The story of the Houston Rockets across much of the past ten years has been one of high hopes dashed by injury.  Much of this tragedy has focused on Yao Ming, who entered the league in 2002 as the number one draft pick but has subsequently played fewer than 60 games in five of his nine NBA seasons (including only five games across the past two years).  Although injuries have hit Ming hard, he is not the only Rocket star to spend significant time just watching basketball in Houston.  Tracy McGrady came to Houston in 2004 and across the next five NBA seasons missed more than 100 regular season games.

The injuries to Ming and McGrady might leave fans of the Rockets thinking their team is cursed.  Certainly one wouldn’t think that this team could be described as “injury-resistant”.  But this is exactly the argument I am going to try to make.

The Pareto Principle Again

The story I am going to tell doesn’t begin in Houston, but a bit further to the East. 

Okay, more than a big further.  The big story in the NBA is in Miami.  And when we look at this team, we see that the Super Friends — LeBron James, Dwyane Wade, and Chris Bosh – have combined to produce 27.9 wins so far this year (prior to Thursday night and according to the automated Wins Produced numbers from Andres Alvarez).  This total is 75.6% of the Heat’s 36.5 Wins Produced. 

Although much attention is paid to the Super Friends, a similar story can be seen with respect to the Boston Celtics and LA Lakers.  The Lakers are led by Lamar Odom, Pau Gasol, and Kobe Bryant.  This trio has combined to produce 28.0 wins, or 78.9% of the team’s total.  And the Celtics trio of Rajon Rondo, Kevin Garnett, and Paul Pierce has combined to produce 25.8 wins, or 73.3% of the team’s Wins Produced.

The dominance of a team’s top three players is not unique to these three teams in 2010-11.  From 1977-78 to 2009-10, the top three producers of wins on each team has produced – on average – 76% of their respective team’s total Wins Produced.  This pattern illustrates the Pareto Principle, which states that 80% of outcomes can be linked to 20% of people.  Although it’s not clear (at least to me) how much this simple rule – originally noted by the famed economist Vilfredo Pareto – applies in the general economy; as noted in the past, it does appear that the Pareto Principle works in the NBA.  

Houston History

To further illustrate this story, let’s return to the subject of the Houston Rockets.  The Rockets won two NBA titles in 1994 and 1995.  But in 1996, the Chicago Bulls – led by Michael Jordan, Dennis Rodman, and Scottie Pippen – won 72 regular season games and the NBA title.  This trio combined for 57.5 Wins Produced, or 78.5% of the team’s total. 

As if to specifically counter the dominance of the Chicago Bulls, the Rockets added Charles Barkley in 1996.  Sir Charles joined a team that already had Hakeem Olajuwon and Clyde Drexler.  In 1995-96, this trio missed nearly 40 games.  But had they each played an entire 82 game season, this trio would have posted the following numbers in 1995-96:

• Charles Barkley: 21.7 Wins Produced
• Clyde Drexler: 18.2 Wins Produced
• Hakeem Olajuwon: 16.9 Wins Produced

Altogether, this trio would have produced – if healthy for the entire 1995-96 season – 56.9 wins; a mark quite close to what the Bulls leading trio produced that season. 

So hopes were quite high for the Rockets in 1996-97.  Unfortunately, although Olajuwon only missed four games, Hakeem’s productivity declined (he was 34 years of age).  And Barkley and Drexler missed 49 games. The Rockets still advanced to the Western Conference Finals, but lost in six games to the Utah Jazz (one of the better teams to never win an NBA title).

The idea that a magical trio, though, could lead a team to the promise land persisted in Houston.  When Drexler retired after the 1997-98 season, Scottie Pippen was brought in to complete the trio.  In the lock-out shortened season of 1999 this trio did combine to produce 92.8% of the team’s Wins Produced.  But after a first round loss to the LA Lakers, and the continued aging of the Rockets’ top trio, this team began to take a different direction.

Before we get to that direction, let’s review the history of the Rockets before this past decade.  The following table reports the top trio in Wins Produced for the Rockets from 1977-78 to 1998-99.

The key number is the percentage of wins produced by the top trio.  From 1977-78 to 1998-99, the percentage of wins produced by Houston’s top trio averaged 83% and only fell below 70% once (in 1994-95).  Before moving on, I also want to note that the top trio on each of these teams averaged 34.4 Wins Produced, while Houston’s teams across these 22 seasons averaged 42.4 Wins Produced.  In other words, everyone else on the team only averaged 8.1 Wins Produced.

