RisingTransfers
AI DNA Similarity

Best Alternatives to Jonathan de Guzmán

Players most similar to Jonathan de Guzmán (Midfielder, €1.0M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Jonathan de Guzmán

  1. 1.Jens Toornstra87% DNA match·Sparta Rotterdam€3.5M
  2. 2.Kees Smit84% DNA match·AZ€22.0M
  3. 3.Jerdy Schouten83% DNA match·PSV€22.0M

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

RT

Intelligence Verdict

???Bottom 14%

De Guzmán remains the Eredivisie’s quintessential metronome...

See Full Verdict + Share Card →

Playing Style Analysis

Creator

De Guzmán remains the Eredivisie’s quintessential metronome, a veteran technician whose game has evolved from the dynamic box-to-box threat of his youth into a sophisticated, deep-lying conductor for a mid-table side. Averaging over 50 passes per 90, he sits comfortably in the league’s top 20% for volume, dictating tempo with a composure that compensates for his declining mobility. While his goal output has evaporated, his underlying creative metrics remain sharp; his 1.25 key passes per 90 and assist rate in the top 30% prove his vision hasn't dimmed. The three most similar players to Jonathan de Guzmán by playing style are:

  • Jens Toornstra(87% match)A Creator. Statistically, he stands out as a capable chance creator (1.3 key passes/90), active in the tackle (2.4 tackles/90), heavily involved in play (54 touches/90), uses long balls frequently (6.0/90) and active off the ball (2.6 press score/90), contributing to defensive transitions. Note: this profile is based on 782 minutes of playing time this season.
  • Kees Smit(84% match)A Box-to-Box. Statistically, he stands out as an elite creator (1.6 key passes/90), meticulous in distribution (88% pass accuracy), heavily involved in play (67 touches/90) and active off the ball (2.5 press score/90), contributing to defensive transitions. However, he loses possession under pressure (1.6 dispossessed/90).
  • Jerdy Schouten(83% match)Schouten is the kind of midfielder who makes a team's possession game feel inevitable rather than effortful—a metronome who rarely wastes a touch and almost never wastes a pass. His 92.3% pass accuracy and 75.3 passes per 90 both sit in the Eredivisie's top five percent, meaning he isn't just accurate, he's relentlessly active with the ball. The 10.32 passes into the final third per 90 is the counterintuitive number here: for a player who reads as a deep-lying anchor, he's consistently threading the needle forward, ranking top ten in the league.

Transfer Intelligence

Jens Toornstra delivers 87% of the same playing style, at a 250% premium over Jonathan de Guzmán, with 1.27 key passes per 90 at age 37.

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

J
Comparison Base
Jonathan de Guzmán
MidfielderNetherlands€1.0M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
J
Jens Toornstra
Sparta Rotterdam · Eredivisie
Netherlands37y
KP/901.27
G/900.00
CreatorSmall Sample
Last 5: ↓ Dip
87% match
€3.5M
#2
K
Kees Smit
AZ · Eredivisie
Netherlands20yContract 2028
KP/901.56
G/900.13
Box-to-BoxCreative
Last 5: → Stable
vs Guzmán: €21M more expensive · 18y younger
84% match
€22.0M
#3
J
Jerdy Schouten
PSV · Eredivisie
Netherlands29yContract 2030
KP/900.91
G/900.04
Metronome
Last 5: → Stable
vs Guzmán: €21M more expensive · 9y younger
83% match
€22.0M
#4
J
Joey Veerman
PSV · Eredivisie
Netherlands27yContract 2028
KP/903.64
G/900.31
CreatorCreative
Last 5: ↓ Dip
83% match
€27.0M
#5
R
Ramiz Zerrouki
FC Twente · Eredivisie
Algeria27y
KP/901.56
G/900.11
MetronomeCreative
Last 5: ↑ Hot
83% match
€7.2M
#6
K
Kian Fitz-Jim
Ajax · Eredivisie
Netherlands22yContract 2027
KP/900.70
G/900.17
MetronomeDefensive
Last 5: → Stable
83% match
€3.5M
#7
M
Marc Aguado
Elche · La Liga
Spain26yContract 2027
KP/900.31
G/900.00
Ball-Winner
Last 5: ↑ Hot
82% match
€3.0M
#8
F
Federico Redondo
Elche · La Liga
Argentina23yContract 2030
KP/901.09
G/900.27
Chance CreatorDeep Distributor
83% match
€4.0M
#9
P
Peer Koopmeiners
AZ · Eredivisie
Netherlands26yContract 2028
KP/901.10
G/900.09
Ball-WinnerDefensive
Last 5: → Stable
83% match
€10.0M
#10
M
Matej Sin
AZ · Eredivisie
Czech Republic21yContract 2030
KP/901.99
G/900.17
CreatorCreative
Last 5: ↑ Hot
82% match
€4.0M
#11
S
Sem Steijn
Feyenoord · Eredivisie
Netherlands24yContract 2029
KP/902.65
G/900.58
CreatorCreative
82% match
€14.0M
#12
K
Kodai Sano
Japan · Eredivisie
Japan22yContract 2028
KP/901.49
G/900.09
Box-to-Box
Last 5: → Stable
81% match
€6.0M

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 Jonathan de Guzmán.

Ask AI about Jonathan de Guzmán

Frequently Asked Questions

Who are the best alternatives to Jonathan de Guzmán?
The top alternatives to Jonathan de Guzmán based on AI DNA playing style analysis include: Jens Toornstra, Kees Smit, Jerdy Schouten, Joey Veerman, Ramiz Zerrouki. 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 Jonathan de Guzmán in 2026?
Players with a similar profile to Jonathan de Guzmán in 2026 include Jens Toornstra (€3.5M), Kees Smit (€22.0M), Jerdy Schouten (€22.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Jonathan de Guzmán play and who plays similarly?
Jonathan de Guzmán plays as a Midfielder. Players with a comparable positional profile include Jens Toornstra (Netherlands, €3.5M); Kees Smit (Netherlands, €22.0M); Jerdy Schouten (Netherlands, €22.0M); Joey Veerman (Netherlands, €27.0M).
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.