Saavik: Permission to speak freely, sir?
Saavik: I do not believe this was a fair test of my command abilities.
Kirk: And why not?
Saavik: Because… there was no way to win.
Kirk: A no-win situation is a possibility every commander may face. Has that never occurred to you?
Saavik: No, sir, it has not.
Kirk: And how we deal with death is at least as important as how we deal with life, wouldn’t you say?
Saavik: As I indicated, Admiral, that thought had not occurred to me.
Kirk: Well, now you have something new to think about. Carry on. –Star Trek II The Wrath of Khan
The genesis of this series came from an article on Carmelo Anthony by Jeremy Wagner of Roundball Mining Company:
The motivation the article is that the author, cannot believe the premise that Carmelo Anthony is inefficient. While he admits that he in most cases believes in statistics, in this case the statistics do not match his conclusions drawn from direct observation. Faced with this quandary, he decided that he was going to take a look at the numbers himself and while I admire his efforts I disagreed with his results. He argued that while Melo was not an efficient scorer he could be if he chose to be. I find a fundamental problem with this idea. I think young players can be molded to appropriate behavior by the right system but veterans are who they are for the most part.
There is an ineffable calculus that makes a person an efficient generator of offense. The end goal is simple, the player needs to find the best possible look or find the man that has it and get him the ball without turning it over. The details, is he open, is it a three, will he get an and 1, etc., are not so simple. If the player is successful his team scores. The needs of the many do indeed outweigh the needs of the few or the one. Measuring this is a challenge and by now you know how I feel about challenges.
The Metric & The Recap
So the goal is to develop a simple measure for the return (points) on investment (possessions) for players on offense (i.e. Player offensive efficiency). My concept is based on the idea that were I an NBA gm paying a player I would care about getting value (points) from my assets (possessions). I want to do this simply in a way that anyone can understand and using publicly available information. My method will be as follows:
- Look at all the data for the 2008, 2009 & 2010 for every player and get:
- Player Position
- Minutes Played (and eliminate all players with less than 1200 Minutes Played which leaves 383 players)
- Pts48 (points per 48 minutes)
- FGA48 (field goal attempts per 48 minutes)
- Pts per Attempt (pts per attempt = pts48/fga48)
- AST48 (assists per 48 minutes)
- TO48 (turnovers per 48 minutes)
- FTA48. This was added because of two of my fabulous readers (some dude and Man of Steele take a bow) who pointed out that some free throws while free do end possession. They’re right of course. So we are going to add the SD-MOS hack-a-shaq correction.
- Offensive possessions used per 48 (FGA48+AST48+TO48 +.44 *FTA48 term (an approximation for possessions used thru Free Throws from Prof. Berri) this is the possesions spent by the player)
- Offense Generated (Pts +Asst *2.68) per 48. The 2.68 is the average points generated per FGA for 2008 thru 2010
- Offense generated per possession used. This is the key measure as it reflects how many points the team generates when the player in question gets the rock.
- Offense Generated at 30 possessions used. Here I’m just projecting every player at an even number of possessions.
In the first piece I Looked at the Six Players from the original Melo Piece:
- Carmelo Anthony
- LeBron James
- Kevin Durant
- Dwyane Wade
- Kobe Bryant
- Kevin Martin
And it looked like this:
Carmelo is tied with Durant at the rear of the group (Durant is young and improving however).
But this was just the intro.
Now I could look at every player together but I noticed a funny thing:
Role & position play a huge role in how you affect the offense so for this series we’ll be looking at players by position and then ranking them.
Yesterday we looked at the generals of the offense, the napoleons of the court, the point guards.
