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I'm hoping I might be able to get some help in creating a forecasting model (in sports) looking at 2 variables that are not independent of each other.

I'll take US Football (same applies to rugby) as an example. The specific forecast I'm interested in here is the expected supremacy between two teams at the end of a match (Team B points minus Team A points).

There's a fair bit out there others have done looking at how to forecast the most likely supremacy outcome (the 'line' which generally isn't just stats, but involves looking at prices set by the betting world).

However, what I'm most interested in is how to create a forecasted probability of supremacies that are different to the line. As an example, if it is assessed that the E(line) is -4.3, I want to work out the probability that the supremacy would actually be any of:

-10, -9, -8, ....., 0, 1, 2, ...9, 10 (etc)

There is obviously error in the line itself, which needs to be taken into account, so I looked at historic data as a guide. As you might expect, combining the expectation of team A's point haul with that of team B's based on the line but independent of each other does not return a good enough fit (not negatively skewed enough and kurtosis too high) - team A's point haul will generally have an affect on team B's (and vice versa) - they are not independent of each other.

Are you able to please share some ideas on how to adjust for the fact that team A and team B are related in calculating supremacy. Really appreciate your help!

Basil