Guide · 4 min read
Value betting and expected value, explained properly
What a fair price is, how to measure your edge against it, and how much variance to expect before the maths shows up in your results.
Updated 20 October 2026
Contents
What a fair price is
A value bet is a bet where the odds you get are higher than the true chance of the outcome justifies. Over many bets, that gap is your profit. Unlike a sure bet, you only bet one side, so any single bet can and often will lose.
The hard part is knowing the true chance. Nobody knows it exactly, but some prices are much better estimates than others. Sharp bookmakers such as Pinnacle, and the Betfair exchange on liquid markets, take big bets from informed players and move their lines quickly. Their prices, especially close to kick-off, are the best public estimate of the real probability.
The reference is only as good as the market behind it. A Premier League match an hour before kick-off has a very efficient sharp price. A lower-league game two days out, or an exchange market with a few hundred euro matched, does not. Be more sceptical of edges found against thin or early lines, and demand a bigger margin before betting them.
So the standard approach is simple: take the sharp price, strip out its margin to get a fair price, and look for soft bookmakers offering more than that.
Removing the margin
A bookmaker's implied probabilities add up to more than 100%. The excess is its margin, also called the overround or vig. To get fair probabilities you remove it.
The multiplicative method is the simplest. Divide each implied probability by the total:
fair p = (1/odds) / Σ(1/odds)
fair odds = 1 / fair p
The power method instead finds an exponent k so that Σ(1/odds)^k = 1. It takes slightly more margin off longshots than favourites, which matches how bookmakers tend to price. For two near-even outcomes the methods give almost the same answer. For a 1.20 favourite against an 8.00 outsider, the difference matters. The no-vig calculator does both.
Expected value
Once you have a fair probability p, the expected value of a bet at decimal odds is:
EV = p × odds − 1
The result is profit per unit staked. An EV of 0.05 means you expect €5 per €100 on average, over a very large number of similar bets.
A sharp book prices a tennis match at 1.95 / 1.95. Each side implies 1/1.95 = 51.28%, so the total is 102.56% and the margin is 2.56%.
Removing it: 51.28% ÷ 102.56% = 50.00% each, a fair price of 2.00 on both players.
A soft bookmaker offers 2.15 on player A. EV = 0.50 × 2.15 − 1 = +0.075, or +7.5%.
On a €100 stake you win €115 half the time and lose €100 half the time. Average result: (0.5 × €115) − (0.5 × €100) = +€7.50 per bet.
Real edges are usually smaller, 2% to 5%. Anything far above that deserves a second look: the sharp line may have moved, it may be a different market, or the soft price may be an error that gets voided.
Variance: why 100 bets prove almost nothing
This is the part most people underestimate. Take flat €10 stakes at odds of about 2.00 with a 4% edge. A 4% edge at 2.00 means a true win probability of 52%. Each bet either returns +€10 or −€10, and the spread of results over n bets is roughly:
SD of profit ≈ stake × odds × √(n × p × (1 − p))
| Bets | Turnover | Expected profit | Standard deviation | Rough 95% range (±2 SD) |
|---|---|---|---|---|
| 100 | €1,000 | €40 | €100 | −€160 to +€240 |
| 1,000 | €10,000 | €400 | €316 | −€232 to +€1,032 |
| 10,000 | €100,000 | €4,000 | €999 | +€2,002 to +€5,998 |
After 100 bets, the noise is two and a half times the signal. After 1,000 bets, a genuinely profitable bettor still has roughly a 1 in 10 chance of being down. Only after several thousand bets does the edge reliably dominate. Higher odds make this worse, because each result swings further.
Practically, that means you need a bankroll that survives the bad runs (see the bankroll guide) and a better way to judge yourself than short-term profit.
Kelly and fractional Kelly
The Kelly criterion sizes each bet to maximise long-run growth:
Kelly fraction = (p × odds − 1) / (odds − 1)
In the example above: 0.075 / 1.15 = 6.52% of bankroll. That assumes your fair probability is exactly right, which it never is. Full Kelly with an overestimated edge leads to large drawdowns, so most value bettors use a quarter or less, here about 1.6%, and cap any single stake. The Kelly calculator works this out for any fraction.
Closing line value: the best short-term evidence
Because results are so noisy, serious bettors track closing line value. Compare the odds you took with the sharp fair price just before the event starts:
CLV = your odds / closing fair odds − 1
If you took 2.15 and the fair closing price was 2.05, your CLV is +4.9%. The closing line includes all the information the market had, so consistently beating it is strong evidence of a real edge, often visible within a few hundred bets, long before profit is.
The flip side: beating the closing line is also exactly what soft bookmakers watch for. Expect restrictions sooner or later, and read how bookmaker limits work.
Sure betting vs value betting
| Sure betting | Value betting | |
|---|---|---|
| Bets per opportunity | One per outcome, all sides covered | One side only |
| Typical edge | 1% to 3%, locked in | 2% to 5%, in expectation |
| Variance | Very low | High |
| Main risks | Price moves, voids, rule mismatches | Losing runs, wrong fair price |
| Capital needed | Spread across many books | Fewer books, more buffer for swings |
| Account lifespan | Often shorter, arbs are easy to spot | Somewhat longer, still limited eventually |
Many people use both: arbs for steady turnover, value bets for higher expected return. Either way, losing runs are part of betting. Only stake what you can afford to lose.