RisingTransfers
AI DNA Similarity

Best Alternatives to Xinli Peng

Players most similar to Xinli Peng (Midfielder, €2.6M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Xinli Peng

  1. 1.Jerome Deom98% DNA match·Roda JC Kerkrade
  2. 2.Yusuf Deniz Sas98% DNA match·Ümraniyespor
  3. 3.Vasco Sousa98% DNA match·Porto

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

RT

Intelligence Verdict

Press IntensityTop 15%
???Bottom 0%

A Box-to-Box....

See Full Verdict + Share Card →

Playing Style Analysis

Box-to-BoxSmall Sample

A Box-to-Box. Statistically, he stands out as a capable chance creator (1.3 key passes/90), a reliable supplier (0.16 assists/90), active in the tackle (2.3 tackles/90), meticulous in distribution (85% pass accuracy), delivers dangerous crosses (2.8 accurate crosses/90), heavily involved in play (58 touches/90), draws fouls effectively (2.6/90) and active off the ball (2.9 press score/90), contributing to defensive transitions. Note: this profile is based on 548 minutes of playing time this season. The three most similar players to Xinli Peng by playing style are:

  • Jerome Deom(98% match)A Box-to-Box. Statistically, he stands out as a capable chance creator (1.5 key passes/90), an aggressive ball-winner (2.6 tackles/90), wins the ball cleanly (2.4 successful tackles/90), heavily involved in play (57 touches/90), active off the ball (2.3 press score/90), contributing to defensive transitions and top 20% creator in the league. Note: this profile is based on 490 minutes of playing time this season.
  • Yusuf Deniz Sas(98% match)A Box-to-Box. Statistically, he stands out as an elite creator (2.7 key passes/90), a reliable supplier (0.19 assists/90), active in the tackle (1.9 tackles/90), wins the physical battle (58% duel success), central to possession (71 touches/90), draws fouls effectively (2.3/90), active off the ball (2.3 press score/90), contributing to defensive transitions and top 10% creator in the league. Note: this profile is based on 469 minutes of playing time this season.
  • Vasco Sousa(98% match)A Box-to-Box. Statistically, he stands out as a capable chance creator (1.2 key passes/90), meticulous in distribution (89% pass accuracy), heavily involved in play (53 touches/90), draws fouls effectively (3.1/90) and a high-intensity presser (press score 3.9/90), constantly disrupting opposition build-up. However, he prone to committing fouls (2.9/90).

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

X
Comparison Base
Xinli Peng
MidfielderChina€2.6M
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 Xinli Peng.

Ask AI about Xinli Peng

Frequently Asked Questions

Who are the best alternatives to Xinli Peng?
The top alternatives to Xinli Peng based on AI DNA playing style analysis include: Jerome Deom, Yusuf Deniz Sas, Vasco Sousa, Jon Ander Olasagasti, Unai Vencedor. 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 Xinli Peng in 2026?
Players with a similar profile to Xinli Peng in 2026 include Jerome Deom (N/A), Yusuf Deniz Sas (N/A), Vasco Sousa (N/A). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Xinli Peng play and who plays similarly?
Xinli Peng plays as a Midfielder. Players with a comparable positional profile include Jerome Deom (Belgium, N/A); Yusuf Deniz Sas (Turkey, N/A); Vasco Sousa (Portugal, N/A); Jon Ander Olasagasti (Spain, 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.