AI DNA Similarity
Best Alternatives to Francisco Sierralta
Players most similar to Francisco Sierralta (Defender, €1.5M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.
Top 3 Alternatives to Francisco Sierralta
- 1.Mohammed Salisu86% DNA match·Monaco€10.0M
- 2.Moussa Niakhaté85% DNA match·Olympique Lyonnais€15.0M
- 3.Montassar Talbi85% DNA match·Lorient€7.0M
Ranked by AI DNA similarity — 768 dimensions across playing style, pressing intensity, and tactical fit.
RT
Intelligence Verdict
Chances MissedTop 0%
???Bottom 0%
“A Ball-Playing CB....”
See Full Verdict + Share Card →Playing Style Analysis
Ball-Playing CBAerialSmall Sample
A Ball-Playing CB. Statistically, he stands out as commanding in the air (6.4 clearances/90), meticulous in distribution (91% pass accuracy) and wins the physical battle (69% duel success). Note: this profile is based on 450 minutes of playing time this season. The three most similar players to Francisco Sierralta by playing style are:
- Mohammed Salisu(86% match) — A Ball-Playing CB. Statistically, he stands out as naturally left-footed, active in the tackle (2.1 tackles/90), commanding in the air (7.0 clearances/90), meticulous in distribution (88% pass accuracy), wins the physical battle (58% duel success), heavily involved in possession (68 passes/90), blocks shots courageously (1.8 blocks/90), central to possession (84 touches/90), dominant in the air (3.2 aerials won/90, 68%), uses long balls frequently (5.9/90) and active off the ball (2.5 press score/90), contributing to defensive transitions.
- Moussa Niakhaté(85% match) — A Ball-Playing CB. Statistically, he stands out as naturally left-footed, commanding in the air (6.4 clearances/90), meticulous in distribution (91% pass accuracy), wins the physical battle (70% duel success), penetrates with forward passing (9.0 final-third passes/90), central to possession (75 touches/90), dominant in the air (3.6 aerials won/90, 71%) and active off the ball (2.2 press score/90), contributing to defensive transitions. Note: this profile is based on 733 minutes of playing time this season.
- Montassar Talbi(85% match) — A Ball-Playing CB. Statistically, he stands out as commanding in the air (6.4 clearances/90), meticulous in distribution (92% pass accuracy) and wins the physical battle (64% duel success).
Transfer Intelligence
Mohammed Salisu delivers 86% of the same playing style, at a 567% premium over Francisco Sierralta, with 0.20 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 →
Similar Players — Ranked by DNA Similarity
#1
M
Mohammed Salisu
Monaco · Ligue 1
Ghana27yContract 2028
Tkl/900.20
KP/900.20
Ball-Playing CBBall-Playing
vs Sierralta: €9M more expensive · 2y younger
86% match
€10.0M
#2
M
Moussa Niakhaté
Olympique Lyonnais · Ligue 1
Senegal30yContract 2028
Tkl/900.85
KP/900.36
Ball-Playing CBBall-Playing
Last 5: ↑ Hotvs Sierralta: €14M more expensive
85% match
€15.0M
#3
M
Montassar Talbi
Lorient · Ligue 1
Tunisia27yContract 2027
Tkl/900.70
KP/900.30
Ball-Playing CBAerial
vs Sierralta: €6M more expensive · 2y younger
85% match
€7.0M
#4
K
Kevin Danso
Tottenham Hotspur · Premier League
Austria27yContract 2030
Tkl/901.84
KP/900.20
Ball-Playing CBBall-Playing
Last 5: → Stable86% match
€22.0M
#5
D
Duje Caleta-Car
Real Sociedad · La Liga
Croatia29yContract 2026
Tkl/900.88
KP/900.20
Ball-Playing CBBall-Playing
Last 5: ↑ Hot86% match
€3.0M
#6
K
Kristoffer Ajer
Brentford · Premier League
Norway28yContract 2028
Tkl/901.87
KP/900.26
Physical StopperAerial
Last 5: ↓ Dip85% match
€18.0M
#7
A
Arouna Sangante
Le Havre · Ligue 1
Senegal24yContract 2026
Tkl/900.88
KP/900.29
Physical StopperAerial
85% match
€8.0M
#8
D
Dan Ballard
Sunderland · Premier League
Northern Ireland26yContract 2028
Tkl/901.38
KP/900.67
Ball-Playing CBAerial
Last 5: → Stable84% match
€20.0M
#9
A
Antoine Mendy
Nice · Ligue 1
France21yContract 2028
Tkl/903.14
KP/900.00
Ball-Playing CBAerial
Last 5: → Stable84% match
€4.0M
#10
B
Brendan Chardonnet
Brest · Ligue 1
France31yContract 2027
Tkl/900.97
KP/900.32
Ball-Playing CBAerial
84% match
€5.0M
#11
P
Pascal Struijk
Leeds United · Premier League
Netherlands26yContract 2027
Tkl/901.17
KP/900.38
Ball-Playing CBBall-Playing
Last 5: → Stable84% match
€22.0M
#12
M
Malick Thiaw
Newcastle United · Premier League
Germany24yContract 2029
Tkl/901.29
KP/900.19
Ball-Playing CBAerial
Last 5: ↑ Hot84% match
€45.0M
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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 Francisco Sierralta.
Ask AI about Francisco Sierralta →Frequently Asked Questions
Who are the best alternatives to Francisco Sierralta?▼
The top alternatives to Francisco Sierralta based on AI DNA playing style analysis include: Mohammed Salisu, Moussa Niakhaté, Montassar Talbi, Kevin Danso, Duje Caleta-Car. 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 Francisco Sierralta in 2026?▼
Players with a similar profile to Francisco Sierralta in 2026 include Mohammed Salisu (€10.0M), Moussa Niakhaté (€15.0M), Montassar Talbi (€7.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Francisco Sierralta play and who plays similarly?▼
Francisco Sierralta plays as a Defender. Players with a comparable positional profile include Mohammed Salisu (Ghana, €10.0M); Moussa Niakhaté (Senegal, €15.0M); Montassar Talbi (Tunisia, €7.0M); Kevin Danso (Austria, €22.0M).
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.