⬡ AI DNA · Live season

DSC Arminia Bielefeld· 2025/2026 · Bundesliga · PARTIAL

Best Alternatives to Arne Sicker

DefenderGermany29yN/AFull profile →

Ball-Playing CBSmall Sample

2.30Tkl/90

0.96KP/90

Based on 846 min · 2025/2026 · Bundesliga · PARTIAL

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

DNA matchPrioritizes Bundesliga & top-tier peers

Top 3 alternatives to Arne Sicker

Niklas Süle98% match
Borussia Dortmund · Bundesliga · €5.0M
Claudio Kammerknecht98% match
Dynamo Dresden · Bundesliga
Philipp Lahm97% match
Bundesliga

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

Ball-Playing CBSmall Sample

A Ball-Playing CB. Statistically, he stands out as an elite creator (1.6 key passes/90), a reliable supplier (0.21 assists/90), active in the tackle (2.3 tackles/90), wins the physical battle (55% duel success), creates high-quality scoring opportunities (0.74 big chances/90), penetrates with forward passing (10.1 final-third passes/90), central to possession (79 touches/90), uses long balls frequently (6.0/90) and active off the ball (2.9 press score/90), contributing to defensive transitions. Note: this profile is based on 846 minutes of playing time this season. The three closest DNA matches to Arne Sicker:

Niklas Süle(98% match) — A Ball-Playing CB. Statistically, he stands out as a reliable supplier (0.18 assists/90), meticulous in distribution (94% pass accuracy), wins the physical battle (56% duel success), heavily involved in possession (67 passes/90), central to possession (79 touches/90) and active off the ball (2.3 press score/90), contributing to defensive transitions.
Claudio Kammerknecht(98% match) — A Ball-Playing CB. Statistically, he stands out as an aggressive ball-winner (3.2 tackles/90), commanding in the air (6.8 clearances/90), meticulous in distribution (86% pass accuracy), wins the physical battle (70% duel success), heavily involved in possession (72 passes/90), penetrates with forward passing (10.1 final-third passes/90), central to possession (96 touches/90), strong in aerial duels (3.2 aerials won/90), uses long balls frequently (7.4/90), a high-intensity presser (press score 3.2/90), constantly disrupting opposition build-up and top 10% tackler in the league.
Philipp Lahm(97% match) — A Ball-Playing CB. Statistically, he stands out as a capable chance creator (1.0 key passes/90), a prolific assist provider (0.26 assists/90), active in the tackle (1.8 tackles/90), meticulous in distribution (91% pass accuracy), wins the physical battle (76% duel success) and heavily involved in possession (76 passes/90).

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

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Similar Players — Ranked by DNA Similarity

Niklas Süle

Borussia Dortmund · Bundesliga

Germany30yContract 2026

Tkl/901.47

KP/900.37

Ball-Playing CBBall-Playing

98% match

€5.0M

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Frequently Asked Questions

Who are the best alternatives to Arne Sicker?▼

The top alternatives to Arne Sicker based on AI DNA playing style analysis include: Niklas Süle, Claudio Kammerknecht, Philipp Lahm, Paul Scholl, Felix Hagmann. 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 Arne Sicker in 2026?▼

Players with a similar profile to Arne Sicker in 2026 include Niklas Süle (€5.0M), Claudio Kammerknecht (N/A), Philipp Lahm (N/A). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.

What position does Arne Sicker play and who plays similarly?▼

Arne Sicker plays as a Defender. Players with a comparable positional profile include Niklas Süle (Germany, €5.0M); Claudio Kammerknecht (Germany, N/A); Philipp Lahm (Germany, N/A); Paul Scholl (Germany, 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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