SFM Tracker

Weekly Top 20 Predictions by League

Reset
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 GD7 Completed
This GD: 25.0% (5/20) 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 FC Brentford 47.2% 60.9% 1 goal
2 Ollie Watkins Aston Villa FC Burnley 36.0% 34.8% No goal
3 Mohamed Salah FC Liverpool FC Chelsea 31.4% 42.6% No goal
4 Benjamin Sesko Manchester United AFC Sunderland 29.8% 33.9% 1 goal
5 Donyell Malen Aston Villa FC Burnley 28.4% 29.7% 2 goals
6 Alexander Isak FC Liverpool FC Chelsea 27.2% 33.1% No goal
7 Joergen Strand Larsen Wolverhampton Wanderers Brighton & Hove Albion 24.6% 22.7% No goal
8 Beto FC Everton Crystal Palace 22.7% 23.2% No goal
9 Dominic Calvert Lewin Leeds United Tottenham Hotspur 21.5% 21.4% No goal
10 Niclas Fuellkrug West Ham United FC Arsenal 21.4% 30.2% No goal
11 Harvey Barnes Newcastle United Nottingham Forest 20.8% 21.1% No goal
12 Bruno Fernandes Manchester United AFC Sunderland 20.5% 22.0% No goal
13 Morgan Rogers Aston Villa FC Burnley 20.3% 23.9% No goal
14 Chris Wood Nottingham Forest Newcastle United 20.1% 27.5% No goal
15 Bryan Mbeumo Manchester United AFC Sunderland 19.8% 18.9% No goal
16 Noah Okafor Leeds United Tottenham Hotspur 19.7% 21.3% 1 goal
17 Hugo Ekitike FC Liverpool FC Chelsea 19.6% 24.7% No goal
18 Jean Philippe Mateta Crystal Palace FC Everton 19.6% 26.4% No goal
19 Bukayo Saka FC Arsenal West Ham United 19.5% 23.6% 1 goal
20 Phil Foden Manchester City FC Brentford 19.5% 27.7% 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.