Quick answer: Expected goals (xG) measures the quality of the chances a team creates. This calculator turns two teams’ xG into the probability of every match outcome — enter a home and away xG figure and it returns win/draw/loss probabilities, the most-likely scorelines, and both-teams-to-score and Over/Under fair odds.
What is xG (expected goals)?
Expected goals is a chance-quality metric. Every shot is assigned a probability of being scored based on factors like distance, angle and the situation it came from, and a team’s xG for a match is simply the sum of those shot probabilities. It captures how many goals a team should have scored from the chances it created — independent of whether the finishing that day was clinical or wasteful.
If you are new to the metric, our plain-English guide to xG walks through how shot models are built and what the numbers mean in practice.
How this calculator turns xG into odds
The model treats each team’s xG as the average (lambda) of a Poisson distribution — the standard way to model how often a rare, repeatable event like a goal occurs. From that average it computes the probability of the team scoring 0, 1, 2, 3 or more goals:
Combining the home and away distributions gives the probability of every scoreline. Those cells are then summed into the markets you care about: home win, draw and away win; both teams to score; and each Over/Under goal line. Dividing 1 by any probability gives its fair odds. It is the same engine behind our Poisson calculator, framed around the xG figures you already track.
A worked example
Suppose a home side is worth 1.8 xG and the visitors 1.1 xG. Entering those figures returns roughly a 52% home win, 24% draw and 24% away win, with 1-0, 2-1 and 1-1 among the most-likely scorelines and Over 2.5 goals landing a little under an even-money chance. If a bookmaker prices the home win at 2.20 (an implied 45%), the xG suggests the price is longer than the fair value — the basis of a value bet.
xG versus actual goals
Over a single match, actual goals decide the result but they are noisy — a team can win 1-0 while being comprehensively outplayed. Across a run of games, xG is a steadier signal of underlying performance and tends to predict future results better than past scorelines, because it removes the finishing variance that does not repeat reliably. That is why analysts watch the gap between a team’s goals and its xG: a big over-performance often regresses.
How to estimate the xG to enter
- Take each team’s recent rolling xG created and xG conceded from a public source.
- Adjust for this specific opponent — a strong attack against a leaky defence pushes the figure up.
- Add a small home-advantage bump to the home side (home teams average slightly more xG).
- Enter the two resulting figures above and read the market probabilities.
For the full workflow, see how to use xG to find value and how to model a football match.
Frequently asked questions
What is xG (expected goals)?
Expected goals (xG) is a measure of chance quality. Every shot is assigned a probability of being scored based on factors like distance, angle and situation, and a team’s xG for a match is the sum of those probabilities. It captures how many goals a team “should” have scored from the chances it created, independent of whether the finishing was lucky or wasteful on the day.
How do you convert xG into match odds?
Treat each team’s xG as the average (lambda) of a Poisson distribution, which gives the probability of them scoring 0, 1, 2, 3+ goals. Combining both teams’ distributions produces the probability of every scoreline, which you then sum into 1X2, BTTS and Over/Under probabilities and convert to fair odds (1 / probability). This calculator does all of that from the two xG figures you enter.
What is a typical xG for a team in one match?
Most teams land between about 0.8 and 2.0 xG in a single game. A dominant home performance might reach 2.5–3.0, while a side pinned back can finish under 0.5. Home teams average slightly higher than away teams, which is why a home-advantage bump is usually applied when estimating.
Is xG better than actual goals for predicting results?
Over a single match, actual goals decide the result, but they are noisy — a team can win 1-0 while being outplayed. Across a run of games, xG is a more stable signal of underlying performance and tends to predict future results better than past scorelines, because it strips out finishing variance that does not repeat reliably.
Where do I get xG numbers to enter?
Public sources publish per-match and rolling-average xG for most major leagues. A common approach is to take each team’s recent rolling xG created and xG conceded, adjust for the specific opponent’s strength, and add a small home-advantage factor before entering the two figures here.
Can xG predict a single match result?
No model predicts a single result with certainty — football has too much variance. xG gives you the probability distribution of outcomes, not a guaranteed score. Its value is in pricing markets consistently over many matches and spotting where a bookmaker’s odds disagree with the underlying chance quality.
Related calculators
- Poisson calculator — full 6×6 scoreline heatmap with home-advantage control.
- Correct score matrix — the most-likely scorelines with fair odds.
- BTTS calculator — both-teams-to-score probability from team xG.
- Over/Under calculator — total-goals fair odds for every line.
For informational and educational purposes only. xG models estimate chance quality; no model guarantees a result. 18+.