SFM Tracker

Weekly Top 20 Predictions by League

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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 GD32 Upcoming
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 FC Chelsea 50.1% 60.9% Pending
2 Mohamed Salah FC Liverpool FC Fulham 40.8% 42.6% Pending
3 Benjamin Sesko Manchester United Leeds United 37.2% 33.9% Pending
4 Ollie Watkins Aston Villa Nottingham Forest 35.4% 34.8% Pending
5 Alexander Isak FC Liverpool FC Fulham 35.2% 33.1% Pending
6 Callum Wilson West Ham United Wolverhampton Wanderers 33.7% 31.5% Pending
7 Gabriel Jesus FC Arsenal AFC Bournemouth 30.9% 28.7% Pending
8 Kai Havertz FC Arsenal AFC Bournemouth 30.8% 24.7% Pending
9 Bukayo Saka FC Arsenal AFC Bournemouth 30.8% 23.6% Pending
10 Eberechi Eze FC Arsenal AFC Bournemouth 29.9% 23.3% Pending
11 Joshua Zirkzee Manchester United Leeds United 27.8% 22.4% Pending
12 Cole Palmer FC Chelsea Manchester City 27.5% 33.8% Pending
13 Gabriel Martinelli FC Arsenal AFC Bournemouth 27.2% 20.5% Pending
14 Leandro Trossard FC Arsenal AFC Bournemouth 27.1% 20.3% Pending
15 Joergen Strand Larsen Crystal Palace Newcastle United 27.0% 22.7% Pending
16 Hugo Ekitike FC Liverpool FC Fulham 26.9% 24.7% Pending
17 Bruno Fernandes Manchester United Leeds United 26.5% 22.0% Pending
18 Cody Gakpo FC Liverpool FC Fulham 26.2% 25.9% Pending
19 Jarrod Bowen West Ham United Wolverhampton Wanderers 25.6% 21.3% Pending
20 Bryan Mbeumo Manchester United Leeds United 25.5% 18.9% Pending
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.