The Rockets Today

Again, the dominance of a team’s top trio is not unique to the Rockets from 1977-78 to 1998-99.  This is often how teams are structured in the NBA.  But after the 1998-99 season — as the following table illustrates — Houston began to build their team’s differently. 

Across these eleven seasons, the percentage of wins produced by the top three players on the team has never exceeded 66.8%, and the average has been 56.5%.  The top trio has also only produced an average of 25.1 wins per season.  But the players not in the top trio are producing 19.6 wins per season.  Consequently, although the Rockets “stars” can’t match what we saw before 1999-00, the teams in Houston are actually a bit better.

And this story continues this year.  The following table reports the Wins Produced we see from the players on the Rockets after 51 games in 2010-11.

The Rockets are currently led by Kevin Martin, Shane Battier, and Kyle Lowry.  This trio has produced 14.1 wins this season, or 54.8% of the team’s total.  If this trio was what we had typically seen in the NBA – where a team’s top trio produces 76% of a team’s wins – then the Rockets would currently have about 18 Wins Produced.  And that would put them on pace for about 30 wins this year.  Just as we have seen across the past decade, though, the Rockets are much more than their top three players.

When we look past the top trio we see five players – Chuck Hayes, Luis Scola, Chase Budinger, Brad Miller, and Patrick Patterson – who are above average (or very close) and who have played more than 300 minutes.  These five players have already produced 11.3 wins.  And because the Rockets have these five players, the team’s efficiency differential (offensive efficiency minus defensive efficiency) is 0.1.  No, that isn’t very impressive.  But it is consistent with a 0.500 team, which is better than a team that wins only about 30 games. 

Now let’s return to the subject of injuries.  Imagine the Heat lose LeBron and/or Wade (which almost happened on Thursday night).  Or the Lakers lose Kobe and/or Gasol.  Or the Celtics lose Garnett and/or Pierce.  What do we expect to happen?  Because these teams rely so much on their top players – as team’s throughout NBA history have generally done – an injury to one or two top players can dramatically change a team’s fortunes. 

Across the past decade, though, the Rockets have relied less on their top players.  So when these player get hurt – as has often happened – the Rockets haven’t collapsed.  In other words, the Rockets are “injury-resistant”. 

Of course that doesn’t mean that injuries don’t hurt (pun intended).  Houston would be better off this year with a healthy Yao Ming.  But Houston seems to be constructed so that impact of losing a top player is less than what we would see on other teams. 

Let me close by noting that this Daryl Morey has been responsible for building the Rockets since 2007.  And since 2007, the team’s reliance on the top trio has declined. The trend we are observing, though, pre-dates Morey’s arrival.  So this may not be about the current decision-makers in Houston.  But whoever is responsible, how Houston is currently building its team, does seem different from what we have seen in the past.   And the practice of defying Pareto, does appear to yield some benefits.

- DJ

Editor’s Note: The following was written by Andres Alvarez and originally posted at Nerd Numbers.  So some of you have already read Andres’ excellent analysis of the upcoming Rookie-Sophomore game.  For those who are not reading Andres on a daily basis, the following gives you an excellent idea of what you are missing. And let me also note that there are great stories being posted on a regular basis on many of the Wages of Wins Network of blogs.  The following blogs each has at least one story that has been posted in the just the past few days.  So click on over and check these out (and by the way, in the time it took me to re-post this from Andres, he has another post up on LeBron James).

• Arturo Silly Little Stats
• Courtside Analyst
• Hickory High
• Miami Heat Index
• NBeh?
• Nerd Numbers The Blog
• Pistons by the Numbers
• Roblog
• Sport Skeptic

In the 11 years the NBA has held a rookie-sophomore game the sophomores hold an 8-3 edge. This is not all that surprising. An NBA player’s peak age is around 24-26 and most rookies are closer to 19-20. That extra year definitely helps. None the less, three talented rookie squads have actually managed to best their elders. Here’s a quick trip down memory lane with a snap shot of how each team looked going into the challenge.

Table 1: The 2000 Rookie Squad. Numbers up to All-Star Break

Table 2: The 2000 Sophomore Squad. Numbers up to All-Star Break

2000 was a good year for the rookies. Only Wally Szczerbiak was a below average player and the squad on the whole was 50% better than an average NBA team. Of course on the flip side the sophomore team was terrible! Only Paul Pierce and Mike Bibby were playing respectably. It wasn’t a surprise that the rookies took this game.

