SFM Tracker

Weekly Top 20 Predictions by League

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Matchday 7: Premier League (Completed Games Only)
One round — 17 graded picks. Small samples swing hard. See the full season.
SFM Top 20 Predictions
17
Predictions
7
Scored
41.2%
Hit Rate
Naive Top 20 Predictions
15
Predictions
6
Scored
40.0%
Hit Rate
SFM outperforms Naive:
41.2% vs 40.0% (+1.2 pp)
Brier Score: Premier League
0.2305
SFM
0.2385
Naive
SFM 0.2305 < Naive 0.2385
Calibration: Premier League
Hit Rate Over Time: Premier League (SFM vs Naive)
Premier League 2026/27 GD07 Completed
This GD: 41.2% (7/17) Overall: 41.2% (7/17) 1 gamedays completed
History: GD01 GD02 GD03 GD04 GD05 GD06 GD07 GD08
Rank Player Team vs SFM Naive Result
1 Erling Haaland Manchester City Ipswich Town 69.3% 58.1% 2 goals
2 Alexander Isak Liverpool Brentford 47.3% 34.9% 1 goal
3 Joao Pedro 10 Chelsea Everton 38.3% 25.8% No goal
4 Thierno Barry Everton Chelsea 37.5% 22.9% No goal
5 Nicolas Jackson Aston Villa Newcastle United 34.4% 30.1% No goal
6 Kai Havertz Arsenal Nottingham Forest 33.2% 24.5% No goal
7 Brian Brobbey Sunderland AFC Bournemouth 33.0% 20.2% Did not play
8 Marcus Tavernier AFC Bournemouth Sunderland 32.8% 14.4% 1 goal
9 Rayan Cherki Manchester City Ipswich Town 31.8% 9.6% Did not play
10 Bukayo Saka Arsenal Nottingham Forest 31.4% 23.7% No goal
11 Bryan Mbeumo Manchester United Leeds United 30.7% 26.4% 1 goal
13 Antoine Semenyo Manchester City Ipswich Town 29.0% 26.9% No goal
14 Kevin Schade Brentford Liverpool 28.4% 18.0% No goal
16 Dominic Calvert Lewin Leeds United Manchester United 27.4% 23.1% 1 goal
17 Yoane Wissa Newcastle United Aston Villa 26.4% 24.5% No goal
19 Benjamin Sesko Manchester United Leeds United 25.4% 33.2% Did not play
20 Omar Marmoush Tottenham Hotspur Coventry City 25.4% 26.7% 1 goal
15 Jack Hinshelwood Brighton & Hove Albion Crystal Palace 25.0% 19.1% 1 goal
17 Bruno Fernandes Manchester United Leeds United 24.8% 22.3% No goal
19 Justin Kluivert AFC Bournemouth Sunderland 24.4% 16.9% 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.