RisingTransfers
AI DNA Similarity

Best Alternatives to Salvatore Esposito

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

Top 3 Alternatives to Salvatore Esposito

  1. 1.Edu Expósito86% DNA match·Espanyol€4.0M
  2. 2.Dário Essugo85% DNA match·Chelsea€20.0M
  3. 3.Maximilian Eggestein84% DNA match·SC Freiburg€10.0M

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

RT

Intelligence Verdict

Press IntensityTop 3%

A Metronome....

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Playing Style Analysis

MetronomeCreative

A Metronome. Statistically, he stands out as an elite creator (1.8 key passes/90), a reliable supplier (0.19 assists/90), meticulous in distribution (88% pass accuracy), wins the physical battle (70% duel success), heavily involved in possession (62 passes/90), penetrates with forward passing (9.7 final-third passes/90), central to possession (83 touches/90), draws fouls effectively (4.2/90), switches play with precision (8.4 long balls/90, 70% accuracy), active off the ball (2.9 press score/90), contributing to defensive transitions and top 20% creator in the league. The three most similar players to Salvatore Esposito by playing style are:

  • Edu Expósito(86% match)A Box-to-Box. Statistically, he stands out as an elite creator (3.2 key passes/90), a prolific assist provider (0.27 assists/90), active in the tackle (1.8 tackles/90), wins the physical battle (55% duel success), creates high-quality scoring opportunities (0.55 big chances/90), delivers dangerous crosses (2.6 accurate crosses/90), penetrates with forward passing (8.9 final-third passes/90), heavily involved in play (60 touches/90), uses long balls frequently (5.4/90), active off the ball (2.4 press score/90), contributing to defensive transitions and top 10% creator in the league.
  • Dário Essugo(85% match)A Box-to-Box. Statistically, he stands out as an aggressive ball-winner (2.7 tackles/90), reads the game exceptionally (1.9 interceptions/90), meticulous in distribution (86% pass accuracy), heavily involved in play (60 touches/90) and active off the ball (2.1 press score/90), contributing to defensive transitions.
  • Maximilian Eggestein(84% match)A Ball-Winner. Statistically, he stands out as an aggressive ball-winner (3.0 tackles/90), wins the physical battle (58% duel success), heavily involved in play (61 touches/90), active off the ball (2.2 press score/90), contributing to defensive transitions and top 10% tackler in the league. Note: this profile is based on 630 minutes of playing time this season.

Transfer Intelligence

Edu Expósito delivers 86% of the same playing style, at 20% lower cost (€4.0M vs €5.0M), with 3.18 key passes per 90 at age 29.

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

S
Comparison Base
Salvatore Esposito
MidfielderItaly€5.0M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
E
Edu Expósito
Espanyol · La Liga
Spain29yContract 2027
KP/903.18
G/900.00
Box-to-BoxCreative
Last 5: → Stable
vs Esposito: 4y older
86% match
€4.0M
#2
D
Dário Essugo
Chelsea · Premier League
Portugal21yContract 2033
KP/900.65
G/900.05
Box-to-BoxDefensive
Last 5: ↑ Hot
vs Esposito: €15M more expensive · 4y younger
85% match
€20.0M
#3
M
Maximilian Eggestein
SC Freiburg · Bundesliga
Germany29y
KP/900.56
G/900.16
Ball-WinnerDefensive
Last 5: ↓ Dip
vs Esposito: 4y older
84% match
€10.0M
#4
L
Lennon Miller
Udinese · Serie A
Scotland19yContract 2030
KP/901.22
G/900.00
Box-to-BoxDefensive
Last 5: ↑ Hot
85% match
€8.0M
#5
M
Manuel Locatelli
Juventus · Serie A
Italy28yContract 2028
KP/901.88
G/900.00
MetronomeDefensive
Last 5: ↓ Dip
84% match
€25.0M
#6
A
Antoni Milambo
Brentford · Premier League
Netherlands21yContract 2030
KP/901.22
G/900.14
Creator
84% match
€20.0M
#7
C
Cristian Volpato
Sassuolo · Serie A
Italy22yContract 2028
KP/901.88
G/900.00
CreatorCreative
85% match
€10.0M
#8
N
Nicolás Domínguez
Nottingham Forest · Premier League
Argentina27yContract 2028
KP/900.91
G/900.08
Ball-WinnerDefensive
Last 5: → Stable
85% match
€15.0M
#9
T
Tomas Suslov
Hellas Verona · Serie A
Slovakia23yContract 2027
KP/900.48
G/900.00
Creator
Last 5: → Stable
84% match
€5.0M
#10
A
Alex Scott
AFC Bournemouth · Premier League
England22yContract 2028
KP/900.88
G/900.10
Box-to-Box
Last 5: ↑ Hot
84% match
€40.0M
#11
F
Fabio Miretti
Juventus · Serie A
Italy22yContract 2028
KP/901.68
G/900.34
CreatorCreative
84% match
€16.0M
#12
M
Michel Aebischer
Pisa · Serie A
Switzerland29yContract 2026
KP/901.05
G/900.00
Balanced Midfielder
Last 5: ↑ Hot
84% match
€4.0M

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 Salvatore Esposito.

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Frequently Asked Questions

Who are the best alternatives to Salvatore Esposito?
The top alternatives to Salvatore Esposito based on AI DNA playing style analysis include: Edu Expósito, Dário Essugo, Maximilian Eggestein, Lennon Miller, Manuel Locatelli. 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 Salvatore Esposito in 2026?
Players with a similar profile to Salvatore Esposito in 2026 include Edu Expósito (€4.0M), Dário Essugo (€20.0M), Maximilian Eggestein (€10.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Salvatore Esposito play and who plays similarly?
Salvatore Esposito plays as a Midfielder. Players with a comparable positional profile include Edu Expósito (Spain, €4.0M); Dário Essugo (Portugal, €20.0M); Maximilian Eggestein (Germany, €10.0M); Lennon Miller (Scotland, €8.0M).
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.