Table 3: 2002 Rookie Squad. Numbers up to All-Star Break

Table 4: 2002 Sophomore Squad: Numbers up to All-Star Break

2002 was a little more interesting. Let’s give a little credit, the rookie squad was average. To be average as rookies though is actually impressive. Just like in 2000 the sophomore squad was downright terrible. Miles, Richardson, Miller and Turkoglu weren’t a bad starting core, but with Fizer, Martin and Mihm putting up negative numbers, this game was pretty lopsided. I’ve heard the 2000 draft listed in its entirety as a bust. It’s hard to disagree with that.

Table 5: 2010 Rookie Squad, Numbers up to All-Star Break

Table 6: 2010 Sophomore Squad. Numbers up to All-Star Break

2010 is funny to look at. The numbers say the rookie squad should have lost. Let’s not take anything away from the rookies, they were quite talented. As I’ve mentioned having a bunch of first year players perform at an average NBA team level is impressive. The sophomore squad looked pretty good though. If I were to have advised the coach it would have gone something like this: “Your top three players are Love, Gasol and Gallinari. Play them together as your front court and you’ll do great. Also, you probably don’t want to give a lot of minutes to Gordon, Beasley or Mayo.” Turns out the coach of the sophomore squad did almost exactly the opposite. He played Lopez and Beasley in front of Love and Gasol and gave fewer minutes to Gallinari than Mayo. While the rookies should be happy with a win, the truth is that bad coaching lost this game.

Table 7: 2011 Rookie Squad. Numbers up to Feb 1st 2011

Table 8: 2011 Sophomore Squad. Numbers up to February 1st 2011.

Tyreke Evans has taken a step back, in part because of an injury. Curry, Ibaka and Blair look strong. None of this matters because of a two-headed rookie beast named Blake Griffin and Landry Fields. Together these two have racked up almost as many wins as the entire sophomore squad. Throw in a talented John Wall, Greg Monroe and Derrick Favors and you’ve actually got a killer line-up for any team. This year should go to the rookies unless a terrible coach doesn’t realize that Blake Griffin and Landry Fields belong on the floor together.

-Dre

This article uses the Wins Produced and Wins Produced per 48 minutes (WP48) metrics. These use the player’s box score statistics, the team statistics, and league averages to estimate how the player contributes to winning. An average player has a WP48 of 0.100. For a regular starter this would generate around 6.0 wins for the team in a full season of play. By contrast a “superstar” player has a WP48 of 0.250 and in the same minutes would generate around 15.0 wins for the team.

Ian Levy is a Third-Grade teacher by day and amateur basketball analyst by afternoon (he usually sleeps at night). Ian suffers from a rare psychological condition known as Anti-Homeritis which renders him incapable of rooting for hometown teams. He grew up in Upstate New York and has therefore been a lifelong Indiana Pacers fan. He writes his own basketball blog, Hickory High, and is a contributor at IndyCornrows.  Ian currently lives in Boise, Idaho, where he roots against the Boise State Broncos.

On November 10^th the Indiana Pacers defeated the Sacramento Kings and reached the middle of a four game West Coast road-trip. At that point they were 9-7 with wins against the Nuggets, Lakers and Heat. Third-year center Roy Hibbert was averaging 16.1 Pts/G, 9.5 Reb/G, 3.1 Ast/G, and 2.1 Blk/G on 48.8% shooting; looking like a lock for the league’s Most Improved Player Award. The team’s preseason goal of making the playoffs for the first time in four seasons seemed assured.

Since then the Pacers have gone 9-20. That includes three separate losing streaks of 3 games as well as a 6 game losing streak. The Pacers fell out of playoff position, Roy Hibbert completely regressed, and the bottom dropped out of the team’s morale. Things came to a head last weekend with a loss to Chicago on Saturday and the firing of Head Coach Jim O’Brien on Sunday.

There have been several issues contributing to this huge collapse over the past two months. The first is the distribution of minutes. The second is the tremendous decline of Roy Hibbert. The third revolves around reasonable expectations. Let’s start by looking at the distribution of minutes. The table below shows the position, minutes played, Adj. WP48, WP48 and Wins Produced for each of the Pacers this season (numbers taken originally from the Automated Wins Produced numbers provided by Andres Alvarez).

