RisingTransfers
AI DNA Similarity

Best Alternatives to Nathan Trott

Players most similar to Nathan Trott (Goalkeeper, €1.5M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Nathan Trott

  1. 1.Robin Roefs85% DNA match·Sunderland€18.0M
  2. 2.Joël Drommel84% DNA match·Sparta Rotterdam€3.5M
  3. 3.Caoimhín Kelleher83% DNA match·Brentford€22.0M

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

RT

Intelligence Verdict

Goals ConcededTop 16%
???Bottom 0%

Trott is a modern orchestrator in gloves, a high-volume distributor whose composure under pressure...

See Full Verdict + Share Card →

Playing Style Analysis

Sweeper-Keeper

Trott is a modern orchestrator in gloves, a high-volume distributor whose composure under pressure transforms him from a mere shot-stopper into a deep-lying playmaker for a League One side. Operating in the top 20% of the division for both pass volume and accuracy, he doesn't just clear his lines; he dictates the tempo, evidenced by his 6.88 passes into the final third per 90 which places him well above the league average for creative output. The counterintuitive insight lies in his 100% duel win rate; while most keepers avoid physical engagement, Trott actively hunts loose balls, showcasing a proactive sweeping style that the data identifies as elite. The three most similar players to Nathan Trott by playing style are:

  • Robin Roefs(85% match)A Commanding Keeper. Statistically, he stands out as naturally left-footed, dominant in aerial duels (97% success), penetrates with forward passing (9.2 final-third passes/90), reliable in goal (3.1 saves/90) and commands the box with authority (0.7 punches/90).
  • Joël Drommel(84% match)A Sweeper-Keeper. Statistically, he stands out as dominant in aerial duels (100% success), penetrates with forward passing (10.5 final-third passes/90) and exceptionally busy shot-stopper (4.2 saves/90). However, he concedes frequently (1.79/90).
  • Caoimhín Kelleher(83% match)A Sweeper-Keeper. Statistically, he stands out as dominant in aerial duels (100% success) and commands the box with authority (0.8 punches/90).

Transfer Intelligence

Robin Roefs delivers 85% of the same playing style, at a 1124% premium over Nathan Trott, and is 23 years old.

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

N
Comparison Base
Nathan Trott
GoalkeeperEngland€1.5M
Full profile →

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 Nathan Trott.

Ask AI about Nathan Trott

Frequently Asked Questions

Who are the best alternatives to Nathan Trott?
The top alternatives to Nathan Trott based on AI DNA playing style analysis include: Robin Roefs, Joël Drommel, Caoimhín Kelleher, David Raya, Jonathan Fischer. 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 Nathan Trott in 2026?
Players with a similar profile to Nathan Trott in 2026 include Robin Roefs (€18.0M), Joël Drommel (€3.5M), Caoimhín Kelleher (€22.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Nathan Trott play and who plays similarly?
Nathan Trott plays as a Goalkeeper. Players with a comparable positional profile include Robin Roefs (Netherlands, €18.0M); Joël Drommel (Netherlands, €3.5M); Caoimhín Kelleher (Republic of Ireland, €22.0M); David Raya (Spain, €35.0M).
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.