AI DNA Similarity
Best Alternatives to Alberto Escassi
Players most similar to Alberto Escassi (Defender, N/A) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.
Playing Style Analysis
A Ball-Playing CB. Statistically, he stands out as wins the physical battle (57% duel success). The three most similar players to Alberto Escassi by playing style are:
- Santiago Bueno(97% match) — A Ball-Playing CB. Statistically, he stands out as active in the tackle (1.9 tackles/90), meticulous in distribution (87% pass accuracy) and wins the physical battle (57% duel success).
- Mark McKenzie(97% match) — A Ball-Playing CB. Statistically, he stands out as meticulous in distribution (86% pass accuracy) and wins the physical battle (55% duel success).
- Thomas Meißner(97% match) — A Ball-Playing CB. Statistically, he stands out as wins the physical battle (56% duel success).
Similarity is calculated using per-90 performance data across multiple playing style dimensions. How Player DNA matching works →
Similar Players — Ranked by DNA Similarity
#1
S
Santiago Bueno
Wolverhampton Wanderers · Premier League
Uruguay27yContract 2028
Tkl/901.96
KP/900.34
Ball-Playing CB
Last 5: ↓ Dip97% match
€10.0M
#2
M
Mark McKenzie
Toulouse · Ligue 1
United States27yContract 2027
Tkl/901.79
KP/900.71
Ball-Playing CB
Last 5: ↓ Dip97% match
€6.0M
#3
T
Thomas Meißner
Schweinfurt · Bundesliga
Germany35y
Tkl/901.41
KP/900.20
Ball-Playing CBSmall Sample
97% match
N/A
#4
F
Felix Luckeneder
Wehen Wiesbaden · Bundesliga
Austria32y
Tkl/900.96
KP/900.17
Ball-Playing CB
97% match
N/A
#5
J
Jarno Janssen
IJsselmeervogels · Eredivisie
Netherlands25y
Tkl/902.59
KP/900.38
Ball-Playing CBAerial
96% match
N/A
#6
J
Joe Rodon
Leeds United · Premier League
Wales28yContract 2028
Tkl/901.17
KP/900.22
Ball-Playing CBAerial
Last 5: → Stable96% match
€12.0M
#7
J
Jerome Opoku
İstanbul Başakşehir · Super Lig
Ghana27y
Tkl/900.83
KP/900.21
Ball-Playing CBBall-Playing
Last 5: ↓ Dip96% match
N/A
#8
A
Abdul Mumin
Rayo Vallecano · La Liga
Ghana27yContract 2026
Tkl/901.82
KP/900.05
Ball-Playing CBAerial
96% match
€4.0M
#9
J
Jacob Vetter
Middelfart · Superliga
Denmark27y
Tkl/901.80
KP/900.60
Ball-Playing CBAerial
96% match
N/A
#10
J
John Neeskens
FC Eindhoven · Eredivisie
United States32y
Tkl/900.93
KP/900.23
Ball-Playing CBAerial
Last 5: ↑ Hot96% match
N/A
#11
R
Robin Yalçın
Eyüpspor · Super Lig
Germany32y
Tkl/901.50
KP/900.22
Ball-Playing CB
Last 5: → Stable96% match
N/A
#12
Y
Yerry Mina
Cagliari · Serie A
Colombia31yContract 2028
Tkl/901.32
KP/900.31
Ball-Playing CBAerial
Last 5: → Stable96% match
€3.0M
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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 Alberto Escassi.
Ask AI about Alberto Escassi →Frequently Asked Questions
Who are the best alternatives to Alberto Escassi?▼
The top alternatives to Alberto Escassi based on AI DNA playing style analysis include: Santiago Bueno, Mark McKenzie, Thomas Meißner, Felix Luckeneder, Jarno Janssen. 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 Alberto Escassi in 2026?▼
Players with a similar profile to Alberto Escassi in 2026 include Santiago Bueno (€10.0M), Mark McKenzie (€6.0M), Thomas Meißner (N/A). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Alberto Escassi play and who plays similarly?▼
Alberto Escassi plays as a Defender. Players with a comparable positional profile include Santiago Bueno (Uruguay, €10.0M); Mark McKenzie (United States, €6.0M); Thomas Meißner (Germany, N/A); Felix Luckeneder (Austria, 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.