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 GD8 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 FC Everton 53.2% 60.9% 2 goals
2 Mohamed Salah FC Liverpool Manchester United 38.8% 42.6% No goal
3 Alexander Isak FC Liverpool Manchester United 33.3% 33.1% No goal
4 Ollie Watkins Aston Villa Tottenham Hotspur 28.1% 34.8% No goal
5 Eberechi Eze FC Arsenal FC Fulham 26.5% 23.3% No goal
6 Taiwo Awoniyi Nottingham Forest FC Chelsea 26.2% 27.1% No goal
7 Randal Kolo Muani Tottenham Hotspur Aston Villa 25.4% 28.0% No goal
8 Hugo Ekitike FC Liverpool Manchester United 25.3% 24.7% No goal
9 Chris Wood Nottingham Forest FC Chelsea 24.6% 27.5% No goal
10 Cody Gakpo FC Liverpool Manchester United 24.6% 25.9% 1 goal
11 Richarlison Tottenham Hotspur Aston Villa 24.1% 21.9% No goal
12 Gabriel Martinelli FC Arsenal FC Fulham 24.0% 20.5% No goal
13 Jean Philippe Mateta Crystal Palace AFC Bournemouth 23.9% 26.4% 3 goals
14 Benjamin Sesko Manchester United FC Liverpool 23.9% 33.9% No goal
15 Phil Foden Manchester City FC Everton 23.6% 27.7% No goal
16 Raul Jimenez FC Fulham FC Arsenal 22.8% 24.6% No goal
17 Jarrod Bowen West Ham United FC Brentford 21.7% 21.3% No goal
18 Donyell Malen Aston Villa Tottenham Hotspur 21.6% 29.7% No goal
19 Mohammed Kudus Tottenham Hotspur Aston Villa 21.2% 21.5% No goal
20 Ashley Barnes FC Burnley Leeds United 20.4% 18.1% 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.