⚡ G-SCORE — Scouting Dashboard
766 players analyzed · 5 leagues · Contextual score updated daily
766 players
⚡ G-SCORE — Contextual index: 60% base (xG, rating, regularity) · 25% context (opponent, home/away) · 15% momentum (form, age)
1
Erling Haaland
Manchester City · F S
⚽83
G 26
xG 28.6
A 8
xA 5.1
Rtg 7.7
Age 25
T10 —
🏠 Aston Villa
2
Bruno Fernandes
Manchester United · M
🎯82
G 8
xG 11.4
A 19
xA 15.9
Rtg 8
Age 32
T10 —
🏠 Nottingham Forest
3
Nico Paz
Como · F M S
⚖️77
G 12
xG 8.9
A 6
xA 7
Rtg 7.7
Age 21
T10 —
🏠 Parma
4
Kylian Mbappé
Real Madrid ·
⚖️75
G 24
xG 23.2
A —
xA 6.1
Rtg 8.1
Age 26
T10 —
✈️ Sevilla
5
Kenan Yildiz
Juventus · F M S
⚖️75
G 10
xG 8.5
A 6
xA 10.6
Rtg 7.5
Age 21
T10 —
🏠 Fiorentina
6
Pablo Pagis
Lorient · F M S
⚖️73
G 10
xG 7.7
A 5
xA 4.8
Rtg 7.3
Age 24
T10 —
🏠 Le Havre
7
Florian Thauvin
Lens · M S
⚖️72
G 10
xG 11.7
A 5
xA 9.7
Rtg 7.4
Age 33
T10 —
🏠 Nice
8
Igor Thiago
Brentford ·
⚽71
G 22
xG 20.4
A —
xA —
Rtg 7.2
Age 25
T10 —
🏠 Crystal Palace
9
Antoine Semenyo
Manchester City · F M
⚖️71
G 16
xG 12.4
A 4
xA 4
Rtg 7.4
Age 26
T10 —
🏠 Aston Villa
10
Matias Fernandez-Pardo
Lille · F M S
⚖️71
G 8
xG 11
A 5
xA 4.8
Rtg 7.2
Age 21
T10 —
🏠 Auxerre
11
Vinícius Júnior
Real Madrid · F M S
⚖️70
G 15
xG 14.2
A 5
xA 6.5
Rtg 7.7
Age 26
T10 —
✈️ Sevilla
12
Lamine Yamal
Barcelona · F M S
🏃70
G 16
xG 14.7
A 11
xA 12.6
Rtg 8.3
Age 18
T10 —
Next —
13
João Pedro
Chelsea · F M S
⚽69
G 15
xG 15.2
A 5
xA 4
Rtg 7.2
Age 25
T10 —
✈️ Sunderland
14
Bukayo Saka
Arsenal · F M S
⚖️69
G 7
xG 8.5
A 4
xA 8.1
Rtg 7.5
Age 24
T10 —
✈️ Crystal Palace
15
Mikel Oyarzabal
Real Sociedad · F S
⚽69
G 15
xG 16
A 3
xA 5.6
Rtg 7.3
Age 29
T10 —
🏠 Valencia
16
Benjamin Sesko
Manchester United · F S
⚽68
G 11
xG 12.5
A 1
xA 0.6
Rtg —
Age 23
T10 —
🏠 Nottingham Forest
17
Wesley Saïd
Lens · M S
⚽68
G 10
xG 11.5
A 2
xA 5.8
Rtg 7.2
Age 31
T10 —
🏠 Nice
18
Michael Olise
Bayern München · M S
⚖️68
G 15
xG 11.4
A 19
xA 17
Rtg 8.1
Age 23
T10 —
Next —
19
Deniz Undav
VfB Stuttgart · F M S
⚽68
G 19
xG 19
A 5
xA 5
Rtg 7.5
Age 30
T10 —
✈️ Bayern
20
Harry Wilson
Fulham · M S
⚽66
G 10
xG 5.6
A 6
xA 5.5
Rtg 7.2
Age 29
T10 —
✈️ Wolverhampton
21
Kiernan Dewsbury-Hall
Everton · M S
⚖️66
G 8
xG 4.3
A 4
xA 5.9
Rtg 7.2
Age 28
T10 —
🏠 Sunderland
22
Bruno Guimarães
Newcastle United · M S
⚖️66
G 9
xG 6.5
A 5
xA 3.9
Rtg 7.6
Age 29
T10 —
🏠 West Ham United
23
Arda Güler
Real Madrid · F M S
🎯66
G —
xG 4.8
A 9
xA 8.9
Rtg 7.5
Age 20
T10 —
✈️ Sevilla
24
Phil Foden
Manchester City · F M S
⚖️65
G 7
