⚡ G-SCORE — Scouting Dashboard
760 players analyzed · 5 leagues · Contextual score updated daily
760 players
⚡ G-SCORE — Contextual index: 60% base (xG, rating, regularity) · 25% context (opponent, home/away) · 15% momentum (form, age)
| # | Player | Profil | G-SCORE ↓ | G | xG | Sh | Ass | xA | KP | 🛡️ + | 🔄 + | Rtg | T10 | R10 | Age | Next |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 |  Erling Haaland Manchester City | ⚽ | 80 | 22 | 23.1 | 102 | 7 | 4.8 | 21 | 17 | 13 | 7.6 | — | — | 24 | 🏠 Liverpool 1.34 xGA/m |
| 2 |  Kylian Mbappé Real Madrid | ⚖️ | 80 | 23 | 20.8 | — | — | 5.6 | — | — | 63 | 8.2 | — | — | 25 | ✈️ Mallorca 1.8 xGA/m |
| 3 |  Bruno Fernandes Manchester United | ⚖️ | 79 | 8 | 11.2 | 73 | 16 | 13 | 102 | 61 | 15 | 8 | — | — | 32 | 🏠 Leeds United 1.47 xGA/m |
| 4 |  Lamine Yamal Barcelona | 🏃 | 78 | 14 | 11.8 | 99 | 9 | 11.1 | 64 | 44 | 118 | 8.3 | — | — | 17 | ✈️ Atlético Madrid 1.24 xGA/m |
| 5 |  Kenan Yildiz Juventus | ⚖️ | 74 | 10 | 8.2 | 87 | 6 | 9.4 | 67 | 23 | 69 | 7.6 | — | — | 21 | 🏠 Genoa 1.45 xGA/m |
| 6 |  Deniz Undav VfB Stuttgart | ⚽ | 74 | 18 | 16.3 | 100 | 5 | 3.7 | 25 | 18 | 6 | 7.5 | — | — | 30 | 🏠 Dortmund 1.28 xGA/m |
| 7 |  Igor Thiago Brentford | 🛡️ | 73 | 19 | 17.5 | — | — | — | — | 38 | 26 | 7.2 | — | — | 25 | 🏠 Everton 1.52 xGA/m |
| 8 |  Vinícius Júnior Real Madrid | ⚖️ | 72 | 11 | 12.2 | 80 | 5 | 5.3 | 50 | 28 | 70 | 7.7 | — | — | 26 | ✈️ Mallorca 1.8 xGA/m |
| 9 | Yan Diomande RB Leipzig | 🏃 | 72 | 10 | 6.4 | 42 | 6 | 7.3 | 44 | 36 | 89 | 7.6 | — | — | 18 | ✈️ Bremen 1.69 xGA/m |
| 10 |  Joaquín Panichelli Strasbourg | ⚖️ | 71 | 16 | 14.7 | 58 | 1 | 3 | 21 | 38 | 11 | 7.3 | — | — | 24 | ✈️ Mainz 1.83 xGA/m |
| 11 |  Bradley Barcola Paris Saint-Germain | ⚖️ | 70 | 10 | 9.8 | 55 | 1 | 4.1 | 32 | 35 | 29 | 7.5 | — | — | 24 | 🏠 Toulouse 1.23 xGA/m |
| 12 |  Nico Paz Como | ⚖️ | 70 | 10 | 8 | 103 | 6 | 5.4 | 43 | 94 | 55 | 7.7 | — | — | 20 | ✈️ Udinese 1.56 xGA/m |
| 13 |  Christoph Baumgartner RB Leipzig | ⚖️ | 70 | 12 | 13.9 | 62 | 6 | 2.8 | 30 | 56 | — | 7.3 | — | — | 27 | ✈️ Bremen 1.69 xGA/m |
| 14 |  Antoine Semenyo Manchester City | ⚽ | 69 | 15 | 11.2 | 68 | 4 | 3.1 | 32 | — | 38 | 7.4 | — | — | 26 | 🏠 Liverpool 1.34 xGA/m |
