RisingTransfers
AI DNA Similarity

Best Alternatives to Tomas Suslov

Players most similar to Tomas Suslov (Midfielder, €5.0M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Tomas Suslov

  1. 1.Martin Baturina86% DNA match·Como€18.0M
  2. 2.Sandi Lovrić85% DNA match·Hellas Verona€6.0M
  3. 3.Cristian Volpato85% DNA match·Sassuolo€10.0M

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

RT

Intelligence Verdict

ShotsTop 10%
???Bottom 0%

A Creator....

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Playing Style Analysis

Creator

A Creator. Statistically, he stands out as naturally left-footed, a capable chance creator (1.2 key passes/90), active in the tackle (2.4 tackles/90), draws fouls effectively (2.8/90) and active off the ball (2.0 press score/90), contributing to defensive transitions. The three most similar players to Tomas Suslov by playing style are:

  • Martin Baturina(86% match)A Creator. Statistically, he stands out as an elite creator (4.1 key passes/90), a proven goalscorer (0.51 goals/90), a prolific assist provider (0.31 assists/90), meticulous in distribution (88% pass accuracy), creates high-quality scoring opportunities (0.72 big chances/90), heavily involved in play (70 touches/90), draws fouls effectively (2.4/90), active off the ball (2.2 press score/90), contributing to defensive transitions and top 10% creator in the league. However, he loses possession under pressure (1.6 dispossessed/90).
  • Sandi Lovrić(85% match)A Creator. Statistically, he stands out as a capable chance creator (1.2 key passes/90), active in the tackle (1.9 tackles/90), meticulous in distribution (85% pass accuracy), heavily involved in play (52 touches/90) and active off the ball (2.3 press score/90), contributing to defensive transitions.
  • Cristian Volpato(85% match)A Creator. Statistically, he stands out as an elite creator (2.0 key passes/90), a regular goalscorer (0.20 goals/90), a prolific assist provider (0.40 assists/90), creates high-quality scoring opportunities (0.50 big chances/90), heavily involved in play (57 touches/90), active off the ball (2.0 press score/90), contributing to defensive transitions and top 10% creator in the league. However, he loses possession under pressure (2.0 dispossessed/90).

Transfer Intelligence

Martin Baturina delivers 86% of the same playing style, at a 260% premium over Tomas Suslov, with 4.11 key passes per 90 at age 23.

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

T
Comparison Base
Tomas Suslov
MidfielderSlovakia€5.0M
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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 Tomas Suslov.

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Frequently Asked Questions

Who are the best alternatives to Tomas Suslov?
The top alternatives to Tomas Suslov based on AI DNA playing style analysis include: Martin Baturina, Sandi Lovrić, Cristian Volpato, Petar Sučić, Nicola Zalewski. 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 Tomas Suslov in 2026?
Players with a similar profile to Tomas Suslov in 2026 include Martin Baturina (€18.0M), Sandi Lovrić (€6.0M), Cristian Volpato (€10.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Tomas Suslov play and who plays similarly?
Tomas Suslov plays as a Midfielder. Players with a comparable positional profile include Martin Baturina (Croatia, €18.0M); Sandi Lovrić (Slovenia, €6.0M); Cristian Volpato (Italy, €10.0M); Petar Sučić (Croatia, €30.0M).
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.