RisingTransfers
AI DNA Similarity

Best Alternatives to Sean Longstaff

Players most similar to Sean Longstaff (Midfielder, €18.0M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Sean Longstaff

  1. 1.Joe Willock89% DNA match·Newcastle United€16.0M
  2. 2.Bruno Guimarães88% DNA match·Newcastle United€75.0M
  3. 3.Sandro Tonali88% DNA match·Newcastle United€80.0M

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

RT

Intelligence Verdict

Chances MissedTop 0%
???Bottom 1%

A Ball-Winner....

See Full Verdict + Share Card →

Playing Style Analysis

Ball-WinnerCreativeDefensive

A Ball-Winner. Statistically, he stands out as an elite creator (2.3 key passes/90), a reliable supplier (0.18 assists/90), an aggressive ball-winner (3.5 tackles/90), meticulous in distribution (85% pass accuracy), wins the physical battle (58% duel success), wins the ball cleanly (2.0 successful tackles/90), heavily involved in play (59 touches/90), active off the ball (2.4 press score/90), contributing to defensive transitions, top 10% creator in the league and top 10% tackler in the league. However, he can be exposed in 1v1 situations. The three most similar players to Sean Longstaff by playing style are:

  • Joe Willock(89% match)A Balanced Midfielder. Statistically, he stands out as a capable chance creator (1.2 key passes/90), active in the tackle (2.0 tackles/90) and active off the ball (2.9 press score/90), contributing to defensive transitions. Note: this profile is based on 852 minutes of playing time this season.
  • Bruno Guimarães(88% match)A Creator. Statistically, he stands out as an elite creator (1.8 key passes/90), a regular goalscorer (0.35 goals/90), a reliable supplier (0.20 assists/90), active in the tackle (2.2 tackles/90), meticulous in distribution (86% pass accuracy), central to possession (73 touches/90), draws fouls effectively (2.6/90), active off the ball (2.6 press score/90), contributing to defensive transitions and top 20% creator in the league. However, he loses possession under pressure (1.7 dispossessed/90).
  • Sandro Tonali(88% match)A Ball-Winner. Statistically, he stands out as a capable chance creator (1.1 key passes/90), penetrates with forward passing (9.0 final-third passes/90), central to possession (71 touches/90), uses long balls frequently (5.3/90) and active off the ball (2.7 press score/90), contributing to defensive transitions.

Transfer Intelligence

Joe Willock delivers 89% of the same playing style, at 11% lower cost (€16.0M vs €18.0M), with 1.16 key passes per 90 at age 26.

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

S
Comparison Base
Sean Longstaff
MidfielderEngland€18.0M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
J
Joe Willock
Newcastle United · Premier League
England26yContract 2027
KP/901.16
G/900.11
Balanced MidfielderSmall Sample
Last 5: ↑ Hot
vs Longstaff: 2y younger
89% match
€16.0M
#2
B
Bruno Guimarães
Newcastle United · Premier League
Brazil28yContract 2028
KP/901.80
G/900.35
CreatorCreative
Last 5: → Stable
vs Longstaff: €57M more expensive
88% match
€75.0M
#3
S
Sandro Tonali
Newcastle United · Premier League
Italy26yContract 2028
KP/901.12
G/900.00
Ball-Winner
Last 5: → Stable
vs Longstaff: €62M more expensive · 2y younger
88% match
€80.0M
#4
J
Joelinton
Newcastle United · Premier League
Brazil29yContract 2028
KP/900.78
G/900.09
Box-to-Box
Last 5: ↓ Dip
86% match
€30.0M
#5
F
Freddie Potts
West Ham United · Premier League
England22yContract 2029
KP/900.62
G/900.00
Box-to-Box
Last 5: ↑ Hot
87% match
€8.0M
#6
A
Alex Scott
AFC Bournemouth · Premier League
England22yContract 2028
KP/900.88
G/900.10
Box-to-Box
Last 5: ↑ Hot
86% match
€40.0M
#7
J
James Garner
Everton · Premier League
England25yContract 2026
KP/901.48
G/900.06
Ball-WinnerDefensive
Last 5: → Stable
86% match
€35.0M
#8
T
Tijjani Reijnders
Manchester City · Premier League
Netherlands27yContract 2030
KP/901.04
G/900.29
Last 5: → Stable
85% match
€60.0M
#9
L
Lewis Miley
Newcastle United · Premier League
England20yContract 2029
KP/900.91
G/900.06
Box-to-Box
Last 5: ↓ Dip
86% match
€25.0M
#10
Y
Youri Tielemans
Aston Villa · Premier League
Belgium29yContract 2027
KP/901.49
G/900.00
Box-to-BoxDefensive
Last 5: ↓ Dip
86% match
€35.0M
#11
D
Dominik Szoboszlai
Liverpool · Premier League
Hungary25yContract 2028
KP/902.00
G/900.18
CreatorCreative
Last 5: → Stable
85% match
€100.0M
#12
M
Mikkel Damsgaard
Brentford · Premier League
Denmark25yContract 2030
KP/901.74
G/900.18
CreatorCreative
Last 5: → Stable
85% match
€30.0M

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 Sean Longstaff.

Ask AI about Sean Longstaff

Frequently Asked Questions

Who are the best alternatives to Sean Longstaff?
The top alternatives to Sean Longstaff based on AI DNA playing style analysis include: Joe Willock, Bruno Guimarães, Sandro Tonali, Joelinton, Freddie Potts. 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 Sean Longstaff in 2026?
Players with a similar profile to Sean Longstaff in 2026 include Joe Willock (€16.0M), Bruno Guimarães (€75.0M), Sandro Tonali (€80.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Sean Longstaff play and who plays similarly?
Sean Longstaff plays as a Midfielder. Players with a comparable positional profile include Joe Willock (England, €16.0M); Bruno Guimarães (Brazil, €75.0M); Sandro Tonali (Italy, €80.0M); Joelinton (Brazil, €30.0M).
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.