RisingTransfers
AI DNA Similarity

Best Alternatives to Julian Weigl

Players most similar to Julian Weigl (Midfielder, €8.0M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Julian Weigl

  1. 1.Aleix García84% DNA match·Bayer 04 Leverkusen€20.0M
  2. 2.Robert Andrich85% DNA match·Bayer 04 Leverkusen€7.0M
  3. 3.Xaver Schlager84% DNA match·RB Leipzig€10.0M

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

RT

Intelligence Verdict

Chances MissedTop 0%
???Bottom 7%

Weigl has built a second career on being the calmest man in any midfield storm—a deep-lying...

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Playing Style Analysis

Metronome

Weigl has built a second career on being the calmest man in any midfield storm—a deep-lying conductor whose 92.5% pass accuracy and 56.9 passes per 90 place him comfortably among the league's most reliable distributors. The aerial numbers are the real surprise: a 75% win rate puts him in the top 5% of midfielders, an almost freakish figure for a player whose game is defined by ground-level control rather than physical dominance. The counterintuitive read here is that his duel win rate sits at a pedestrian average—yet his interception and tackle numbers both outperform most peers, suggesting he wins the ball through positioning and anticipation rather than brute confrontation. The three most similar players to Julian Weigl by playing style are:

  • Aleix García(84% match)A Metronome. Statistically, he stands out as meticulous in distribution (92% pass accuracy), wins the physical battle (64% duel success), heavily involved in possession (98 passes/90), penetrates with forward passing (10.8 final-third passes/90), central to possession (108 touches/90) and switches play with precision (7.1 long balls/90, 69% accuracy). Note: this profile is based on 610 minutes of playing time this season.
  • Robert Andrich(85% match)A Metronome. Statistically, he stands out as active in the tackle (1.8 tackles/90), meticulous in distribution (89% pass accuracy), wins the physical battle (67% duel success), heavily involved in possession (64 passes/90), central to possession (77 touches/90) and uses long balls frequently (7.8/90). Note: this profile is based on 498 minutes of playing time this season.
  • Xaver Schlager(84% match)A Box-to-Box. Statistically, he stands out as naturally left-footed, a capable chance creator (1.0 key passes/90), active in the tackle (1.9 tackles/90), meticulous in distribution (87% pass accuracy), heavily involved in play (66 touches/90) and active off the ball (3.0 press score/90), contributing to defensive transitions.

Transfer Intelligence

Aleix García delivers 84% of the same playing style, at a 150% premium over Julian Weigl, with 0.72 key passes per 90 at age 28.

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

J
Comparison Base
Julian Weigl
MidfielderGermany€8.0M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
A
Aleix García
Bayer 04 Leverkusen · Bundesliga
Spain28yContract 2026
KP/900.72
G/900.18
MetronomeSmall Sample
Last 5: ↑ Hot
vs Weigl: €12M more expensive · 2y younger
84% match
€20.0M
#2
R
Robert Andrich
Bayer 04 Leverkusen · Bundesliga
Germany31yContract 2028
KP/900.12
G/900.12
MetronomeSmall Sample
Last 5: ↑ Hot
85% match
€7.0M
#3
X
Xaver Schlager
RB Leipzig · Bundesliga
Austria28yContract 2026
KP/900.92
G/900.18
Box-to-Box
Last 5: ↑ Hot
vs Weigl: 2y younger
84% match
€10.0M
#4
F
Fábio Vieira
Hamburger SV · Bundesliga
Portugal25yContract 2026
KP/901.32
G/900.17
CreatorCreative
84% match
€18.0M
#5
M
Manuel Locatelli
Juventus · Serie A
Italy28yContract 2028
KP/901.88
G/900.00
MetronomeDefensive
Last 5: ↓ Dip
83% match
€25.0M
#6
N
Nicolas Seiwald
RB Leipzig · Bundesliga
Austria25yContract 2028
KP/900.58
G/900.00
Ball-WinnerDefensive
Last 5: → Stable
84% match
€22.0M
#7
E
Ellyes Skhiri
Eintracht Frankfurt · Bundesliga
Tunisia31yContract 2027
KP/900.38
G/900.00
Ball-WinnerSmall Sample
84% match
€6.0M
#8
E
Eric Martel
FC Köln · Bundesliga
Germany24yContract 2026
KP/900.93
G/900.16
MetronomeSmall Sample
83% match
€8.0M
#9
A
Aljoscha Kemlein
FC Union Berlin · Bundesliga
Germany21y
KP/901.02
G/900.00
Box-to-Box
84% match
€9.0M
#10
D
Denis Huseinbasic
FC Köln · Bundesliga
Germany24yContract 2027
KP/900.79
G/900.00
Balanced MidfielderSmall Sample
84% match
€3.0M
#11
N
Nicolai Remberg
Hamburger SV · Bundesliga
Germany25yContract 2028
KP/900.67
G/900.00
Box-to-BoxSmall Sample
84% match
€5.0M
#12
T
Tom Bischof
FC Bayern München · Bundesliga
Germany20yContract 2029
KP/901.01
G/900.00
CreatorCreative
Last 5: ↑ Hot
83% match
€40.0M

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 Julian Weigl.

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Frequently Asked Questions

Who are the best alternatives to Julian Weigl?
The top alternatives to Julian Weigl based on AI DNA playing style analysis include: Aleix García, Robert Andrich, Xaver Schlager, Fábio Vieira, 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 Julian Weigl in 2026?
Players with a similar profile to Julian Weigl in 2026 include Aleix García (€20.0M), Robert Andrich (€7.0M), Xaver Schlager (€10.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Julian Weigl play and who plays similarly?
Julian Weigl plays as a Midfielder. Players with a comparable positional profile include Aleix García (Spain, €20.0M); Robert Andrich (Germany, €7.0M); Xaver Schlager (Austria, €10.0M); Fábio Vieira (Portugal, €18.0M).
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.