SFM Tracker

Weekly Top 20 Predictions by League

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Matchday 1: Premier League (Completed Games Only)
One round — 13 graded picks. Small samples swing hard. See the full season.
SFM Top 20 Predictions
13
Predictions
2
Scored
15.4%
Hit Rate
Naive Top 20 Predictions
13
Predictions
2
Scored
15.4%
Hit Rate
SFM and Naive tied at 15.4%
Brier Score: Premier League
0.1335
SFM
0.1752
Naive
SFM 0.1335 < Naive 0.1752
Calibration: Premier League
Hit Rate Over Time: Premier League (SFM vs Naive)
Premier League 2026/27 GD01 Completed
This GD: 15.4% (2/13) Overall: 15.4% (2/13) 1 gamedays completed
History: GD01 GD02 GD03 GD04 GD05
Rank Player Team vs SFM Naive Result
1 Kai Havertz Arsenal Coventry City 37.4% 24.1% 1 goal
2 Erling Haaland Manchester City AFC Bournemouth 34.3% 58.3% No goal
3 Eberechi Eze Arsenal Coventry City 32.2% 21.2% No goal
4 Gabriel Jesus Arsenal Coventry City 31.2% 27.8% Did not play
5 Benjamin Sesko Manchester United Hull City 29.8% 33.2% No goal
6 Bukayo Saka Arsenal Coventry City 29.5% 22.4% 1 goal
7 Viktor Gyoekeres Arsenal Coventry City 29.1% 33.0% Did not play
8 Arnaud Kalimuendo Muinga Nottingham Forest Leeds United 28.3% 27.7% No goal
9 Antoine Semenyo Manchester City AFC Bournemouth 27.6% 25.8% No goal
10 Omar Marmoush Manchester City AFC Bournemouth 27.1% 26.3% Did not play
11 Gabriel Martinelli Arsenal Coventry City 27.0% 18.7% Did not play
12 Alexander Isak Liverpool Newcastle United 26.9% 34.4% No goal
13 Igor Thiago Brentford Tottenham Hotspur 25.1% 44.0% No goal
14 Chris Wood Nottingham Forest Leeds United 25.0% 28.8% No goal
15 Yoane Wissa Newcastle United Liverpool 25.0% 25.1% No goal
16 Taiwo Awoniyi Nottingham Forest Leeds United 25.0% 24.5% No result
17 Ollie Watkins Aston Villa Brighton & Hove Albion 24.9% 33.9% Did not play
18 Nonso Madueke Arsenal Coventry City 24.7% 13.7% No goal
19 Fabio Vieira Arsenal Coventry City 24.5% 22.1% Did not play
20 Bryan Mbeumo Manchester United Hull City 24.1% 26.8% No goal
20 Emanuel Emegha Chelsea Fulham 23.9% 32.3% Did not play
Understanding Hit Rate vs Brier Score

You might notice that hit rate and Brier Score can tell different stories.

Hit Rate

Simply counts: "How many of my top 20 picks scored?"

A naive model that always picks proven strikers (Haaland, Kane) will have a high hit rate because these players score often, regardless of the match context.

Brier Score

Asks: "How accurate were the probability estimates?"

If SFM says "32% chance" and Naive says "38% chance" for the same player who doesn't score, SFM gets a better Brier Score because its estimate was closer to reality.

Bottom line: Hit rate measures selection quality (who you pick), while Brier Score measures probability quality (how well-calibrated your predictions are). A model can pick slightly fewer scorers but still be more valuable if its probabilities are more trustworthy for betting or decision-making.
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.