Glossary

The terms used throughout Pro Edge^™, defined plainly. For how these concepts fit together in practice, see our Methodology.

Arbitrage (arbing)

Simultaneously backing every outcome of an event across different bookmakers at prices that collectively guarantee a profit regardless of the result. A function of price discrepancy between books rather than predictive edge, and not the strategy this service is built around.

Asian handicap

A goal-based handicap applied to a match before kick-off to level two unevenly matched sides, removing the draw as a possible outcome (or reducing it to a partial refund on a quarter-line). Common in football markets as an alternative to the standard three-way match result.

Backtesting

Testing a model's selections against historical prices and results to estimate how it would have performed, before any of it is risked on a live market.

Bankroll

The fund a bettor sets aside for wagering, kept separate from day-to-day finances. A unit is typically fixed as a small, constant fraction of starting bankroll and then held flat; Kelly-derived sizing instead recalculates the stake against current bankroll on every bet. Either way, sizing is defined relative to bankroll, not as a fixed cash figure, so a staking plan stays valid regardless of its size.

Bayesian updating

A method of revising a probability estimate as new evidence arrives, rather than treating the original estimate as fixed. Formally, the posterior probability of an outcome given new evidence is proportional to the prior probability of that outcome multiplied by the likelihood of the evidence under it: P(outcome | evidence) ∝ P(outcome) × P(evidence | outcome). In our pipeline, the prior is the model's early-week estimate and the evidence is late team news, so a fixture's assessment can shift as lineups are confirmed close to kick-off, rather than staying frozen at whatever it was days in advance.

Calibration

A model is well calibrated if, among every selection it assigns a given probability, that outcome actually occurs at roughly that rate over a large sample - a 60% probability should win close to 60% of the time, not 80% or 40%. Calibration, not accuracy on any single pick, is the property a probabilistic model is judged on.

Closing-line value (CLV)

The gap, in percentage points of implied probability, between the closing price - the market's final, most efficient price before the event starts - and the price taken on a selection: CLV = closing implied probability − implied probability at the price taken. A positive figure means the market's final assessment priced the side taken as more likely to win than the odds implied when the selection was published, in other words a better price than the market ultimately settled on. Computed the same way as edge, but benchmarked against the close rather than the price at publication. Consistent positive CLV, averaged across a large sample, is the most reliable single indicator of a repeatable edge, independent of any one result.

Confidence interval

A range around a model's point estimate that reflects how certain - or uncertain - it is, rather than presenting a single probability as though it were exact. A wide interval signals that the underlying methods disagree or the available data is thin, and is itself a reason for caution rather than something to be averaged away. Strictly, frequentist methods produce confidence intervals and Bayesian methods credible intervals; the term is used here in its everyday sense to cover both.

Decimal odds

A price format, standard across Europe, in which the figure quoted is the total return per unit staked, including the original stake (decimal odds of 1.91 return £1.91 from a £1 stake, a £0.91 profit). We quote prices exclusively in decimal odds rather than fractional or American formats, since it converts most directly to implied probability.

Drawdown

The decline from a bankroll's peak to its subsequent trough before a new peak is reached. A normal feature of any genuinely profitable strategy - variance guarantees it will happen - so its size and duration matter more than its mere occurrence.

Edge

The percentage-point gap between a model's fair probability for an outcome and the market's overround-adjusted implied probability. A positive, backtested edge is the precondition for publishing a selection.

Ensemble (model ensemble)

A forecasting approach that combines several independently built models - each capturing different signal - and reconciles their outputs into a single probability, rather than relying on any one method alone. Disagreement between the constituent models is treated as information about uncertainty, not noise to be discarded.

Expected value (EV)

The probability-weighted average profit or loss of a bet - what it would return on average if repeated many times at the same probability and price. A positive-EV bet is profitable on average over a large enough sample, even though any single instance of it can still lose.

Flat staking

Sizing every bet at the same fixed unit, regardless of the size of its edge. Simpler to verify and more behaviourally robust than edge-proportional methods, at the cost of some theoretical long-run growth - see Kelly criterion for the alternative, and our Methodology for why we publish this way.

