xkonk wrote: ↑
Fri Jan 25, 2019 12:32 am
When are these stats calculated relative to the win differential? Like if the Dubs are +8 in 2015-16, is their age from the beginning of the 2015-16 season? The end? Is it minute-weighted or anything? Basically, I'm curious if the correlation is an artifact of teams that are having bad luck (and thus maybe a lower/negative win differential) giving their rookies and young guys more run, trading vets during the season, something like that.
At b-r.com, player 'age' for the season is at Feb. 1, roughly middle of the season. It's arbitrary, but it applies equally to all teams. Unless a team has lots of players born in winter or in spring, it's not too big an influence.
Yes, it's weighted by the minutes for each player. Thus, average age on the court, over the course of the season.
Negative correlation with youth could indeed be in part an artifact of younger teams tending to be developing, rebuilding, or even tanking.
Also, it looks like MOV has a .1 correlation? After Pythag wins is already based on MOV, right? Is this a sign that Pythag wins should have the MOV term further adjusted somehow? Is it just noise and telling us that anything up to (at least) a .1 correlation should be seen as unreliable?
It may be related to the previous question about how players are used on rebuilding teams. And how about the correlation with crowd size? Is that referee bias creeping in? If so, maybe in a very close game, they tend to side with the team they think is better?
Some have argued for a bigger exponent in pythWins, which would seem to be supported by the MOV bump. In playoffs, we might see better refs and less correlation..