SFM Tracker

Weekly Top 20 Predictions by League

Reset
Overall Performance: Premier League (Completed Games Only)
SFM Top 20 Predictions
620
Predictions
122
Scored
19.7%
Hit Rate
Naive Top 20 Predictions
620
Predictions
118
Scored
19.0%
Hit Rate
SFM outperforms Naive:
19.7% vs 19.0% (+0.6 pp)
Brier Score: Premier League
0.1577
SFM
0.1587
Naive
SFM 0.1577 < Naive 0.1587
Calibration: Premier League
Hit Rate Over Time: Premier League (SFM vs Naive)
Premier League 2025/26 GD27 Completed
This GD: 25.0% (5/20) Overall: 19.7% (122/620) 31 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
Rank Player Team vs SFM Naive Result
1 Erling Haaland Manchester City Newcastle United 54.7% 60.9% No goal
2 Ollie Watkins Aston Villa Leeds United 38.4% 34.8% No goal
3 Mohamed Salah FC Liverpool Nottingham Forest 32.4% 42.6% No goal
4 Cole Palmer FC Chelsea FC Burnley 31.9% 33.8% No goal
5 Joergen Strand Larsen Crystal Palace Wolverhampton Wanderers 28.6% 22.7% No goal
6 Callum Wilson West Ham United AFC Bournemouth 25.8% 31.5% No goal
7 Jadon Sancho Aston Villa Leeds United 25.2% 25.8% No goal
8 Benjamin Sesko Manchester United FC Everton 24.7% 33.9% 1 goal
9 Phil Foden Manchester City Newcastle United 24.7% 27.7% No goal
10 Tammy Abraham Aston Villa Leeds United 24.6% 25.7% 1 goal
11 Randal Kolo Muani Tottenham Hotspur FC Arsenal 23.0% 28.0% 1 goal
12 Omar Marmoush Manchester City Newcastle United 22.5% 20.5% No goal
13 Bukayo Saka FC Arsenal Tottenham Hotspur 22.0% 23.6% No goal
14 Morgan Rogers Aston Villa Leeds United 22.0% 23.9% No goal
15 Eberechi Eze FC Arsenal Tottenham Hotspur 21.3% 23.3% 2 goals
16 Beto FC Everton Manchester United 21.1% 23.2% No goal
17 Joao Pedro 10 FC Chelsea FC Burnley 20.6% 17.5% 1 goal
18 Leon Bailey Aston Villa Leeds United 20.5% 19.2% No goal
19 Antoine Semenyo Manchester City Newcastle United 20.4% 14.6% No goal
20 Hugo Ekitike FC Liverpool Nottingham Forest 20.4% 24.7% 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.