Belgium
Who's most likely to win the Jupiler Pro LeagueGolden Boot, from 1,000 Monte Carlo simulations. Each player's current goals-per-90 rate is projected forward over their team's remaining fixtures, with minutes-per-appearance scaling the expected workload to match their actual usage pattern.
See the wider season picture in our Jupiler Pro League final standings probabilities.
| Player | NT | G | xG | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|---|---|---|
 N. Tresoldi | 🇩🇪 | 15 | 15.8 | 61.9 | 36.4 | 1.7 | ||
 C. Tzolis | 🇬🇷 | 15 | 15.9 | 36.7 | 61.3 | 2.0 | ||
 J. Erenbjerg | 🇩🇰 | 13 | 13.0 | 35.9 | 52.4 | 11.7 | ||
 T. Hazard | 🇧🇪 | 13 | 13.8 | 1.1 | 1.9 | 50.7 | 37.6 | 8.3 |
 W. Kanga | 🇨🇮 | 12 | 12.8 | 8.5 | 8.3 | 72.6 | ||
 Nacho Ferri | 🇪🇸 | 11 | 11.3 | 1.8 | ||||
 P. Fall | 🇸🇳 | 11 | 11.0 | |||||
 K. Goto | 🇯🇵 | 11 | 11.6 | 0.9 | 1.3 | 5.2 | ||
 Joseph Okopu | 🇬🇭 | 10 | 10.0 | |||||
 D. Heymans | 🇧🇪 | 10 | 10.4 |