Thus far there are four players who have been producing wins in the negative range for the Pacers. Three of those four, Solomon Jones, T.J. Ford and James Posey, were playing significant minutes off the bench under Jim O’Brien. Minutes for those three players came at the expense of much more productive players like Jeff Foster, Darren Collison, Tyler Hansbrough and Josh McRoberts.

n addition players were not always used in ways that played to their strengths. James Posey has been played primarily at power forward. This may be the position he’s most suited to play at this point in his career, but it certainly doesn’t make it a good choice for the team. Towards the end of his tenure O’Brien began to change his rotations but it was a case of too little, too late.

The second issue is the curious case of Roy Hibbert. On December 13^th, already two weeks into his slide, I wrote a post for IndyCornrows looking at the Pacers and Wins Produced. Up to that point in the season Roy Hibbert had posted a WP48 of 0.211. As of the first of February his WP48 is 0.023. In the span of two months he went from playing like an All-Star to barely keeping his production in the positive range. To find out how that could be possible let’s look at some of his individual statistics for those two time periods.

The table below shows Hibbert’s numbers for October and November compared to his numbers for December and January and his numbers for last season. The values in red are below average for a center.

Hibbert’s numbers for October and November were a significant improvement over last year’s performance. However, his numbers for December and January were among the worst of his career and much worse than what he produced last season. While his performance the first two months of the season may have been an unsustainable anomaly, his performance the last two months appears to be equally anomalous.

This is Hibbert’s third season in the NBA, a time when players typically show improvement. In addition, he spent the summer refining his fitness level, dropping nearly 20 pounds. He began receiving treatment for “athlete-induced” asthma, a previously undiagnosed ailment which plagued him for years and drastically impacted his endurance.

Everything appeared in concert for Hibbert to step forward as a legitimate NBA contributor, and for two months we saw that and more. So why the sudden drop off? The answer is probably a mix of learning to play at a lower weight, receiving an increase in defensive attention with his early season success, being utilized poorly by your coach and a loss of self confidence. How could a player having a breakout season suddenly lose confidence? Maybe if he heard his coach say this:

“I think that Roy would say – and I certainly share this belief – I don’t think he’s having a very good season. I think that he can play at a much, much higher level right away than he’s doing right now. I don’t think he’s being the facilitator of our offense that I think he’s going to become; I think he’s a great passer. I think he can be a much better rebounder. And my expectations probably aren’t as high as Roy’s expectations. So even though he could be mentioned as Most Improved, I think he has a long way to go and he has a long way to go this year.”  – Jim O’Brien speaking to the media after a loss to the Chicago Bulls 12/13/10

Hibbert’s struggles have also affected his teammates. Hibbert spends most of his time on the floor with Mike Dunleavy, Brandon Rush, Danny Granger, Darren Collison, Tyler Hansbrough, and Josh McRoberts. You can see from the Wins Produced chart above that of those six only Dunleavy and McRoberts have been producing at an above average level. Dunleavy and McRoberts are also the only two who’s WP48 have increased compared to last season.

Hibbert is not solely to blame for their struggles. For better or worse the Pacers’ offense this season was constructed with him acting as a focal point as both scorer and distributor. When he was playing well things were very easy for his teammates. Since he’s been struggling things have gotten much more difficult for his teammates.

The last issue to examine is the idea of realistic expectations. I wrote a piece on the Pacers in this forum in early August and at that point they looked like a 32-33 win team. When they won 9 of their first 16 games expectations shot through the roof. Fans and many in the media saw them as the 2008 Atlanta Hawks, a young team on the rise, ready to make the playoffs and challenge some of the NBA’s elite. Perhaps a better comparison would have been the 2007 Atlanta Hawks, a 30 win team still putting the pieces together and figuring out how to use their talent to win games.

Still, the Pacers are lucky to be playing in a dilapidated Eastern Conference where even a collapse of this magnitude hasn’t eliminated them from playoff contention. As of February 1^st they trailed the Charlotte Bobcats by just one game for the 8^th and final playoff spot. They trailed the Philadelphia 76ers by just two games for the 7^th playoff spot.

The Pacers had a terrific October and November but those two months weren’t a true reflection of the status of the team. They had a horrific December and January but those two months weren’t a true reflection of the team either. The truth lies somewhere in between.

