RisingTransfers
AI DNA Similarity

Best Alternatives to Elseid Hysaj

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

Top 3 Alternatives to Elseid Hysaj

  1. 1.Gabriele Zappa87% DNA match·Cagliari€4.0M
  2. 2.Giovanni Di Lorenzo 85% DNA match·Napoli€10.0M
  3. 3.Adam Marusic85% DNA match·Lazio€3.0M

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

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Intelligence Verdict

AssistsTop 0%
???Bottom 0%

Hysaj remains one of Serie A’s most enigmatic paradoxes...

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Playing Style Analysis

Active Full-BackSmall Sample

Hysaj remains one of Serie A’s most enigmatic paradoxes, a veteran fullback who has traded defensive volume for surgical offensive output in a limited sample size. While his 39.7 passes per 90 suggest a standard involvement level, his efficiency is elite; ranking in the top 5% for assists and the top 10% for key passes, he operates more like a deep-lying playmaker than a traditional stopper. The counterintuitive reality is that despite a 100% ground duel success rate, he is an incredibly passive defender, ranking in the bottom 10% for press intensity. The three most similar players to Elseid Hysaj by playing style are:

  • Gabriele Zappa(87% match)A Active Full-Back. Statistically, he stands out as a capable chance creator (1.0 key passes/90), a prolific assist provider (0.33 assists/90), meticulous in distribution (85% pass accuracy) and uses long balls frequently (5.0/90). Note: this profile is based on 806 minutes of playing time this season.
  • Giovanni Di Lorenzo (85% match)Di Lorenzo is the rare full-back who makes a back four feel like a possession unit—a defender whose passing range does more damage than most midfielders. Playing for a mid-table Serie A side, he sits in the top 10% of his position for both passes into the final third and shots per 90, numbers that reveal an attacking output quietly outpacing his surroundings. His 85.8% pass accuracy and 58.1 passes per 90 confirm he's the engine of build-up, not just a participant.
  • Adam Marusic(85% match)A Active Full-Back. Statistically, he stands out as meticulous in distribution (87% pass accuracy) and wins the physical battle (56% duel success).

Transfer Intelligence

Gabriele Zappa delivers 87% of the same playing style, at a 167% premium over Elseid Hysaj, with 1.79 tackles won per 90 at age 26.

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

E
Comparison Base
Elseid Hysaj
DefenderAlbania€1.5M
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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 Elseid Hysaj.

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Frequently Asked Questions

Who are the best alternatives to Elseid Hysaj?
The top alternatives to Elseid Hysaj based on AI DNA playing style analysis include: Gabriele Zappa, Giovanni Di Lorenzo , Adam Marusic, Marcus Pedersen, Leonardo Spinazzola. 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 Elseid Hysaj in 2026?
Players with a similar profile to Elseid Hysaj in 2026 include Gabriele Zappa (€4.0M), Giovanni Di Lorenzo  (€10.0M), Adam Marusic (€3.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Elseid Hysaj play and who plays similarly?
Elseid Hysaj plays as a Defender. Players with a comparable positional profile include Gabriele Zappa (Italy, €4.0M); Giovanni Di Lorenzo  (Italy, €10.0M); Adam Marusic (Montenegro, €3.0M); Marcus Pedersen (Norway, €3.5M).
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.