RisingTransfers
AI DNA Similarity

Best Alternatives to Afsha

Players most similar to Afsha (Midfielder, €1.2M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Afsha

  1. 1.Mathijs Tielemans84% DNA match·Excelsior
  2. 2.James Maddison  83% DNA match·Tottenham Hotspur€30.0M
  3. 3.Muhammet Özbaskıcı84% DNA match·Samsunspor

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

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Intelligence Verdict

Dribbled PastTop 0%
???Bottom 0%

Mohamed Magdi Kafsha is a Creator....

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Playing Style Analysis

CreatorCreative

Mohamed Magdi Kafsha is a Creator. Playmaker unlocking defenses with key passes. Statistically, he stands out as an elite creator (2.8 key passes/90), a regular goalscorer (0.24 goals/90), a prolific assist provider (0.40 assists/90), meticulous in distribution (86% pass accuracy), heavily involved in possession (62 passes/90), creates high-quality scoring opportunities (1.21 big chances/90) and top 10% creator in the league. The three most similar players to Afsha by playing style are:

  • Mathijs Tielemans(84% match)Mathijs Tielemans is a Creator. Playmaker unlocking defenses with key passes. Statistically, he stands out as an elite creator (2.0 key passes/90), meticulous in distribution (89% pass accuracy) and top 10% creator in the league.
  • James Maddison  (83% match)James Maddison is a Creator. Playmaker unlocking defenses with key passes. Statistically, he stands out as an elite creator (2.1 key passes/90), a proven goalscorer (0.45 goals/90), a prolific assist provider (0.35 assists/90), meticulous in distribution (87% pass accuracy), penetrates with forward passing (8.6 final-third passes/90), central to possession (78 touches/90), draws fouls effectively (3.2/90) and top 10% creator in the league.
  • Muhammet Özbaskıcı(84% match)Muhammet Ali Özbaskıcı is a Creator. Playmaker unlocking defenses with key passes. Statistically, he stands out as an elite creator (2.2 key passes/90), a regular goalscorer (0.32 goals/90), an aggressive ball-winner (4.1 tackles/90), top 10% creator in the league and top 10% tackler in the league. (Limited sample: 283 mins)

Transfer Intelligence

James Maddison   delivers 83% of the same playing style, at a 2400% premium over Afsha, with 2.12 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 →

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Comparison Base
Afsha
MidfielderEgypt€1.2M
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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 Afsha.

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Frequently Asked Questions

Who are the best alternatives to Afsha?
The top alternatives to Afsha based on AI DNA playing style analysis include: Mathijs Tielemans, James Maddison  , Muhammet Özbaskıcı, Osama Rashid, Lander Astiazaran. 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 Afsha in 2026?
Players with a similar profile to Afsha in 2026 include Mathijs Tielemans (N/A), James Maddison   (€30.0M), Muhammet Özbaskıcı (N/A). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Afsha play and who plays similarly?
Afsha plays as a Midfielder. Players with a comparable positional profile include Mathijs Tielemans (Netherlands, N/A); James Maddison   (England, €30.0M); Muhammet Özbaskıcı (Turkey, N/A); Osama Rashid (Iraq, 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.