SFM Tracker

Weekly Top 20 Predictions by League

Overall Performance: Premier League (Completed Games Only)
SFM Top 20 Predictions
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Naive Top 20 Predictions
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No results yet. Predictions are for upcoming matches. Once games are played and results are updated, performance stats will appear here.
Premier League 2026/27 GD01 Upcoming
0 gamedays completed
Rank Player Team vs SFM Naive Result
1 Kai Havertz Arsenal Coventry City 37.4% 24.1% Pending
2 Erling Haaland Manchester City AFC Bournemouth 34.3% 58.3% Pending
3 Eberechi Eze Arsenal Coventry City 32.2% 21.2% Pending
4 Gabriel Jesus Arsenal Coventry City 31.2% 27.8% Pending
5 Benjamin Sesko Manchester United Hull City 29.8% 33.2% Pending
6 Bukayo Saka Arsenal Coventry City 29.5% 22.4% Pending
7 Viktor Gyoekeres Arsenal Coventry City 29.1% 33.0% Pending
8 Arnaud Kalimuendo Muinga Nottingham Forest Leeds United 28.3% 27.7% Pending
9 Antoine Semenyo Manchester City AFC Bournemouth 27.6% 25.8% Pending
10 Omar Marmoush Manchester City AFC Bournemouth 27.1% 26.3% Pending
11 Gabriel Martinelli Arsenal Coventry City 27.0% 18.7% Pending
12 Alexander Isak Liverpool Newcastle United 26.9% 34.4% Pending
13 Igor Thiago Brentford Tottenham Hotspur 25.1% 44.0% Pending
14 Chris Wood Nottingham Forest Leeds United 25.0% 28.8% Pending
15 Yoane Wissa Newcastle United Liverpool 25.0% 25.1% Pending
16 Taiwo Awoniyi Nottingham Forest Leeds United 25.0% 24.5% Pending
17 Ollie Watkins Aston Villa Brighton & Hove Albion 24.9% 33.9% Pending
18 Nonso Madueke Arsenal Coventry City 24.7% 13.7% Pending
19 Fabio Vieira Arsenal Coventry City 24.5% 22.1% Pending
20 Bryan Mbeumo Manchester United Hull City 24.1% 26.8% Pending
SFM Tracker
Top 20 Selection

For each league and gameday, we select the 20 players with the highest median probability of scoring at least one goal as predicted by the SFM.

Frozen Predictions

Predictions are locked before matches are played. This ensures transparent, verifiable performance tracking.

Fair Comparison

We compare SFM's top 20 picks against Naive's own top 20 picks (ranked by historical average). This is apples-to-apples.

Brier Score

Evaluation metric for probabilistic predictions. Measures both calibration and discrimination. Lower is better.