SFM Tracker

Weekly Top 20 Predictions by League

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Overall Performance: Premier League (Completed Games Only)
SFM Top 20 Predictions
520
Predictions
104
Scored
20.0%
Hit Rate
Naive Top 20 Predictions
520
Predictions
99
Scored
19.0%
Hit Rate
SFM outperforms Naive:
20.0% vs 19.0% (+1.0 pp)
Brier Score: Premier League
0.1574
SFM
0.1564
Naive
Naive 0.1564 < SFM 0.1574
Calibration: Premier League
Hit Rate Over Time: Premier League (SFM vs Naive)
Premier League 2025/26 GD10 Completed
This GD: 15.0% (3/20) Overall: 20.0% (104/520) 26 gamedays completed
History: GD1 GD2 GD3 GD4 GD5 GD6 GD7 GD8 GD9 GD10 GD11 GD12 GD13 GD14 GD15 GD16 GD17 GD18 GD19 GD20 GD21 GD22 GD23 GD24 GD25 GD26 GD27
Rank Player Team vs SFM Naive Result
1 Erling Haaland Manchester City AFC Bournemouth 52.2% 60.9% 2 goals
2 Mohamed Salah FC Liverpool Aston Villa 37.8% 42.6% 1 goal
3 Randal Kolo Muani Tottenham Hotspur FC Chelsea 30.3% 28.0% No goal
4 Ollie Watkins Aston Villa FC Liverpool 29.1% 34.8% No goal
5 Callum Wilson West Ham United Newcastle United 28.1% 31.5% No goal
6 Benjamin Sesko Manchester United Nottingham Forest 26.7% 33.9% No goal
7 Rodrigo Muniz FC Fulham Wolverhampton Wanderers 25.5% 30.2% No goal
8 Taiwo Awoniyi Nottingham Forest Manchester United 25.5% 27.1% No goal
9 Raul Jimenez FC Fulham Wolverhampton Wanderers 25.3% 24.6% No goal
10 Hugo Ekitike FC Liverpool Aston Villa 24.5% 24.7% No goal
11 Jean Philippe Mateta Crystal Palace FC Brentford 24.3% 26.4% 1 goal
12 Cody Gakpo FC Liverpool Aston Villa 23.9% 25.9% No goal
13 Richarlison Tottenham Hotspur FC Chelsea 23.9% 21.9% No goal
14 Phil Foden Manchester City AFC Bournemouth 22.8% 27.7% No goal
15 Donyell Malen Aston Villa FC Liverpool 22.5% 29.7% No goal
16 Jarrod Bowen West Ham United Newcastle United 21.0% 21.3% No goal
17 Mohammed Kudus Tottenham Hotspur FC Chelsea 21.0% 21.5% No goal
18 Omar Marmoush Manchester City AFC Bournemouth 20.7% 20.5% No goal
19 Bukayo Saka FC Arsenal FC Burnley 20.6% 23.6% No goal
20 Eberechi Eze FC Arsenal FC Burnley 19.9% 23.3% 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.