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 GD31 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 Ollie Watkins Aston Villa West Ham United 40.1% 34.8% 1 goal
2 Randal Kolo Muani Tottenham Hotspur Nottingham Forest 29.4% 28.0% No goal
3 Rodrigo Muniz FC Fulham FC Burnley 27.7% 30.2% No goal
4 Raul Jimenez FC Fulham FC Burnley 27.7% 24.6% 1 goal
5 Gabriel Jesus FC Arsenal Wolverhampton Wanderers 27.1% 28.7% No goal
6 Bukayo Saka FC Arsenal Wolverhampton Wanderers 27.1% 23.6% 1 goal
7 Benjamin Sesko Manchester United AFC Bournemouth 26.7% 33.9% No goal
8 Jadon Sancho Aston Villa West Ham United 26.6% 25.8% No goal
9 Eberechi Eze FC Arsenal Wolverhampton Wanderers 26.3% 23.3% No goal
10 Tammy Abraham Aston Villa West Ham United 25.9% 25.7% No goal
11 Yoane Wissa Newcastle United AFC Sunderland 25.3% 22.9% No goal
12 Gabriel Martinelli FC Arsenal Wolverhampton Wanderers 23.8% 20.5% No goal
13 Leandro Trossard FC Arsenal Wolverhampton Wanderers 23.7% 20.3% No goal
14 Morgan Rogers Aston Villa West Ham United 23.2% 23.9% No goal
15 Richarlison Tottenham Hotspur Nottingham Forest 23.2% 21.9% No goal
16 Cole Palmer FC Chelsea FC Everton 23.1% 33.8% No goal
17 Beto FC Everton FC Chelsea 22.4% 23.2% 2 goals
18 Leon Bailey Aston Villa West Ham United 21.7% 19.2% No goal
19 Taiwo Awoniyi Nottingham Forest Tottenham Hotspur 21.3% 27.1% 1 goal
20 Nonso Madueke FC Arsenal Wolverhampton Wanderers 21.2% 12.5% 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.