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 GD4 Completed
This GD: 20.0% (4/20) 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 Manchester United 52.1% 60.9% 2 goals
2 Mohamed Salah FC Liverpool FC Burnley 36.5% 42.6% 1 goal
3 Callum Wilson West Ham United Tottenham Hotspur 28.6% 31.5% No goal
4 Ollie Watkins Aston Villa FC Everton 27.7% 34.8% No goal
5 Benjamin Sesko Manchester United Manchester City 24.4% 33.9% No goal
6 Beto FC Everton Aston Villa 23.9% 23.2% No goal
7 Jean Philippe Mateta Crystal Palace AFC Sunderland 23.7% 26.4% No goal
8 Rodrigo Muniz FC Fulham Leeds United 23.6% 30.2% No goal
9 Richarlison Tottenham Hotspur West Ham United 23.6% 21.9% No goal
10 Hugo Ekitike FC Liverpool FC Burnley 23.5% 24.7% No goal
11 Raul Jimenez FC Fulham Leeds United 23.5% 24.6% No goal
12 Cody Gakpo FC Liverpool FC Burnley 22.9% 25.9% No goal
13 Cole Palmer FC Chelsea FC Brentford 22.8% 33.8% 1 goal
14 Phil Foden Manchester City Manchester United 22.7% 27.7% 1 goal
15 Jarrod Bowen West Ham United Tottenham Hotspur 21.4% 21.3% No goal
16 Donyell Malen Aston Villa FC Everton 21.2% 29.7% No goal
17 Arnaud Kalimuendo Muinga Nottingham Forest FC Arsenal 20.8% 26.6% No goal
18 Gabriel Martinelli FC Arsenal Nottingham Forest 20.7% 20.5% No goal
19 Harvey Barnes Newcastle United Wolverhampton Wanderers 20.7% 21.1% No goal
20 Chris Wood Nottingham Forest FC Arsenal 19.7% 27.5% 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.