The Model Scorecard — Whole-Season ML Prediction Accuracy

Permanent, self-updating scorecard for the BeyondThePrem ML model. Whenever you want to know how often the model’s top call has actually landed this season, this is the URL. Updates automatically as each round’s results flow in.

IMPORTANT — NOT AN EV PAGE

Everything below tracks the ML model’s top-call accuracy — how often the outcome the model rates as most likely actually happens. That is different from EV selections, which are picks where the model’s probability beats the market’s implied probability.

For EV-specific accountability (stake on / return on / ROI), see our model accountability posts.




Season-to-Date — All Leagues Combined

Running Scorecard

CHAMPIONSHIP

67/321 (21%)

LEAGUE ONE

73/300 (24%)

WSL

Each cell: correct / total predictions (hit rate). Random baseline for a 3-way market is 33%. A trained model should clear that over a sufficient sample — and we are honest when it does not.

All-League Stats Dashboard

Combined Stats

Prediction Stats (2025)

Championship

Predictions107
Played107
Points67 / 321
Correct scores11 (10.3%)
Correct results45 (42.1%)

League One

Predictions100
Played100
Points73 / 300
Correct scores12 (12%)
Correct results49 (49%)

Championship — Model Scorecard

MODEL VERSION

goals_logreg_v1_cal — Platt-calibrated Logistic Regression

Trained on 3,266 Championship matches across six seasons (2019/20–2024/25). Log loss 1.036 vs baseline 1.064.

Season-to-Date Stats

Prediction Stats (2025)

Championship

Predictions107
Played107
Points67 / 321
Correct scores11 (10.3%)
Correct results45 (42.1%)

Accuracy by Matchday

Points by Matchday (2025)

Accuracy by Predicted Outcome

Prediction Breakdown (2025)

By Team

Avg Points by Team (2025)

Common Score Patterns

Most-Predicted Scorelines (2025)

League One — Model Scorecard

MODEL VERSION

leagueone_goals_v1 — Random Forest

Trained on League One historical data. Picks draws as its top call more often than the Championship model, reflecting how draw-heavy League One actually is.

Season-to-Date Stats

Prediction Stats (2025)

League One

Predictions100
Played100
Points73 / 300
Correct scores12 (12%)
Correct results49 (49%)

Accuracy by Matchday

Points by Matchday (2025)

Accuracy by Predicted Outcome

Prediction Breakdown (2025)

By Team

Avg Points by Team (2025)

Common Score Patterns

Most-Predicted Scorelines (2025)

WSL — Model Scorecard

MODEL VERSION

wsl_goals_v1 — Random Forest (exploratory)

The WSL model is our most recent addition and still in a genuinely exploratory phase. Treat the numbers below as early data, not established performance.

Season-to-Date Stats

No prediction data found.

Accuracy by Matchday

No matchday prediction data found.

Accuracy by Predicted Outcome

No prediction breakdown data found.

The honest framing

A probability model’s output is not a forecast you can bet on like a weather app. It is an estimate of how likely each outcome is, given the features the model has been trained on. We track these numbers in public because:

  • Accountability keeps us honest — we publish the bad rounds as loudly as the good ones
  • Small samples can lie — the numbers above only get statistically meaningful as the season accumulates
  • The right metric depends on the question — hit rate is easy to read, log loss is more rigorous, and neither answers “is this a good bet”

For the “is it a good bet” question, see our EV-filtered Heinz and Yankee posts and the follow-up accountability write-ups.

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