RisingTransfers
AI DNA Similarity

Best Alternatives to Patrik Wålemark

Players most similar to Patrik Wålemark (Attacker, €1.8M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Patrik Wålemark

  1. 1.Szymon Wlodarczyk84% DNA match·Excelsior€3.0M
  2. 2.Ahmed Kutucu82% DNA match·Galatasaray€4.0M
  3. 3.Ayase Ueda82% DNA match·Feyenoord€15.0M

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

RT

Intelligence Verdict

ShotsTop 9%
???Bottom 8%

Wålemark is a high-volume executioner masquerading as a facilitator...

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Playing Style Analysis

PoacherProlific

Wålemark is a high-volume executioner masquerading as a facilitator, operating with a clinical efficiency that feels almost out of place in the chaotic landscape of the Ekstraklasa. While his 100% pass accuracy suggests a cautious recycler of possession, the reality is far more predatory; he ranks in the top 5% for goals per 90 and the top 10% for shot volume, proving he isn’t just finding teammates—he’s finding the back of the net with relentless frequency. The counterintuitive truth of his 1.39 key passes per 90 is that he isn't a traditional playmaker, but rather a high-IQ technician who refuses to waste the ball. The three most similar players to Patrik Wålemark by playing style are:

  • Szymon Wlodarczyk(84% match)A Target Man. Statistically, he stands out as a capable chance creator (1.3 key passes/90), a constant goal threat (2.5 shots/90) and a regular goalscorer (0.36 goals/90). However, he loses possession under pressure (1.7 dispossessed/90).
  • Ahmed Kutucu(82% match)Kutucu is the Turkish Süper Lig’s most efficient offensive enigma, a high-volume shooter who combines the clinical finishing of a veteran poacher with the creative vision of a seasoned playmaker. While playing for a Tier C side, his output is undeniably elite; he ranks in the top 5% of the league for both shots and assists per 90, proving he is as much a facilitator as he is a finisher. The data reveals a fascinating paradox: despite being an "Attacker" by trade, his 85.7% pass accuracy puts him in the top 10% of his peers, suggesting he operates more as a sophisticated offensive hub than a traditional line-leader.
  • Ayase Ueda(82% match)A Complete Forward. Statistically, he stands out as a constant goal threat (3.6 shots/90), a proven goalscorer (0.89 goals/90), strong in aerial duels (3.5 aerials won/90), draws fouls effectively (2.1/90) and top 10% scorer in the league.

Transfer Intelligence

Szymon Wlodarczyk delivers 84% of the same playing style, at a 67% premium over Patrik Wålemark, with 0.36 goals per 90 at age 23. That's 64% of Patrik Wålemark's output in goals per 90 — a credible like-for-like option.

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

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Comparison Base
Patrik Wålemark
AttackerSweden€1.8M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
S
Szymon Wlodarczyk
Excelsior · Eredivisie
Poland23y
G/900.36
A/900.12
Target ManSmall Sample
Last 5: ↑ Hot
vs Wålemark: -0.20 G/90
84% match
€3.0M
#2
A
Ahmed Kutucu
Galatasaray · Super Lig
Turkey26yContract 2028
G/900.49
A/900.37
Inside ForwardProlific
vs Wålemark: 2y older · -0.07 G/90
82% match
€4.0M
#3
A
Ayase Ueda
Feyenoord · Eredivisie
Japan27yContract 2028
G/900.89
A/900.04
Complete ForwardProlific
Last 5: ↓ Dip
vs Wålemark: €13M more expensive · 3y older · +0.33 G/90
82% match
€15.0M
#4
V
Viktor Gyökeres
Arsenal · Premier League
Sweden27yContract 2030
G/900.57
A/900.04
PoacherProlific
Last 5: → Stable
81% match
€65.0M
#5
M
Mikkel Duelund
Vejle Boldklub · Superliga
Denmark28y
G/900.41
A/900.14
Complete ForwardProlific
Last 5: ↑ Hot
81% match
€4.0M
#6
Y
Yassir Zabiri
Rennes · Liga Portugal
Morocco21yContract 2029
G/900.55
A/900.00
PoacherProlific
81% match
€4.0M
#7
A
Andreas Schjelderup
Benfica · Liga Portugal
Norway21yContract 2028
G/900.38
A/900.27
Inside ForwardProlific
Last 5: ↑ Hot
81% match
€14.0M
#8
C
Clayton
Rio Ave · Liga Portugal
Brazil27y
G/900.56
A/900.23
PoacherProlific
Last 5: ↓ Dip
81% match
€3.0M
#9
A
Alassane Pléa
PSV · Eredivisie
France33yContract 2028
G/900.52
A/900.23
Complete ForwardProlific
80% match
€3.0M
#10
V
Vedat Muriqi
Mallorca · La Liga
Kosovo32yContract 2029
G/900.71
A/900.00
Complete ForwardProlific
Last 5: → Stable
80% match
€4.5M
#11
E
Esmir Bajraktarevic
PSV · Eredivisie
United States21yContract 2029
G/900.34
A/900.34
Inside Forward
Last 5: ↑ Hot
81% match
€5.0M
#12
E
Eli Kroupi
AFC Bournemouth · Premier League
France19yContract 2030
G/900.70
A/900.00
PoacherProlific
Last 5: → Stable
81% match
€22.0M

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 Patrik Wålemark.

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Frequently Asked Questions

Who are the best alternatives to Patrik Wålemark?
The top alternatives to Patrik Wålemark based on AI DNA playing style analysis include: Szymon Wlodarczyk, Ahmed Kutucu, Ayase Ueda, Viktor Gyökeres, Mikkel Duelund. 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 Patrik Wålemark in 2026?
Players with a similar profile to Patrik Wålemark in 2026 include Szymon Wlodarczyk (€3.0M), Ahmed Kutucu (€4.0M), Ayase Ueda (€15.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Patrik Wålemark play and who plays similarly?
Patrik Wålemark plays as a Attacker. Players with a comparable positional profile include Szymon Wlodarczyk (Poland, €3.0M); Ahmed Kutucu (Turkey, €4.0M); Ayase Ueda (Japan, €15.0M); Viktor Gyökeres (Sweden, €65.0M).
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.