, Frederik Grube, Justin Janssen, Simon Bækgård, Mark Brink. 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 Mikkel Wohlgemuth in 2026?▼

Players with a similar profile to Mikkel Wohlgemuth in 2026 include Magnus Saaby (N/A), Frederik Grube (N/A), Justin Janssen (N/A). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.

What position does Mikkel Wohlgemuth play and who plays similarly?▼

Mikkel Wohlgemuth plays as a Midfielder. Players with a comparable positional profile include Magnus Saaby (Denmark, N/A); Frederik Grube (Denmark, N/A); Justin Janssen (Denmark, N/A); Simon Bækgård (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.

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