Classics repost: What’s the Use?

The first principle is that you must not fool yourself – and you are the easiest person to fool.- Richard Feynman

(Editor’s Note: on vacation thru thursday so I thought I’d dig up a best of Arturo. Don’t worry, regular service will resume by Friday. For now? Enjoy)

For an engineer like me , unintended consequences are a way of life. Someone comes up with something fancy and we see some new way to apply it. Getting from point A to point B is the goal but somehow serindipitously we wind up at point X.

Let me explain. You may have noticed that it’s getting a little crowded in this space. This is all prelude. We are working on something. As part of that, I’ve been going thru some projects I had pending. One of them was continuing to breakdown and reverse engineer all the advanced stat models out there. I finally finished most of that this weekend and I will be writing it up at some point in the future.

But this post is not about that.

It’s about this:

Usage Percentage (The percentage of a teams plays a player uses while he’s on the floor): 100 * ((FGA + 0.44 * FTA + TOV) * (Tm MP / 5)) / (MP * (Tm FGA + 0.44 * Tm FTA + Tm TOV)).

And this:

And maybe a little of this:

Before we get too crazy here, feel free to go to the Basics for background. The numbers are courtesy of Nerdnumbers and all the stats,tables and madness that follows is based on 2010-2011 data for the NBA thru 03/26/11.

Let’s get started with some background:what’s usage and what are usage curves? Usage is the % of the available offensive possessions a player uses when he’s in the game. Usage curves come from Dean Oliver’s Basketball on Paper (which I may or may not have spent an inordinate amount of time perusing recently).In chapter 19, Oliver goes over “skill curves” that plot a player’s offensive efficiency when compared with usage . In the book, he does this on a player by player basis to show that players who take fewer shots become more efficient and as they take more shots their efficiency drops. He doesn’t  quite explain this other than with some hand waving voodoo magic which is something that could be frustrating to someone who was trying to understand what he was doing and trying to …. Really need to stop with the inner monologue.

Quick, Here’s a picture of a kitty to distract you!

So anyways, the theory, based on Dean’s skill curves (or usage curves) goes goes as  follows:

  • Certain players need to take all of the shots because their teammates can’t
  • There are diminishing returns on shooting. Taking lots of shots is not easy, the more shots you take the harder it gets.
  • Each player has an optimal range of shooting and players like Kobe are good with 15-25 shots a game but other players wouldn’t be.

The usage argument boils down to teams need a guy (like say Melo) to take shots (i.e. become high usage guys) and that this is the way to succeed in the NBA.

The counter argument is that you can always find someone to take that shot because the supply of shooters far outweighs the supply of shots (see Denver).
So we have a clear difference of opinion and the biggest problem has been the lack of a clear data set to attack it.
Funny thing that I have just the data set for it.
If I take every player game for 2010-2011 with more than 30 MP (about 7300 data points so far) and I plot Points per 20 Possesions vs Usage Rate it looks like so:
Can you see what I see? No? What if I sort every point by usage and work out 100 point averages and standard deviations and plot it again:
Ok, Let’s put it in Layman’s terms. Avg Points per possesion does not change based on usage. It stays about the same. Rather than a curve what I found is that it’s more like a usage line at about 20 pts per 20 possesions (The equation I get is y = 0.1921x + 19.638).
Variability does however change significantly and inversely proportional to usage. So more usage less variability. This implies selection bias and diminishing returns. So you get more shots only if you make at around an average rate and it becomes increasingly difficult to score at a better than average rate the more of a ball hog you are.
What if I look at Wins produced?
If I look at Wins per 48 minutes the higher my investment of possessions in a player the higher my wins but this is deceptive. First my risk is also higher because my variability skyrockets. Second, if I normalize to wins produced per 20 possessions I get:
Lower usage has a higher average rate of return but a higher variability (or risk).
For WP48, the implication is that increased usage implies higher wp48 (again typically higher return replies higher investment) but the downside is that it implies higher risk.High usage is a high risk strategy for the most part. We want a good spread going to players who are at above average at the needed usage.

I’ll leave you with a look at some players:
We’ll get into this one later.


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