xG 6.7
A 5
xA 5.9
Rtg 7.4
Age 26
T10 —
🏠 Aston Villa
25
Jérémy Doku
Manchester City · F M S
🏃65
G —
xG 3.8
A 5
xA 7.5
Rtg 7.4
Age 24
T10 —
🏠 Aston Villa
26
Hákon Arnar Haraldsson
Lille ·
⚽65
G 8
xG 7.8
A —
xA 4.9
Rtg 7.2
Age 23
T10 —
🏠 Auxerre
27
Mamadou Sangaré
Lens · M S
⚖️65
G —
xG 3.3
A 4
xA 2.8
Rtg 7.7
Age 24
T10 —
🏠 Nice
28
Vedat Muriqi
Mallorca · F S
⚽65
G 22
xG 19.7
A 1
xA 1.8
Rtg 7.2
Age 32
T10 —
✈️ Levante UD
29
Abdessamad Ezzalzouli
Real Betis · F M S
⚽65
G 9
xG 9.7
A 8
xA 6.1
Rtg 7.6
Age 25
T10 —
✈️ FC Barcelona
30
Nikola Krstovic
Atalanta · F S
⚽65
G 10
xG 15.2
A 5
xA 3.2
Rtg —
Age 26
T10 —
🏠 Bologna
| # | Player | Profil | G-SCORE ↓ | G | xG | Sh | Ass | xA | KP | 🛡️ + | 🔄 + | Rtg | T10 | R10 | Age | Next |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Erling Haaland Manchester City | ⚽ | 83 | 26 | 28.6 | 123 | 8 | 5.1 | 24 | — | 13 | 7.7 | — | — | 25 | 🏠 Aston Villa 1.49 xGA/m |
| 2 | Bruno Fernandes Manchester United | 🎯 | 82 | 8 | 11.4 | 79 | 19 | 15.9 | 125 | — | 15 | 8 | — | — | 32 | 🏠 Nottingham Forest 1.64 xGA/m |
| 3 | Nico Paz Como | ⚖️ | 77 | 12 | 8.9 | 121 | 6 | 7 | 51 | — | 60 | 7.7 | — | — | 21 | 🏠 Parma 1.61 xGA/m |
| 4 | Kylian Mbappé Real Madrid | ⚖️ | 75 | 24 | 23.2 | — | — | 6.1 | — | — | 68 | 8.1 | — | — | 26 | ✈️ Sevilla 1.62 xGA/m |
| 5 | Kenan Yildiz Juventus | ⚖️ | 75 | 10 | 8.5 | 93 | 6 | 10.6 | 73 | — | 70 | 7.5 | — | — | 21 | 🏠 Fiorentina 1.5 xGA/m |
| 6 | Pablo Pagis Lorient | ⚖️ | 73 | 10 | 7.7 | 56 | 5 | 4.8 | 43 | — | 19 | 7.3 | — | — | 24 | 🏠 Le Havre 1.65 xGA/m |
| 7 | Florian Thauvin Lens | ⚖️ | 72 | 10 | 11.7 | 72 | 5 | 9.7 | 78 | — | 44 | 7.4 | — | — | 33 | 🏠 Nice 1.82 xGA/m |
| 8 | Igor Thiago Brentford | ⚽ | 71 | 22 | 20.4 | — | — | — | — | — | 26 | 7.2 | — | — | 25 | 🏠 Crystal Palace 1.52 xGA/m |
| 9 | Antoine Semenyo Manchester City | ⚖️ | 71 | 16 | 12.4 | 81 | 4 | 4 | 36 | — | 43 | 7.4 | — | — | 26 | 🏠 Aston Villa 1.49 xGA/m |
| 10 | ⚖️ | 71 | 8 | 11 | 66 | 5 | 4.8 | 35 | — | 29 | 7.2 | — | — | 21 | 🏠 Auxerre 1.48 xGA/m | |
| 11 | Vinícius Júnior Real Madrid | ⚖️ | 70 | 15 | 14.2 | 98 | 5 | 6.5 | 67 | — | 75 | 7.7 | — | — | 26 | ✈️ Sevilla 1.62 xGA/m |
| 12 | Lamine Yamal Barcelona | 🏃 | 70 | 16 | 14.7 | 117 | 11 | 12.6 | 73 | — | 133 | 8.3 | — | — | 18 | — |
| 13 | João Pedro Chelsea | ⚽ | 69 | 15 | 15.2 | 71 | 5 | 4 | 29 | — | 28 | 7.2 | — | — | 25 | ✈️ Sunderland 1.61 xGA/m |
| 14 | Bukayo Saka Arsenal | ⚖️ | 69 | 7 | 8.5 | 68 | 4 | 8.1 | 61 | — | 49 | 7.5 | — | — | 24 | ✈️ Crystal Palace 1.52 xGA/m |
