How MaxEdge finds value

Bookmakers don’t publish true probabilities — they publish prices designed to make money. Every market carries a built-in margin (the “overround” or vig), typically 4–8%, so the implied probabilities of all outcomes add up to more than 100%.

Implied probability  =  1 / decimal odds

Home 2.00 -> 50.0%
Draw 3.50 -> 28.6%
Away 4.00 -> 25.0%
                ───────
        total = 103.6%   ← the 3.6% is the bookmaker's margin

To compare a price to fair value, that margin has to come out first.

Our primary model follows the Kaunitz et al. (2017) method — “beating the bookies with their own numbers.” The collective price of 15+ bookmakers is, on average, the sharpest probability estimate that exists. We turn it into a fair, margin-free number by de-vigging: average each outcome’s implied probability across books, then normalise so the three add up to exactly 100%.

Fair P(outcome)  =  mean(1 / odds)  ÷  Σ mean(1 / odds)

We then keep a small 2% relative safety margin and flag value
only when the BEST available price beats that fair price.

Two guards keep it honest: a single book pricing an outcome wildly out of line (a “palpable error”) is discarded before averaging, and the fair price is shaved by a relative margin so long shots aren’t over-flagged.

Alongside the consensus, an API-Football predictive model prices every match from fundamentals — expected goals, recent form and team strength — rather than from the market. It’s a deliberately different lens: when both the market-consensus and the predictive model point the same way, conviction is highest. Each signal is tagged with which model produced it, and the predictive model is trusted less in scoring (below) because it has no market discipline.

“Edge” on MaxEdge is Expected Value (EV) per unit staked — not a vague probability gap. It answers one question: for every £1 on this price, how much do we expect to win or lose long-term?

Edge (EV)   =  modelProb × decimal odds − 1
modelProb   =  (edge + 1) / decimal odds

Model gives 55% on a 2.10 price:
Edge = 0.55 × 2.10 − 1 = +0.155  →  +15.5% EV

A positive edge means the price is longer than it should be. This is the same figure everywhere on the site — the feed, the signals page and the detail page all show the identical number, because they all use the identical formula.

Raw edge alone is misleading — a 15% edge on a 1.8 favourite is worth far more than a 15% edge on a 12.0 long shot, because the long shot loses far more often. The Model Edge Score is a single risk-adjusted 0–100 rating built on the Kelly criterion (the mathematically optimal stake fraction):

kelly  =  min(edge, 15%) / (odds − 1)
MES    =  100 × trust × (1 − e^(−19 × kelly))

trust:  Consensus 1.0 · Dixon-Coles 0.9 · Predictive 0.55
        Corners / Cards 0.5

Kelly naturally punishes long odds and thin edges, and the per-model trust caps how high a less-reliable model can score — so the board can’t be dominated by speculative bets.

One card per match merges all five markets together; a market with no value stays collapsed, value opens automatically.

Win rates over small samples are mostly noise. The honest measure of whether an edge was real is Closing Line Value — did our signal price beat the market’s final price at kick-off? Consistently beating the closing line is the single best predictor of long-term profit, and we track it on every settled signal.

CLV  =  ln( signal odds / closing odds )

Signalled 2.10, closed 1.90  →  ln(2.10/1.90) = +10.0%  beat the close

Every signal is settled against the real result and disclosed on the Performance page — wins and losses.

No model is perfect. Team-strength and goals inputs update periodically and don’t yet capture late lineup changes, weather or motivation. The corners and cards models lean on patchy international stats, which is exactly why they’re anchored to the market. Treat every MaxEdge number as one input among many.