It should be interpreted as an average over the entire 25 year timespan while accounting for the prior. Looking at smaller samples (like five year RAPM results) would be more appropriate for estimating a player’s peak value.

That is correct (the team plus-minus is adjusted for how many possessions were played in that stint, though – it’s per 100 possessions, to be exact). And yes, if two players are always on the court with one another, we would not be able to isolate each individual player’s impact.

Great question. Unfortunately, the sklearn ridge regression function doesn’t have variance built-in or any way to extract it. I then tried to do the ridge regression manually with matrix multiplication as seen here, but my computer proved to not have the memory to handle the massive amount of data involved when doing so. It’s an interesting and important question so I’d love to see someone else tackle it.

I imagine that depending on priors there are different stable solutions?