Gradient-boosted trees

A machine-learning technique that builds a sequence of shallow decision trees, each one trained on the error the trees before it still leave unexplained - a form of gradient descent carried out over functions rather than parameters, typically scored on log loss when the target is a probability. Regularised by shrinking each tree's contribution and capping its depth, which trades a slower fit for one far less prone to memorising noise. Well suited to capturing non-linear interactions between variables - fixture congestion compounding with travel distance, for example - that simpler additive models like Poisson regression miss unless the interaction is specified by hand.

Hedging

Placing a bet on the opposing outcome of an existing position to lock in a profit or cap a loss before an event finishes, usually after a significant price move. A risk-management decision independent of whether the original selection still represents value.

Implied probability

The probability of an outcome implied by its price: for decimal odds, the reciprocal of the odds (a price of 2.00 implies 50%). Summed across every outcome of a market, implied probabilities exceed 100% by exactly the bookmaker's overround, which is why they are adjusted before being compared with a modelled probability.

Kelly criterion

A staking formula that maximises the long-run geometric growth rate of a bankroll while keeping the risk of ruin bounded. For decimal odds d and win probability p, the full-Kelly stake is the fraction f* = (p(d − 1) − (1 − p)) / (d − 1) of bankroll; fractional variants (half-Kelly, quarter-Kelly) trade growth for lower volatility. Pro Edge^™ publishes flat 1-unit stakes rather than Kelly-sized ones, for consistency and ease of verification; a member who wants Kelly-style sizing can apply the formula to their own bankroll against our published edge, entirely at their own discretion and risk.

Line movement

The change in a market's price between when it opens and when it closes, driven by new information and the weight of money on either side. A price that has already moved against a published selection by kick-off has, by definition, eroded some of the original edge.

Log loss

A scoring rule for probabilistic forecasts that penalises confident, wrong predictions far more heavily than cautious ones. For a single prediction p on an outcome that resolves as 1 (occurred) or 0 (didn't), log loss is −[y·log(p) + (1 − y)·log(1 − p)]; averaged across every prediction, it rewards both accuracy and honest uncertainty. A model that is well calibrated and avoids overconfidence scores better on log loss than one that is merely accurate on average, which is why we use it - rather than plain accuracy - to score every backtest.

Market efficiency

The degree to which a price already reflects all available public information. A perfectly efficient market leaves no edge to find; the case for a quantitative approach rests on markets being efficient enough that the closing line is a meaningful benchmark, yet not so efficient that a disciplined, backtested edge cannot survive contact with them.

Modelled probability

The probability our models assign to an outcome, calculated independently of - and before seeing - the market price.

Moneyline

A market betting purely on which side wins a fixture, with no handicap or goal line attached - the most direct expression of a model's probability for the match result itself.

Monte Carlo simulation

Re-running a modelled scenario thousands of times with inputs drawn at random from their estimated distributions, to build a distribution of outcomes rather than a single point estimate - a common technique for translating goal-rate or scoring-rate models into full scoreline probabilities.

Net yield

Net profit expressed as a percentage of total stakes - profit divided by total turnover, not by bankroll or any other base. Under flat, unit-sized staking the figure is numerically identical to return on investment, which is why the two are often used loosely as synonyms; they can diverge once stake sizes vary, which is why we state which one a given figure represents rather than treating them as interchangeable.

Normalisation

Adjusting raw data onto a common, comparable scale before it is fed into a model - correcting for confirmed lineup changes, fixture congestion, or known structural biases such as home advantage, so the model isn't comparing distorted figures.

Overfitting

A model that has learned the noise in its historical sample rather than the genuine signal, producing a backtest that looks excellent and live performance that doesn't match it. Guarded against by testing on data the model has never seen and discarding any selection whose edge doesn't survive that out-of-sample bar.

Overround (or "vig" / "juice")

A bookmaker's built-in margin: the amount by which the sum of the implied probabilities across a market's outcomes exceeds 100%. Normalising those probabilities back to a sum of 100% recovers the market's overround-adjusted (fair) prices - the figures our modelled probabilities are compared against.