The playoffs aren’t out of reach and the path for new Head Coach, Frank Vogel should be clear. Clean up the playing rotations, slashing minutes for those negative contributors and find a way to get Hibbert playing the way he was early in the season. One game does not a season make, but a 104-93 win over Toronto in Vogel’s first game Monday night, along with a 24 point, 11 rebound performance by Roy Hibbert leads me to believe he’s thinking the same thing I am.

- Ian Levy

Some time ago, Andres (Dre) Alvarez – of NerdNumbers and the creator of the Automated Wins Produced page – asked me about the history of the NBA Efficiency measure.  As Dre noted on Sunday (during our podcast) – when this issue came up again — the NBA Efficiency measure is the primary statistical summary measure at NBA.com.  Yet where this came from is somewhat of a mystery.  So by request, I am going to discuss what I know of the origins of NBA Efficiency (and if anyone knows any more behind this story, please let us know). 

Back in the early 1990s (maybe 1993?) I had my first exposure to sports economics.  Much of the research at the time examined baseball (this has changed dramatically in the past 20 years).  Given my interest in basketball, I decided to see if I could employ the data generated by basketball players to answer various questions in economics. 

Such research seemed to require a summary measure of basketball performance (similar to batting average, slugging average, linear weights, etc… in baseball). So I started searching for a similar measure in basketball.

This search turned up two potential – and similar – candidates.  Dave Heeran offered the TENDEX model.

TENDEX = {[(PTS + REB + AST + BLK + STL – TOV – All Missed Shots)/minutes played]/game pace}*minutes played

In the introduction to the 1994-95 Basketball Abstract (there were four editions before this book appeared), it’s noted that “…Heeran invented his TENDEX system for evaluating players 35 years ago.”  So that means this model was developed around 1960. 

The TENDEX model is quite similar to Robert Bellotti’s Points Created measure.  The simple version of this measure (a more complex version adjusts for the average number of points scored per possession) – as detailed in The Points Created Basketball Book of 1991-92 – is as follows:

Points Created = PTS + REB + AST + BLK + STL – Missed Shots – TOV – PF/2

Obviously Points Created is quite similar to TENDEX.  And both are quite similar to NBA Efficiency.

NBA Efficiency = PTS + REB + AST + BLK + STL – TOV – All Missed Shots

Given these similarities, I would argue that NBA Efficiency has its origins in the work of Dave Heeran.  And that means it goes back about 50 years.

At first glance it would seem these measures are too simplistic to be of much value.  Bellotti, though, offered this defense of his Points Created model:

“Points Created is accurate.  … This contention is borne out by facts.  In the past eight years, the NBA’s Most Valuable Player has finished either first or secon that season in my Points Created rankings.  In the past 14 years, the MVP has finished first or second 12 times in Points Created.  In the other two years, the MVP finished third and fourth in Points Created, and in both years, the margin between the top three or four players was small.”

The MVP award is decided by members of the media, so explaining this vote may not prove the accuracy of the model.  One should note, though, that one can connect other player evaluations to the NBA Efficiency class of models and the story is essentially the same.  Whether we look at salaries (decided by NBA general managers) or voting for the All-Rookie team (decided by the NBA coaches), the NBA Efficiency model does a great job of explaining the evaluations we observe by NBA decision-makers.

Still, it is a simple metric.  And people tend to be more impressed by complexity.  So in recent years we have John Hollinger’s Player Efficiency Rating.  This metric is much more complex than NBA Efficiency, Points Created, or TENDEX.  But as noted in the Wages of Wins Journal FAQ page, this complexity doesn’t really change much of the story.  Hollinger offers a simple measure of PER called Game Score. 

Game Score = PTS + 0.4 * FGM – 0.7 * FGA – 0.4*(FTA – FTM) + 0.7 * ORB + 0.3 * DRB + STL + 0.7 * AST + 0.7 * BLK – 0.4 * PF – TO

As noted on the FAQ page, “for the 2008-09 season, PER and Game Score per 48 minutes for the 445 NBA players employed had a 0.99 correlation.”

And although Game Score doesn’t look like NBA Efficiency, these measures are also quite similar.  Again, as noted on the FAQ page, “for the 2008-09 season there was a 0.99 correlation between a player’s NBA Efficiency and Game Score.”