| 15 | Mikel Oyarzabal Real Sociedad | ⚽ | 69 | 15 | 16 | 78 | 3 | 5.6 | 41 | — | 29 | 7.3 | — | — | 29 | 🏠 Valencia 1.47 xGA/m |
| 16 | Benjamin Sesko Manchester United | ⚽ | 68 | 11 | 12.5 | 61 | 1 | 0.6 | 9 | — | 7 | — | — | — | 23 | 🏠 Nottingham Forest 1.64 xGA/m |
| 17 | Wesley Saïd Lens | ⚽ | 68 | 10 | 11.5 | 55 | 2 | 5.8 | 26 | — | 22 | 7.2 | — | — | 31 | 🏠 Nice 1.82 xGA/m |
| 18 | Michael Olise Bayern München | ⚖️ | 68 | 15 | 11.4 | 101 | 19 | 17 | 79 | — | 54 | 8.1 | — | — | 23 | — |
| 19 | Deniz Undav VfB Stuttgart | ⚽ | 68 | 19 | 19 | 123 | 5 | 5 | 34 | — | 6 | 7.5 | — | — | 30 | ✈️ Bayern 1.33 xGA/m |
| 20 | Harry Wilson Fulham | ⚽ | 66 | 10 | 5.6 | 74 | 6 | 5.5 | 36 | — | 14 | 7.2 | — | — | 29 | ✈️ Wolverhampton 1.82 xGA/m |
| 21 | Kiernan Dewsbury-Hall Everton | ⚖️ | 66 | 8 | 4.3 | 37 | 4 | 5.9 | 33 | — | 24 | 7.2 | — | — | 28 | 🏠 Sunderland 1.61 xGA/m |
| 22 | Bruno Guimarães Newcastle United | ⚖️ | 66 | 9 | 6.5 | 39 | 5 | 3.9 | 44 | — | 12 | 7.6 | — | — | 29 | 🏠 West Ham United 1.78 xGA/m |
| 23 | Arda Güler Real Madrid | 🎯 | 66 | — | 4.8 | 49 | 9 | 8.9 | 70 | — | 26 | 7.5 | — | — | 20 | ✈️ Sevilla 1.62 xGA/m |
| 24 | Phil Foden Manchester City | ⚖️ | 65 | 7 | 6.7 | 55 | 5 | 5.9 | 50 | — | 22 | 7.4 | — | — | 26 | 🏠 Aston Villa 1.49 xGA/m |
| 25 | Jérémy Doku Manchester City | 🏃 | 65 | — | 3.8 | 36 | 5 | 7.5 | 57 | — | 65 | 7.4 | — | — | 24 | 🏠 Aston Villa 1.49 xGA/m |
| 26 | ⚽ | 65 | 8 | 7.8 | — | — | 4.9 | — | — | — | 7.2 | — | — | 23 | 🏠 Auxerre 1.48 xGA/m | |
| 27 | Mamadou Sangaré Lens | ⚖️ | 65 | — | 3.3 | 53 | 4 | 2.8 | 37 | — | 29 | 7.7 | — | — | 24 | 🏠 Nice 1.82 xGA/m |
| 28 | Vedat Muriqi Mallorca | ⚽ | 65 | 22 | 19.7 | 103 | 1 | 1.8 | 18 | — | 14 | 7.2 | — | — | 32 | ✈️ Levante UD 1.79 xGA/m |
| 29 | Abdessamad Ezzalzouli Real Betis | ⚽ | 65 | 9 | 9.7 | 71 | 8 | 6.1 | 29 | — | — | 7.6 | — | — | 25 | ✈️ FC Barcelona 1.33 xGA/m |
| 30 | Nikola Krstovic Atalanta | ⚽ | 65 | 10 | 15.2 | 96 | 5 | 3.2 | 22 | — | 16 | — | — | — | 26 | 🏠 Bologna 1.36 xGA/m |
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How does the G-SCORE work?
The G-SCORE is a contextual performance index (0-99) calculated for each player. It combines statistical base (xG, xA, FotMob rating, regularity — 60%), next match context (opponent xGA, PPDA, home/away — 25%) and momentum (recent form, age curve — 15%). It's not an exact prediction but an indicator to identify in-form players facing favorable opponents.