Poisson regression

A statistical model for count-based outcomes - goals scored, for instance - that estimates the rate λ at which an event occurs as a function of explanatory variables, such as each side's attacking and defensive strength: the probability of exactly k goals is P(X = k) = e^−λλ^k/k!. The workhorse for scoring-based markets like totals and correct score, though two sides' goal counts are not quite independent in practice - low-scoring draws occur slightly more often than the naive product of two Poisson rates predicts, an effect the Dixon-Coles correction adjusts for directly.

Push

A bet that neither wins nor loses because the result lands exactly on the handicap or total line, returning the stake in full. Common on whole-number lines (e.g. a 2-goal handicap or a 2.0 total) rather than the quarter- or half-lines that avoid the possibility entirely.

Regression to the mean

The tendency for unusually good or bad results to be followed by results closer to the underlying average, simply because extreme outcomes are partly luck. Mistaking a hot or cold streak for a genuine change in ability is one of the most common analytical errors in sports betting.

Return on investment (ROI)

Net profit as a percentage of the capital committed to generate it. In flat-staking contexts such as ours, ROI and net yield are calculated the same way and return the same figure; the distinction becomes material under variable or compounding stake sizes, where capital committed and total turnover are no longer equal.

Risk of ruin

The probability that a staking strategy depletes a bankroll to a level it cannot recover from, given a stated edge and bet sizing. Kelly-derived sizing keeps risk of ruin mathematically bounded by shrinking stakes as a bankroll falls; flat, unit-based staking - our own published approach - instead manages it through a conservative, fixed unit size and the discipline of never increasing it after a loss.

Sample size

The number of independent observations - selections, in our case - a result is based on. A short run of wins or losses is dominated by variance and says little on its own; only over a large enough sample does an edge's true size become statistically visible.

Sharp money

Wagers placed by bettors with a demonstrated history of beating the closing line, whose action tends to move market prices as bookmakers adjust around it.

Statistical significance

A measure of how unlikely a result would be to occur by chance alone if there were, in fact, no real edge. We stay silent on a fixture rather than publish a selection whose modelled edge hasn't cleared this bar.

Steam move

A sudden, sharp price movement across multiple bookmakers simultaneously, typically triggered by coordinated sharp money. A steam move after a selection has been published is a real-time signal that the market is converging toward - or away from - the price that was taken.

Survivorship bias

The distortion that creeps into a backtest when it only includes data that "survived" to be available for analysis - discontinued leagues, delisted teams, or restated historical odds - while silently excluding the cases that didn't. Backtests built on incomplete historical data routinely overstate how well a strategy would actually have performed.

Totals (Over/Under)

A market betting on whether a combined statistic in a fixture - most commonly total goals - finishes over or under a stated threshold (e.g. Over 2.5 goals), rather than on the match result itself.

Transaction costs

The frictions that erode a theoretical edge once it meets a live market - reduced limits, wider prices on smaller books, or a price moving before a stake can be placed. A backtest that ignores transaction costs overstates real-world performance, which is why ours are factored in before a model clears the bar for publication.

Unit (u)

A fixed proportion of bankroll used as the base measure for stake sizing (e.g. "1u" or "1.5u") - capped at 1/20th (5%) of bankroll and often smaller, then held fixed for a minimum of 30 days rather than recalculated bet to bet - so performance can be compared independently of any individual bankroll size.

Value bet

A bet whose odds are longer than the fair odds implied by the outcome's true probability - the bookmaker's implied probability understates the actual likelihood, so the bet carries positive expected value. Synonymous with a positive-edge selection.

Variance

The natural spread of outcomes around a statistical average. A genuinely profitable model can, and will, still produce losing streaks; variance alone does not indicate a broken model.

Void bet

A bet cancelled and the stake returned in full, typically because a fixture was postponed, abandoned, or didn't meet the conditions stated when the bet was placed. Distinct from a loss or a push: no result is graded either way.

Win rate

The proportion of settled selections that finish as winners. On its own it says nothing about the price taken on each selection, so two runs with an identical win rate can represent a genuine edge or a steady loss, depending on the odds - see closing-line value for the metric that accounts for this.