Poisson Distribution xG Match Score & Odds Predictor
Compute exact score probabilities, fair 1X2 market odds, and Over/Under goal expectations from team expected goals (xG).
1.75
1.10
Fair 1X2 Market Probabilities & Zero-Vig Odds
Home Win (1)
52.4%
Fair: 1.91
Draw (X)
24.1%
Fair: 4.15
Away Win (2)
23.5%
Fair: 4.26
Over / Under 2.5 Goals
Over 2.5:
55.8% (1.79)
55.8% (1.79)
Under 2.5:
44.2% (2.26)
44.2% (2.26)
Both Teams To Score (BTTS)
BTTS Yes:
56.2% (1.78)
56.2% (1.78)
BTTS No:
43.8% (2.28)
43.8% (2.28)
Understanding Poisson xG Modeling in Sports Analytics
The Poisson distribution expresses the probability of a given number of events occurring in a fixed interval of time if these events occur with a known constant mean rate and independently of the time since the last event. In association football, expected goals (xG) act as the Poisson intensity parameter ($lambda$).
By computing joint independent distributions $P(X = x, Y = y) = P(X = x) imes P(Y = y)$, quantitative analysts derive zero-vig fair market odds to identify bookmaker line inefficiencies.