SFM Tracker

Weekly Top 20 Predictions by League

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Overall Performance: Premier League (Completed Games Only)
SFM Top 20 Predictions
760
Predictions
159
Scored
20.9%
Hit Rate
Naive Top 20 Predictions
760
Predictions
152
Scored
20.0%
Hit Rate
SFM outperforms Naive:
20.9% vs 20.0% (+0.9 pp)
Brier Score: Premier League
0.1624
SFM
0.1609
Naive
Naive 0.1609 < SFM 0.1624
Calibration: Premier League
Hit Rate Over Time: Premier League (SFM vs Naive)
Premier League 2025/26 GD37 Completed
This GD: 20.0% (4/20) Overall: 20.9% (159/760) 38 gamedays completed
History: GD01 GD02 GD03 GD04 GD05 GD06 GD07 GD08 GD09 GD10 GD11 GD12 GD13 GD14 GD15 GD16 GD17 GD18 GD19 GD20 GD21 GD22 GD23 GD24 GD25 GD26 GD27 GD28 GD29 GD30 GD31 GD32 GD33 GD34 GD35 GD36 GD37 GD38
Rank Player Team vs SFM Naive Result
1 Erling Haaland Manchester City AFC Bournemouth 51.1% 60.9% 1 goal
2 Ollie Watkins Aston Villa FC Liverpool 37.1% 34.8% 2 goals
3 Kai Havertz FC Arsenal FC Burnley 37.1% 24.7% 1 goal
4 Bukayo Saka FC Arsenal FC Burnley 37.0% 23.6% No goal
5 Eberechi Eze FC Arsenal FC Burnley 36.1% 23.3% No goal
6 Gabriel Martinelli FC Arsenal FC Burnley 33.1% 20.5% No goal
7 Leandro Trossard FC Arsenal FC Burnley 33.0% 20.3% No goal
8 Mohamed Salah FC Liverpool Aston Villa 32.2% 42.6% No goal
9 Cole Palmer FC Chelsea Tottenham Hotspur 31.7% 33.8% No goal
10 Martin Oedegaard FC Arsenal FC Burnley 29.4% 18.9% No goal
11 Martin Zubimendi FC Arsenal FC Burnley 28.4% -- No goal
12 Yoane Wissa Newcastle United West Ham United 28.3% 22.9% No goal
13 Viktor Gyoekeres FC Arsenal FC Burnley 28.1% -- No goal
14 Declan Rice FC Arsenal FC Burnley 28.0% -- No goal
15 Joshua Zirkzee Manchester United Nottingham Forest 27.5% 22.4% No goal
16 Bruno Fernandes Manchester United Nottingham Forest 26.2% 22.0% No goal
17 Bryan Mbeumo Manchester United Nottingham Forest 25.3% 18.9% 1 goal
18 Rodrigo Muniz FC Fulham Wolverhampton Wanderers 25.1% 30.2% No goal
19 Raul Jimenez FC Fulham Wolverhampton Wanderers 25.0% 24.6% No goal
20 Beto FC Everton AFC Sunderland 24.9% 23.2% No goal
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.