I was thinking of this the other day. The vast, vast majority of data analysis and advanced metrics depend on the assumption that each possession is a unique and independent event.
I would argue that this is not the case. Basketball, and all sports, when you get down to it, is about a group of people competing against another. These people make endless series of split-second decisions while playing. Each decision made is influenced by a multitude of tiny factors. One of the more important factors are previous decisions and the consequences of those decisions.
To bring this back to basketball: I think that a possession is defined by more than the number of points that it produces.
If a pick and pop is run one play that results in a missed mid range jumper, but the next play, the same pick-setter rolls to the basket, is left open and scores, what effect did that pick and pop have on producing the 2 points in the later possession?
I'm going to veer off course a little here, and discuss tennis. As an individual sport with many distinct 'units' (rallies, games, sets), there should be a huge amount of data mining going on. But this isn't the case, and I don't think it's simply because no one's thought of trying.
I think it's because, not only does each rally affect subsequent rallies, but effective strategies are largely dependent on the opponent on the other side of the court. Given this, the strategies that are most effective in a given time period, likely depend on the 'meta'-sport. If 7 of the top 10 players have weaker forehands, shots to that side will be analytically superior. But does this define the sport? Is it a law of tennis that it is necessarily advantageous to hit to the forehand side? Or do these things change over time independently of the rules?