FOOTGolden Boot › Premier League
2026-27
Premier League Golden Boot 2026-27: AI race predictions
Who wins the Premier League Golden Boot 2026-27? The official scorer table plus 10,000 AI season simulations: win probabilities, projected final tallies and the record chase — updated after every round.
10 scorers on the official board · probabilities from 10,000 season simulations · kick-off switches to your time zone
| # | Player | Team | Goals | Assists | Apps | Left | Proj. | Win % |
|---|---|---|---|---|---|---|---|---|
| 1 | Erling Haaland | Manchester City FC | 9 | 0 | 8 | 3 | 12.1 | 94% |
| 2 | Alexander Isak | Liverpool FC | 6 | 0 | 7 | 3 | 7.9 | 3% |
| 3 | Marcus Tavernier | AFC Bournemouth | 5 | 0 | 8 | 3 | 6.8 | 1% |
| 4 | João Pedro | Chelsea FC | 5 | 3 | 6 | 3 | 6.8 | 1% |
| 5 | Rayan Cherki | Manchester City FC | 4 | 2 | 8 | 3 | 5.8 | <1% |
| 6 | Bryan Mbeumo | Manchester United FC | 4 | 0 | 8 | 3 | 5.7 | <1% |
| 7 | Nicolas Jackson | Aston Villa FC | 4 | 0 | 7 | 3 | 5.6 | <1% |
| 8 | Morgan Rogers | Chelsea FC | 4 | 1 | 8 | 3 | 5.4 | <1% |
| 9 | Bukayo Saka | Arsenal FC | 3 (1P) | 0 | 7 | 3 | 4.9 | <1% |
| 10 | Thierno Barry | Everton FC | 3 | 0 | 8 | 3 | 4.2 | <1% |
All-time single-season record: 36 goals — Erling Haaland, 2022-23.
How the projections work
The Footalytics AI engine simulates the rest of the season 10,000 times. Machine-rated opponent strength weights every remaining fixture, Bayesian calibration keeps small samples honest, injuries and rotation are priced in as uncertainty — refreshed automatically after every round by the same engine that grades every match prediction in public.
Assumptions: scoring rates from this season's official scorer table, opponent strength from team ratings, an appearance probability per game, and no January transfers. Probabilities are model projections, not facts — the table itself is official data.