RisingTransfers
AI DNA Similarity

Best Alternatives to Angelo Stiller

Players most similar to Angelo Stiller (Midfielder, €45.0M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Angelo Stiller

  1. 1.Maximilian Eggestein86% DNA match·SC Freiburg€10.0M
  2. 2.Aleksandar Pavlovic87% DNA match·FC Bayern München€75.0M
  3. 3.Patrick Sontheimer86% DNA match·Saarbrücken€5.0M

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

RT

Intelligence Verdict

Key PassesTop 4%
???Bottom 0%

A Metronome....

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Playing Style Analysis

MetronomeCreativeSmall Sample

A Metronome. Statistically, he stands out as an elite creator (2.0 key passes/90), a prolific assist provider (0.29 assists/90), meticulous in distribution (88% pass accuracy), wins the physical battle (63% duel success), heavily involved in possession (89 passes/90), penetrates with forward passing (12.4 final-third passes/90), central to possession (104 touches/90), uses long balls frequently (6.8/90), active off the ball (2.9 press score/90), contributing to defensive transitions and top 20% creator in the league. Note: this profile is based on 625 minutes of playing time this season. The three most similar players to Angelo Stiller by playing style are:

  • Maximilian Eggestein(86% match)A Ball-Winner. Statistically, he stands out as an aggressive ball-winner (3.0 tackles/90), wins the physical battle (58% duel success), heavily involved in play (61 touches/90), active off the ball (2.2 press score/90), contributing to defensive transitions and top 10% tackler in the league. Note: this profile is based on 630 minutes of playing time this season.
  • Aleksandar Pavlovic(87% match)A Metronome. Statistically, he stands out as comfortable with both feet, a capable chance creator (1.4 key passes/90), active in the tackle (1.8 tackles/90), meticulous in distribution (95% pass accuracy), heavily involved in possession (102 passes/90), penetrates with forward passing (12.1 final-third passes/90), central to possession (115 touches/90), switches play with precision (5.8 long balls/90, 80% accuracy) and active off the ball (2.5 press score/90), contributing to defensive transitions.
  • Patrick Sontheimer(86% match)A Box-to-Box. Statistically, he stands out as an elite creator (1.5 key passes/90), a reliable supplier (0.18 assists/90), an aggressive ball-winner (2.7 tackles/90) and top 20% creator in the league.

Transfer Intelligence

Maximilian Eggestein delivers 86% of the same playing style, at 78% lower cost (€10.0M vs €45.0M), with 0.56 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 →

A
Comparison Base
Angelo Stiller
MidfielderGermany€45.0M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
M
Maximilian Eggestein
SC Freiburg · Bundesliga
Germany29y
KP/900.56
G/900.16
Ball-WinnerDefensive
Last 5: ↓ Dip
vs Stiller: €35M cheaper · 4y older
86% match
€10.0M
#2
A
Aleksandar Pavlovic
FC Bayern München · Bundesliga
Germany22yContract 2029
KP/901.30
G/900.08
Metronome
Last 5: → Stable
vs Stiller: €30M more expensive · 3y younger
87% match
€75.0M
#3
P
Patrick Sontheimer
Saarbrücken · Bundesliga
Germany27yContract 2027
KP/901.54
G/900.00
Box-to-BoxCreative
vs Stiller: €40M cheaper · 2y older
86% match
€5.0M
#4
A
Aljoscha Kemlein
FC Union Berlin · Bundesliga
Germany21y
KP/901.02
G/900.00
Box-to-Box
86% match
€9.0M
#5
E
Ezequiel Fernández
Bayer 04 Leverkusen · Bundesliga
Argentina23yContract 2030
KP/901.03
G/900.00
Ball-WinnerDefensive
Last 5: ↓ Dip
86% match
€25.0M
#6
L
Leon Avdullahu
TSG Hoffenheim · Bundesliga
Switzerland22yContract 2029
KP/901.02
G/900.00
MetronomeSmall Sample
Last 5: ↑ Hot
85% match
€17.0M
#7
R
Romano Schmid
Werder Bremen · Bundesliga
Austria26yContract 2025
KP/902.69
G/900.08
CreatorCreative
85% match
€17.0M
#8
F
Fabian Rieder
FC Augsburg · Bundesliga
Switzerland24yContract 2030
KP/901.14
G/900.13
CreatorSmall Sample
Last 5: → Stable
85% match
€8.0M
#9
T
Tom Bischof
FC Bayern München · Bundesliga
Germany20yContract 2029
KP/901.01
G/900.00
CreatorCreative
Last 5: ↑ Hot
85% match
€40.0M
#10
E
Eric Martel
FC Köln · Bundesliga
Germany24yContract 2026
KP/900.93
G/900.16
MetronomeSmall Sample
84% match
€8.0M
#11
K
Kevin Stöger
Borussia Mönchengladbach · Bundesliga
Austria32yContract 2027
KP/901.56
G/900.00
CreatorCreative
85% match
€3.0M
#12
R
Robert Andrich
Bayer 04 Leverkusen · Bundesliga
Germany31yContract 2028
KP/900.12
G/900.12
MetronomeSmall Sample
Last 5: ↑ Hot
85% match
€7.0M

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 Angelo Stiller.

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Frequently Asked Questions

Who are the best alternatives to Angelo Stiller?
The top alternatives to Angelo Stiller based on AI DNA playing style analysis include: Maximilian Eggestein, Aleksandar Pavlovic, Patrick Sontheimer, Aljoscha Kemlein, Ezequiel Fernández. 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 Angelo Stiller in 2026?
Players with a similar profile to Angelo Stiller in 2026 include Maximilian Eggestein (€10.0M), Aleksandar Pavlovic (€75.0M), Patrick Sontheimer (€5.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Angelo Stiller play and who plays similarly?
Angelo Stiller plays as a Midfielder. Players with a comparable positional profile include Maximilian Eggestein (Germany, €10.0M); Aleksandar Pavlovic (Germany, €75.0M); Patrick Sontheimer (Germany, €5.0M); Aljoscha Kemlein (Germany, €9.0M).
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.