Both Teams to Score: The Data Science Behind the Probability
The probability that both teams score (BTTS) is the sum of every cell in the score matrix where each side scores at least once. In top professional leagues both teams score in roughly 45-55% of matches, which is why an honest model's BTTS output usually sits in the 40-65% band and never reaches certainty. This page explains how that probability is built from dual xG, clean-sheet rates and a Poisson goal matrix. It is football data analytics, not betting advice.
What question does "both teams to score" really ask?
Stripped of betting-market language, BTTS is a single, precise statistical question: what is the probability that both teams score at least one goal in this match? It is the intersection of two events — the home side scoring and the away side scoring. Looking at one team's strength is not enough; the productivity of both attacks and the permeability of both defences must be weighed simultaneously.
For a sense of scale, the league base rate is a useful anchor: across the major European leagues, both teams score in roughly 45-55% of matches per season. Even before any match-specific data, this is close to a coin-flip event. The job of a model is to update that base rate up or down with match-specific evidence — not to turn it into a certainty.
Dual xG: from two goal rates to two scoring probabilities
The first layer of the calculation is dual xG: each team's attack profile against the opposing defence — including the expected-goals production covered in our xG guide — is converted into a match-specific goal rate (λ) per side. In a Poisson framework, the probability a team scores at least once has a compact form: P(scores) = 1 − e−λ.
A worked example sharpens the intuition: a home side with a goal rate of 1.5 scores at least once with roughly 78% probability; an away side at 1.2 lands near 70%. Assuming independence, the joint event is only about 54% — both teams "probably score" individually, yet both scoring together is still a coin-flip. That counter-intuitive shrinkage of the product is the most commonly missed lesson in BTTS analysis.
The mirror metric: clean-sheet rate
The mirror image of BTTS is the clean sheet: under Poisson, the probability a team concedes zero is P(0 goals) = e−λ. If the opponent's goal rate is 1.5, the clean-sheet probability is about 22%; at 1.2 it rises to roughly 30%. The "no" side of BTTS is exactly the event that at least one of those clean sheets happens.
This is why a single elite defence can matter more than two productive attacks: one team with a high clean-sheet rate drags the joint scoring probability down on its own. It is also why 11Stat's team profiles track defensive permeability and recent clean-sheet frequency alongside attacking output — BTTS analysis is, in equal measure, a defence analysis run in both directions.
The Poisson goal matrix: where the real number comes from
The exact figure comes not from the product shortcut but from the score matrix: a Poisson-based model computes the probability of every scoreline, and the BTTS probability is the sum of all cells where both sides score at least once (1-1, 2-1, 1-2, 2-2…). Its complement is the first row plus the first column, counting the 0-0 cell only once.
Here the Dixon-Coles correction is decisive: the low-scoring cells where pure Poisson is systematically biased (0-0, 1-0, 0-1, 1-1) are precisely the cells that decide the BTTS calculation. Reweighting them and modelling the dependence between the two teams' goals moves the joint scoring probability directly. That makes BTTS one of the most sensitive tests of matrix quality a goal model faces.
How 11Stat reports the both-teams-score probability
At 11Stat the BTTS figure is not the output of a single engine: the Poisson/Dixon-Coles core engine, the Elo-form engine and the advanced bivariate engine each produce their own matrix, and the published percentage is derived from their consensus, then calibrated against historical performance. Monte Carlo simulation stress-tests the estimate across match-specific scenarios.
The output is always presented as a two-sided percentage — for example 58% both score / 42% not both score — because a one-sided label hides the other half of the distribution. In balanced leagues it is normal for this figure to cluster in the 40-65% band; claims above 80% are rarely compatible with a healthy goal distribution and usually signal that uncertainty is being concealed.
The line between certainty rhetoric and probability analysis
Content promising that "both teams are sure to score" is statistically indefensible: dressing a probability whose realistic ceiling hovers around the low 70s in certainty language misleads the reader by hiding the variance. Even two productive attacks can go silent on the same night — per-team clean-sheet rates of 20-30% per match are the mathematical proof. 11Stat rejects that language by definition; no BTTS output is a guarantee, and none is presented as one.
The honest yardstick is not a single match landing but long-run calibration: of the matches labelled 60% both-score, roughly 60% should see both teams score. 11Stat accounts for this in its public calibration reports, and every hit-rate or ROI figure around it is a simulated paper-test metric. Analytical model — no outcome is guaranteed; this is not a betting service.
Frequently Asked Questions
What is the probability that both teams score?
It is the probability that each side scores at least one goal in the match. Technically it equals the sum of all score-matrix cells where both teams score at least once. In top professional leagues, both teams score in roughly 45-55% of matches per season.
How is the BTTS probability calculated from xG?
Each team's attack-versus-defence profile is converted into a goal rate (λ); under Poisson, the chance a team scores at least once is 1 − e^(−λ). The exact figure is then read off a Dixon-Coles-corrected score matrix by summing every cell where both sides score.
What is a typical both-teams-to-score percentage?
In balanced professional matches, model outputs mostly cluster between 40% and 65%. When two productive attacks meet, the figure can approach the low 70s; claims above 80% are rarely compatible with a healthy goal distribution.
If both teams have high xG, is both teams scoring certain?
No. Even a team with a goal rate of 1.5 fails to score about 22% of the time, so the joint event usually lands at 55-65% when two high rates are combined. High xG raises the probability; no combination of inputs produces certainty.
Is the BTTS probability consistent with totals and scoreline probabilities?
Yes. Both-teams-score, total-goals and 1X2 probabilities are all derived by summing cells of the same score matrix. That shared foundation is a structural consistency test that prevents percentages in different panels from contradicting each other.
Is this page betting advice?
No. 11Stat is a football data-analytics platform; the both-teams-score output is a probability report, not a recommendation to wager. No output carries a guarantee, and every hit-rate or ROI metric is a simulated paper-test value.