Congestion & Travel: The Hidden Edges Most Models Miss
Rest days, rotation and miles traveled quietly change outcomes. We price that in.
Read more →Derbies & Psychology: When Data Meets Narrative
Intense fixtures inject variance. Here’s how we adapt probabilities and staking.
Read more →Exact Score Predictions: Why ~10% Hit Rate Is Strong
Exact scores are brutally difficult. Doubling the random baseline is meaningful edge—if you use it correctly.
Read more →How to Read WinPred Cards: Confidence, Not Certainty
Our UI shows agreement, confidence, and market-aware insights—here’s how to use it.
Read more →Under the Hood: How We Build Match Probabilities
From squads and styles to simulation and calibration—our probability pipeline.
Read more →League Breakdown: Where Our Model Shines (and Why)
Why we’re strong in Portugal, Spain and Brazil—and what makes the Premier League harder.
Read more →Learning in the Wild: How Our Insight Engine Improves Over Time
We track monthly accuracy and a simple ‘drop-the-oldest’ learning curve to show how our signals sharpen as the season unfolds.
Read more →Roadmap 2025/26: Accuracy Up, Variance Down
Better availability modeling, league‑specific calibration and clearer explanations.
Read more →Turning xG into Edges on Totals
xG is a great start—but you need variance, set pieces and game state to size up an Over/Under.
Read more →Champions League Knockouts: What Changes and Why It Matters
Two‑leg ties, no away‑goals rule, and extra time reshape probabilities.
Read more →Why 51% Accuracy on 1X2 Actually Matters
51% sounds modest until you compare it to random guesswork and the naive home‑win baseline.
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