I am familiar with this basic Poisson model for modelling the outccome of a football match: https://help.smarkets.com/hc/en-gb/articles/115001457989-How-to-calculate-Poisson-distribution-for-football-betting
This model assumes that the goals scored by team A (say the home team) is independent to the number of goals scored by team B (the away team), and we multiply the Poisson PDFs together.
Is there any way to incorporate dependence into this, it would seem natural that there is a dependence between the two, are there any alternatives to just multiplying the models together?
The model incorporates interdependence of the two teams in the calculation of average goals. What it does not incorporate is a claim that if A scores a lot of goals you should expect that B scores more or less goals than average. Maybe A gets tired scoring all those goals and is so far ahead it allows extra goals to B. Maybe B gets discouraged and doesn't try very hard. If you believe close games are more likely, you could multiply all the entries in the table by some factor, say $\frac {1.2}{1+\text { goal differential}/10}$, then add up all the values and divide by the total to normalize the probability. Whether that improves the prediction is left as an exercise.