M
Marsh Mello2
Guest
The tremendous number of variables that are there and the limited number of minutes the starters spend on the bench all make this a very hard thing to use to determine who the "best players" are.
To be fair, they weren't really intended to be a measure of "best players" and the stat's originators, Aaron Barzilai and Steve Ilardi, explicitly say that explaining:
It is important to note that the adjusted +/- rating is not a “holy grail” statistic that perfectly captures each player’s overall value...the estimates suffer from the issue of skewed sampling—the fact that most players usually find themselves on the court in the company of certain teammates and not others. As a result, it can be difficult to accurately tease out the individual effects of two players who almost always appear on the court together.
Rosenbaum and others have outlined different ways of addressing these issues, most notably using multiple years’ worth of data and augmenting regression results with additional analyses based on box score statistics.
The problem with using multiple seasons though becomes that that teammates change, and some players improve while others don't.
The bottom line is that while these stats can do a great job of telling us what units play well together, they don't tell us what makes a good player.
https://bleacherreport.com/articles...ss-stats-and-arguments-in-basketball%23slide5
To be fair, they weren't really intended to be a measure of "best players" and the stat's originators, Aaron Barzilai and Steve Ilardi, explicitly say that explaining:
It is important to note that the adjusted +/- rating is not a “holy grail” statistic that perfectly captures each player’s overall value...the estimates suffer from the issue of skewed sampling—the fact that most players usually find themselves on the court in the company of certain teammates and not others. As a result, it can be difficult to accurately tease out the individual effects of two players who almost always appear on the court together.
Rosenbaum and others have outlined different ways of addressing these issues, most notably using multiple years’ worth of data and augmenting regression results with additional analyses based on box score statistics.
The problem with using multiple seasons though becomes that that teammates change, and some players improve while others don't.
The bottom line is that while these stats can do a great job of telling us what units play well together, they don't tell us what makes a good player.
https://bleacherreport.com/articles...ss-stats-and-arguments-in-basketball%23slide5
