Match Simulator — What If?

Expected goals — commonly abbreviated to xG — is a statistical measure that quantifies the quality of a goal-scoring chance. Each shot is assigned a value between 0 and 1 based on historical data: how likely a shot from that location, angle, and situation was to result in a goal. A tap-in from six yards might carry an xG of 0.85, while a long-range effort from 30 metres might be just 0.04.

Summing the xG values for all of a team's shots in a match gives the team's total xG — an estimate of how many goals they "should" have scored based on the chances they created. This makes xG a better predictor of future performance than the raw scoreline, which can be heavily influenced by luck, goalkeeping heroics, or woodwork.

In this simulator, you enter xG values as the "expected rate" of goals for each team — think of it as the average goals you'd expect them to score if this match were played many times over under the same conditions.

The Poisson distribution is a probability model that describes how many times a random, independent event occurs in a fixed interval — given an average rate. In football, goals per match fit this model reasonably well because goals are relatively rare, largely independent events.

Given a team's expected goals (lambda), the Poisson formula calculates the probability of them scoring exactly 0, 1, 2, 3 … goals. Multiplying the home and away distributions together gives the probability of every possible scoreline. From there, it is straightforward to derive 1X2 (home win / draw / away win) probabilities, Both Teams to Score (BTTS), and Over/Under totals.

This model is a simplification — it does not account for in-game dynamics, red cards, or the correlation between the two teams' scoring rates. But it is a solid baseline used by professional analysts and bookmakers as a starting point. Use it as an educational "what if" tool to explore how changing xG inputs shifts the probability landscape.