RisingTransfers
AI DNA Similarity

Best Alternatives to Matte Smets

Players most similar to Matte Smets (Defender, €2.3M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Matte Smets

  1. 1.Armando Obispo83% DNA match·PSV€3.0M
  2. 2.Zeno Debast83% DNA match·Sporting CP€30.0M
  3. 3.Jozhua Vertrouwd82% DNA match·Rayo Vallecano€3.0M

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

RT

Intelligence Verdict

Chances MissedTop 0%

A Ball-Playing CB....

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Playing Style Analysis

Ball-Playing CBBall-PlayingAerial

A Ball-Playing CB. Statistically, he stands out as active in the tackle (1.9 tackles/90), commanding in the air (7.0 clearances/90), meticulous in distribution (92% pass accuracy), wins the physical battle (59% duel success), heavily involved in possession (75 passes/90), central to possession (90 touches/90), switches play with precision (9.9 long balls/90, 61% accuracy) and active off the ball (2.1 press score/90), contributing to defensive transitions. The three most similar players to Matte Smets by playing style are:

  • Armando Obispo(83% match)A Ball-Playing CB. Statistically, he stands out as naturally left-footed, a regular goalscorer (0.22 goals/90), commanding in the air (6.2 clearances/90), meticulous in distribution (90% pass accuracy), wins the physical battle (57% duel success), heavily involved in possession (66 passes/90), penetrates with forward passing (9.2 final-third passes/90), central to possession (79 touches/90), strong in aerial duels (4.3 aerials won/90) and switches play with precision (5.4 long balls/90, 62% accuracy). Note: this profile is based on 806 minutes of playing time this season.
  • Zeno Debast(83% match)A Ball-Playing CB. Statistically, he stands out as a reliable supplier (0.15 assists/90), meticulous in distribution (91% pass accuracy), wins the physical battle (56% duel success), heavily involved in possession (81 passes/90), penetrates with forward passing (11.9 final-third passes/90), central to possession (91 touches/90) and switches play with precision (10.2 long balls/90, 69% accuracy).
  • Jozhua Vertrouwd(82% match)A Ball-Playing CB. Statistically, he stands out as active in the tackle (1.8 tackles/90), meticulous in distribution (91% pass accuracy), heavily involved in possession (63 passes/90), central to possession (75 touches/90) and active off the ball (2.0 press score/90), contributing to defensive transitions. Note: this profile is based on 498 minutes of playing time this season.

Transfer Intelligence

Armando Obispo delivers 83% of the same playing style, at a 33% premium over Matte Smets, with 0.67 tackles won per 90 at age 27.

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

M
Comparison Base
Matte Smets
DefenderBelgium€2.3M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
A
Armando Obispo
PSV · Eredivisie
Netherlands27yContract 2027
Tkl/900.67
KP/900.00
Ball-Playing CBBall-Playing
Last 5: ↓ Dip
vs Smets: 5y older
83% match
€3.0M
#2
Z
Zeno Debast
Sporting CP · Liga Portugal
Belgium22yContract 2029
Tkl/901.22
KP/900.46
Ball-Playing CBBall-Playing
Last 5: → Stable
vs Smets: €28M more expensive
83% match
€30.0M
#3
J
Jozhua Vertrouwd
Rayo Vallecano · La Liga
Netherlands21yContract 2026
Tkl/901.81
KP/900.36
Ball-Playing CBBall-Playing
82% match
€3.0M
#4
J
Joël Schmied
FC Köln · Bundesliga
Switzerland27yContract 2029
Tkl/900.69
KP/900.00
Ball-Playing CBSmall Sample
82% match
€3.5M
#5
G
Gijs Smal
Feyenoord · Eredivisie
Netherlands28yContract 2028
Tkl/902.24
KP/901.60
Active Full-BackSmall Sample
Last 5: → Stable
82% match
€6.0M
#6
T
Thomas Beelen
Feyenoord · Eredivisie
Netherlands24yContract 2028
Tkl/901.24
KP/900.27
Ball-Playing CBBall-Playing
81% match
€7.0M
#7
B
Bram Nuytinck
NEC Nijmegen · Eredivisie
Netherlands36y
Tkl/901.08
KP/900.00
Ball-Playing CBBall-Playing
Last 5: → Stable
81% match
€3.0M
#8
V
Víctor Chust
Elche · La Liga
Spain26yContract 2026
Tkl/902.73
KP/900.19
Ball-Playing CBBall-Playing
Last 5: ↑ Hot
82% match
€4.0M
#9
J
Jan Paul van Hecke
Brighton & Hove Albion · Premier League
Netherlands25yContract 2027
Tkl/901.54
KP/900.36
Ball-Playing CBBall-Playing
Last 5: → Stable
81% match
€35.0M
#10
A
Anel Ahmedhodzic
Feyenoord · Eredivisie
Bosnia and Herzegovina27yContract 2029
Tkl/901.10
KP/900.53
Ball-Playing CBBall-Playing
Last 5: → Stable
81% match
€12.0M
#11
S
Samson Baidoo
Lens · Ligue 1
Austria22yContract 2030
Tkl/901.98
KP/900.28
Ball-Playing CBAerial
Last 5: → Stable
81% match
€12.0M
#12
G
Gustaf Lagerbielke
Sporting Braga · Liga Portugal
Sweden26yContract 2030
Tkl/901.02
KP/900.31
Ball-Playing CBBall-Playing
Last 5: ↑ Hot
82% match
€5.0M

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 Matte Smets.

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Frequently Asked Questions

Who are the best alternatives to Matte Smets?
The top alternatives to Matte Smets based on AI DNA playing style analysis include: Armando Obispo, Zeno Debast, Jozhua Vertrouwd, Joël Schmied, Gijs Smal. 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 Matte Smets in 2026?
Players with a similar profile to Matte Smets in 2026 include Armando Obispo (€3.0M), Zeno Debast (€30.0M), Jozhua Vertrouwd (€3.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Matte Smets play and who plays similarly?
Matte Smets plays as a Defender. Players with a comparable positional profile include Armando Obispo (Netherlands, €3.0M); Zeno Debast (Belgium, €30.0M); Jozhua Vertrouwd (Netherlands, €3.0M); Joël Schmied (Switzerland, €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.