Edge (Betting Edge)
Definition
Betting edge is the percentage difference between your estimated probability of an outcome and the probability implied by the bookmaker's odds. A positive edge means you have identified a bet where the true probability is higher than what the bookmaker is pricing. Edge is the foundation of every profitable bet.
Example
Our model estimates Fiorentina wins at 58%. The bookmaker prices Fiorentina at 2.00 (implied 50%). Edge = 58% − 50% = +8%.
This +8% edge means for every £100 wagered on comparable bets over a large sample, you expect to make approximately £8 in profit — assuming the model's probability estimates are accurate.
How CalibrSports Predicts This
Edge is the primary filter in our bet selection pipeline. Every market across every fixture is scored for edge after our model generates probabilities and we retrieve bookmaker odds. Only bets that meet per-market and per-league minimum edge thresholds proceed to the AI advisor review stage. We publish the edge figure alongside every pick for full transparency.
Key Facts
Formula
Edge = Model Prob − Implied Prob
Minimum edge (1X2)
~5–10%
Minimum edge (O/U)
3–5%
Published
Yes — shown per pick
Related Terms
Frequently Asked Questions
Is a 1% edge worth betting on?
Theoretically yes, but practically no. A 1% edge is smaller than model uncertainty and barely exceeds the bookmaker's overround. We set minimum edge thresholds of 3–8% depending on market to ensure only robust opportunities are flagged.
Can the same bet have different edges at different bookmakers?
Yes, and this is why line shopping matters. If one bookmaker offers 2.10 and another offers 1.85 for the same outcome, the edge at 2.10 can be significantly higher. We always reference the best available odds.
How do I know if an edge is real or just model error?
You cannot know with certainty on any single bet. Over 200+ bets, if the edge is real, the win rate and ROI should converge toward the expected values. This is why we publish our full track record — so you can verify the edge empirically.