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 GD28 Completed
This GD: 20.0% (4/20) 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 Mohamed Salah FC Liverpool West Ham United 40.7% 42.6% No goal
2 Ollie Watkins Aston Villa Wolverhampton Wanderers 36.1% 34.8% No goal
3 Benjamin Sesko Manchester United Crystal Palace 32.0% 33.9% 1 goal
4 Hugo Ekitike FC Liverpool West Ham United 26.8% 24.7% 1 goal
5 Cody Gakpo FC Liverpool West Ham United 26.1% 25.9% 1 goal
6 Kai Havertz FC Arsenal FC Chelsea 25.4% 24.7% No goal
7 Bukayo Saka FC Arsenal FC Chelsea 25.4% 23.6% No goal
8 Eberechi Eze FC Arsenal FC Chelsea 24.7% 23.3% No goal
9 Rodrigo Muniz FC Fulham Tottenham Hotspur 24.6% 30.2% No goal
10 Raul Jimenez FC Fulham Tottenham Hotspur 24.4% 24.6% No goal
11 Yoane Wissa Newcastle United FC Everton 23.9% 22.9% No goal
12 Jadon Sancho Aston Villa Wolverhampton Wanderers 23.5% 25.8% No goal
13 Joshua Zirkzee Manchester United Crystal Palace 23.4% 22.4% No goal
14 Tammy Abraham Aston Villa Wolverhampton Wanderers 22.8% 25.7% No goal
15 Gabriel Martinelli FC Arsenal FC Chelsea 22.3% 20.5% No goal
16 Bruno Fernandes Manchester United Crystal Palace 22.2% 22.0% 1 goal
17 Leandro Trossard FC Arsenal FC Chelsea 22.2% 20.3% No goal
18 Bryan Mbeumo Manchester United Crystal Palace 21.4% 18.9% No goal
19 Enes Uenal AFC Bournemouth AFC Sunderland 21.4% 22.6% No goal
20 Randal Kolo Muani Tottenham Hotspur FC Fulham 21.3% 28.0% 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.