Why are these measures so similar?  Yes, this issue is also addressed on the FAQ page:

These measures all align because each tells a similar story about player scoring.  For example, imagine a player who takes twelve shots from two-point range.  If he makes four shots, his NBA Efficiency will rise by eight.  The eight misses, though, will cause his value to decline by eight. So a player breaks-even with respect to NBA Efficiency by converting on 33% of his shots from two-point range.  From three-point range, a player only needs to makes 25% of his shots to break-even.

Most NBA players can exceed these thresholds.  Therefore, the more shots most NBA players take the higher will be his NBA Efficiency total.  As a consequence, players who take a large number of shots tend to dominate the player rankings produced by this measure.

For Game Score the same problem exists, only the problem is a bit worse.  The break-even point on two-point shots for Game Score is 29.2%.  From three-point range a player breaks-even if he hits on 20.6% of his shots.  If a player surpasses these break-even points – and again, most players can do this – then the more shots he takes the higher will be his value.

Because these measures reward a player for just taking shots, they don’t tend to explain wins very well.  A team’s NBA Efficiency only explains 32% of the variation in team wins.  A team’s Game Score and PER explains 31% and 33% of the variation in win respectively.  One might note, though, that these measures don’t include the team defensive adjustment employed in the calculation of Wins Produced.  Unfortunately, if you add the team defensive adjustment to NBA Efficiency, Game Score, and PERs, explanatory power only rises to 58%, 60%, and 56% respectively.

One can go one step further and allow the individual components of the team defensive adjustment (detailed in Berri (2008) and employed in the calculation of Wins Produced) to vary. Such a step does raise the explanatory power of PERs to 82%. Wins Produced, though, explains 95% of wins, so even with the team defensive adjustments components added, the more popular measures come up short.

One should note that PERs –by itself – 0nly explains about 33% of team wins.  If you add in all the defensive variables – and you let the coefficients take on any value – you can raise the explanatory power to 82%.  But then, it is the team defensive factors that are offering the bulk of your explanatory power.   So what you learn about individual players from PERs is still not helping much.  Finally – as noted – even if you let the team defensive variables take on any value, you still can’t match the explanatory power of Wins Produced.

Let me summarize what we know about these measures:

• The efficiency metrics seem to derive from the work of Dave Heeran, and that means these metrics go back about 50 years.
• The story told by TENDEX, Points Created, NBA Efficiency, and the Player Efficiency Rating is quite similar.  Although these metrics look different, the measures are highly correlated.
• These measures – as Bellotti notes – do a wonderful job of explaining player evaluation. So if this is your objective – and I have published work with co-authors (a paper with Tony Krautmann and Peter von Allmen looking at monopsonistic exploitation in sports is a good example) that have used NBA Efficiency – then these measures are quite useful.
• These measures, though, over-value inefficient scoring. 
• As a consequence, these measures are not a very good measure of a player’s actual productivity (i.e. actual contribution to team wins).  A point we can clearly see when we look at how well these measures actually explain wins in the NBA.

And that means, the search had to continue to find a metric that captured an NBA player’s performance on the court.  That search led to an article published with Stacey Brook examining trades in the NBA in 1999 (originally presented in 1997).  The model presented with Stacey was further revised for a paper I published in Managerial and Decision Economics in 1999.  That model was then revised for a paper published with Tony Krautmann in 2006 (which appeared in Economic Inquiry). And that model was modified for the Wins Produced model presented in The Wages of Wins (and yet another paper published in 2008).

After all this history, what will we see in the future?  Wins Produced – as noted – explains more of wins than any of the NBA Efficiency family of metrics.  So will the Efficiency metrics – after 50 years – start fading from use? 

No, these measures are still consistent with popular perception.  And I just don’t think popular perception – which focuses on scorers – is going to change any time soon.  So if you fear Wins Produced is going to take over the NBA… well, I don’t think you have to worry.  And if you want people to pay more attention to players like Landry Fields and less attention to Andrea Bargnani…. well, you are probably going to be disappointed.

Let me close by emphasizing that the Wins Produced metric was created because a measure of how a player contributes to wins seems necessary to address various issues important to economists (at least, important to this economist).  It was not created in an effort to change how people view basketball (although if it does this, I am okay with that) or in an effort to change how NBA teams make decisions (although if it does this, I am okay with that also).  Again this metric was designed to further research in economics.  And for the reasons stated above, the efficiency measures – and one might add, the plus-minus measures (for reasons stated in the FAQ page) – are not as helpful because they do not appear to be good representations of the productivity of individual players. 

- DJ