Since Dixon-Coles model uses Poisson distribution I guess it is not possible (or easy) to use xG values instead of actual goals to train the model. I tried multiplying xG values by 100 and then rounding, and it gave team strengths just fine (I guess) but simulating the games did not work correctly (the outcomes become 0).
Just wanted to learn if it is possible to somehow integrate xG into this. I came across your Dixon Coles and xG: together at last blog post and it suggests we can incorporate xG simulations into this by "tricking" the model. Personally I could not come up with a way to do it in Python (mainly because I did not understand what to pass as weights parameter).
Some help is appreciated if you are still maintaining this.
Since Dixon-Coles model uses Poisson distribution I guess it is not possible (or easy) to use xG values instead of actual goals to train the model. I tried multiplying xG values by 100 and then rounding, and it gave team strengths just fine (I guess) but simulating the games did not work correctly (the outcomes become 0).
Just wanted to learn if it is possible to somehow integrate xG into this. I came across your Dixon Coles and xG: together at last blog post and it suggests we can incorporate xG simulations into this by "tricking" the model. Personally I could not come up with a way to do it in Python (mainly because I did not understand what to pass as
weightsparameter).Some help is appreciated if you are still maintaining this.