Top Tier (>2 std dev): Jose Calderon (Mr. Efficiency), Steve Nash (too many turnovers for Steve), Jason Kidd,Chris Paul
2nd Tier (>1 std dev):Deron Williams,Chris Duhon, Brevin Knight, Antonio Daniels,Rajon Rondo, Travis Diener, Anthony Carter, Steve Blake (#%#% Lakers), Jason Williams, Jeff McInnis, Carlos Arroyo (Boricua power),Chauncey Billups
Now come the shooters
Gimme the Rock Rankings: Shooting Guards
80 Shooting Guards are eligible. Here goes:
Can you see what’s wrong with this picture? I’m sure that some of you do (In fact I’m sure some of you are screaming it). Usage. Yep, it’s the shooter dilemma. Take less shots and it’s easy to be more efficient. But someone has to shoot the rock and by shooting in volume you run into diminishing returns. Just what is diminishing returns? Let’s go to wikipedia :
In economics, diminishing returns (also called diminishing marginal returns) refers to how the marginal production of a factor of production starts to progressively decrease as the factor is increased, in contrast to the increase that would otherwise be normally expected. According to this relationship, in a production system with fixed and variable inputs (say factory size and labor), each additional unit of the variable input (i.e., man-hours) yields smaller and smaller increases in outputs, also reducing each worker’s mean productivity. Conversely, producing one more unit of output will cost increasingly more (owing to the major amount of variable inputs being used, to little effect).
This concept is also known as the law of diminishing marginal returns or the law of increasing relative cost.
So clearly the more you shoot and spend possessions the less marginal return you’re going to receive. Player, particularly shooting guards, are not in complete control of their destiny. Their teams and coaches call plays. They’re told to shoot or not to shoot. So not only do we need to divide players by role but usage figures in as well.
Luckily I have possession use stats for everybody and I can use those to standardize and divide every group into quarters:
- Group 1 :Very High Possession Usage for Position
- Group 2 : High possession Usage for Position
- Group 3: Medium possession Usage for Position
- Group 4: Low possession Usage for Position
Lets do this now:
In terms of comparison, this looks more reasonable now. We’re comparing apples to apples. By group:
- Group 1 >1 Std Dev Above Average (The Elite): Manu, Roy, Wade and Iverson
- Group2 >1 Std Dev Above Average (The Good): Hinrich,Terry,Allen
- Group3 >1 Std Dev Above Average (Serviceable): Jaric,West,Redick
- Group4 >1 Std Dev Above Average (Won’t hurt you but you need someone else to get the points): Barry, Damon Jones,Fred Jones, Anthony Parker
Only four players in the very high usage group are one standard deviation above the mean as generators of offense (Ginobli, Roy, Wade and Iverson). Wade deserves special mention because his use of possession is off the charts with an insanely high efficiency. Iverson it should be clarified is sneaking in at SG (he wouldn’t do so well as a PG). Kobe to be fair is the second highest user of possessions per 48 minutes and still comes in at above average (but he’s clearly not the best shooting guard on the list, that honor belongs to Wade and Ginobli’s the closest).
Group two features Hinrich, Terry and Allen as one std dev. above average generators of offense. Group 3 has Marco Jaric, Delonte West and J.J Redick.
Now If I use this logic and revisit Point Guards as well we see the following:
Group 1 >1 Std Dev Above Average (The Elite): Nash, Paul, Deron, Chauncey
Group2 >1 Std Dev Above Average (The Good): Calderon,Kidd, Rondo
Group3 >1 Std Dev Above Average (Serviceable): Diener & Carter
Group4 >1 Std Dev Above Average (Won’t hurt you but you need someone else to get the points): Duhon, Knight, Antonio Daniels,Blake, Jason Williams, Jeff McInnis Carlos Arroyo
Next up are the Small Forwards and our old friend Melo.
Comments as always are welcome
Note: Quick clarification I didn’t mean to imply that efficiency and usage are linked. Diminishing returns doesn’t actually happen when players increase their usage. Go to the article below for detail
What I meant is that it’s harder to be efficient for high volume scorers. So a fair comparison accounts for role and usage.
Whoops, said that wrong. What I meant is that the populations in the data are different depending on position and usage. For example: