I'm working on a toy project that involves fantasy basketball, I know this is the quantitative finance stackexchange, but it seemed like the best place to ask this question.
My goal is to make predictions about fantasy points totals for different NBA players in upcoming games. I'm considering
fantasy_points_total a continuous random variable and my goal is to predict both a) an expected mean
E(mean) and expected variance
E(Var). I then want to use those predicted mean and variance to model and sample a normal distribution
N(mean, var) in a monte carlo simulation.
I'm using a gradient boosted random forest to predict
E(mean) and I'm satisfied with it's accuracy. I'm running into problems predicting
It seems like for a normal random variable with no changes over time one could expect that variance is constant and a decent way to calculate
E(Var) would be to look at all past data, calculate it's variance and use that past variance as
Is it possible to get a more accurate forecast of
E(Var) when variance might change due to other factors? For example in my case, home vs. away, injuries and opponent quality could all effect the variance of a players fantasy points expectations.