| 15 |  Mikel Oyarzabal Real Sociedad | ⚽ | 69 | 12 | 13.6 | 68 | 3 | 5.2 | 34 | 23 | 26 | 7.3 | — | — | 29 | 🏠 Levante UD 1.89 xGA/m |
| 16 |  Andrej Kramaric TSG 1899 Hoffenheim | ⚽ | 69 | 10 | 9.5 | 52 | 5 | 4.1 | 31 | — | 16 | 7.2 | — | — | 35 | 🏠 Mainz 1.83 xGA/m |
| 17 |  João Pedro Chelsea | ⚖️ | 68 | 14 | 14.2 | 63 | 5 | 4 | 28 | 30 | 26 | 7.2 | — | — | 25 | 🏠 Port Vale 1.2 xGA/m |
| 18 |  Raphinha Barcelona | ⚽ | 68 | 11 | 10 | 58 | 3 | 5.8 | 38 | — | 21 | 7.8 | — | — | 27 | ✈️ Atlético Madrid 1.24 xGA/m |
| 19 |  Ousmane Dembélé Paris Saint-Germain | ⚖️ | 67 | 8 | 4.9 | 33 | 5 | 3.9 | 33 | — | 17 | 7.4 | — | — | 27 | 🏠 Toulouse 1.23 xGA/m |
| 20 |  Arda Güler Real Madrid | ⚖️ | 67 | — | 4.7 | 46 | 8 | 8.1 | 63 | 51 | 25 | 7.5 | — | — | 21 | ✈️ Mallorca 1.8 xGA/m |
| 21 |  Fermín López Barcelona | ⚖️ | 67 | 5 | 5.3 | 49 | 8 | 6.8 | 29 | 38 | 33 | 7.4 | — | — | 23 | ✈️ Atlético Madrid 1.24 xGA/m |
| 22 |  Benjamin Sesko Manchester United | ⚽ | 66 | 9 | 10 | 53 | 1 | 0.5 | 8 | — | 6 | — | — | — | 21 | 🏠 Leeds United 1.47 xGA/m |
| 23 |  Désiré Doué Paris Saint-Germain | ⚖️ | 66 | 5 | 7.3 | 40 | 2 | 3 | 22 | 31 | 22 | 7.3 | — | — | 19 | 🏠 Toulouse 1.23 xGA/m |
| 24 |  Antony Real Betis | ⚖️ | 66 | 7 | 6.6 | 65 | 5 | 5.4 | 45 | — | 17 | 7.5 | — | — | — | 🏠 Espanyol 1.62 xGA/m |
| 25 |  Michael Olise Bayern München | ⚖️ | 66 | 11 | 9.8 | 84 | 17 | 15.4 | 71 | 36 | 51 | 8.2 | — | — | 22 | — |
| 26 |  Bryan Mbeumo Manchester United | ⚖️ | 65 | 9 | 9.7 | 60 | 3 | 4.3 | 38 | 30 | 14 | 7.2 | — | — | 27 | 🏠 Leeds United 1.47 xGA/m |
| 27 |  Nikola Krstovic Atalanta | ⚽ | 65 | 8 | 11.8 | 74 | 4 | 2.5 | 20 | — | 14 | — | — | — | 26 | ✈️ Lecce 1.67 xGA/m |
| 28 |  Fisnik Asllani TSG 1899 Hoffenheim | ⚖️ | 65 | 8 | 7.6 | 57 | 5 | 4.3 | 27 | 19 | 12 | — | — | — | 24 | 🏠 Mainz 1.83 xGA/m |
| 29 |  Abdessamad Ezzalzouli Real Betis | ⚖️ | 64 | 5 | 7.5 | 56 | 5 | 4.8 | 20 | 46 | — | 7.4 | — | — | 25 | 🏠 Espanyol 1.62 xGA/m |
| 30 |  Harry Kane Bayern München | ⚽ | 64 | 31 | 26.7 | 108 | 5 | 6.5 | 36 | 24 | 29 | 8.3 | — | — | 31 | — |
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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.