Quantifying Growth in an NBA Player

In the last post, we generated a Price/Earnings (P/E) ratio on player contracts – a relationship between the production a given player might provide his team, and the cost (viewed as a multiple of that production) involved in obtaining that performance.  In this construct, we were able to determine player value – whether or not a player is reasonably priced, over-priced, or a relative bargain – much in the manner that investors determine the value of a stock’s share in relation to its earnings.

In this week’s post, we are looking to borrow from another Wall Street paradigm – the PEG ratio, or Price/Earnings Growth ratio.   To make such an analysis more accessible, I will start with a glossary of statistical terms, which can be referred to by the reader:

SPR = [2PFGM + 1.5(3PFGM) + (FTM/2) + AST]/[FGA + (FTA/2) + AST + TOV]

TAPPS = PTS/[FGA + (FTA/2) + TOV]

TOT = TOV/[TOV + FGA + (FTA/2) + TRB + STL + AST]

SSI = FTA/FGA

SAR = [FGA + (FTA/2)]/AST

3PR = 3PFGA/FGA

3PS = 3PFGM/FGM

E = SPR + TAPPS + (1 – TOT)

wCE = (MPG/48) x [SPR + TAPPS + (1 – TOT)]

P/E = Salary/[SPR + TAPPS + (1 – TOT)]

wP/E = Salary/(MPG/48) x [SPR + TAPPS + (1 – TOT)]

In a PEG ratio analysis, a stock’s P/E ratio is divided by the growth it demonstrated in the previous year:  PEG = (P/E)/(growth%).  In this manner, a company whose stock share costs $10 and earns $1 per share has a P/E of 10.  Additionally, if the same company demonstrated 5% growth in the preceding year, its PEG would be 2 (10/5).

For NBA players, we defined unweighted P/E and weighted P/E (wP/E) with the formulas listed above.  Similarly, Earnings (E) and weighted Cumulative Earnings (wCE) are defined with the formulas listed above.  Using a measure similar to that of corporate earnings growth, unweighted Earnings Growth (EG) is now defined as:

EG = (Present Year’s E – Previous Year’s E)/(Previous Year’s E)

And weighted Earnings Growth (wEG) is defined as:

wEG = (Present Year’s wCE – Previous Year’s wCE)/(Previous Year’s wCE)

In sum, the formulas for unweighted PEG and weighted PEG (wPEG) are thus:

PEG = (P/E)/EG

wPEG = (wP/E)/wEG

I initially decided to try out the new formulas (EG, wEG, PEG, wPEG) on players who are at relatively early stages in their careers (second or third year, for the most part), since older players are more likely to already have long-term, guaranteed contracts.  Also, older players are generally at a point in their careers where they are compensated for what they do (or, all too often, what they have done), rather than for what they will do.  The ten players I chose to evaluate were Derrick Rose, Marc Gasol, Danilo Gallinari, Stephen Curry, Darren Collison, Eric Gordon, Brandon Jennings, Brook Lopez, Russell Westbrook, and Tyreke Evans (all players who have yet to test free agency).

As a means of explanation, I will begin with a comparison of Eric Gordon of the Los Angeles Clippers and Tyreke Evans of the Sacramento Kings, as each player has similar styles that nevertheless demonstrate very different growth patterns.  First let’s look at their traditional statistics over their last two seasons:

                                    PPG      AST      REB      FG%     FT%

Gordon, ’09-’10           16.9     3.0       2.6       .449     .748

Gordon, ’10-’11           23.7     4.4       3.0       .465     .821

Evans, ’09-’10              20.1     5.8       5.3       .458     .748

Evans, ’10-’11              18.3     5.5       4.9       .411     .763

As the traditional statistics suggest, Gordon has shown tremendous improvement from last season to the current one, and Evans seems to have taken a step backwards.  Furthermore, the traditional stats suggest that in ’09-’10, Evans was the better player by a fairly wide margin, whereas this year Gordon appears to edge out Evans.  What the traditional statistics do not tell us, however, is how significant these changes really are.  For example, is Gordon really 40% better this year than last, since his scoring average this year has increased by 40%?  And was Gordon really half the player that Evans was in ’09-’10, since he was only able to produce half as many assists and rebounds?  Both claims seem equally implausible, and we will address this later in the post, when we analyze each player’s cumulative earnings. 

Let’s now take a comparison of the same two players in the same two years, but this time we will use the alternative, Basketball I.Q. statistics:

                                    SPR      TAPPS    TOT

Gordon, ’09-’10           .561     .970       .096

Gordon, ’10-’11           .588     1.021     .081

Evans, ’09-’10              .562     .899       .086

Evans, ’10-’11              .522     .811       .095

The alternative statistics suggest that Gordon truly is improving, and Evans’ sophomore campaign indeed represents a decline.  Gordon’s Successful Possession Rate (SPR) last season, which was pretty good for a backcourt player, has begun to approach All-Star levels.  Meanwhile, his Turnover-Adjusted Points per Shot (TAPPS) started off as extremely impressive, but now has him at a level seen by the very best of low-post players, who take nothing but high percentage shots (this is probably the result of Gordon’s significantly improved free throw shooting, as well as his generally improved shooting from the floor).  Gordon is even taking much better care of the ball, as his Turnovers per Touch (TOT) have declined tremendously.

Unfortunately, you can say just about everything has gone in the opposite direction for Evans: his SPR has declined to a rate that is quite low for a backcourt player; his TAPPS is dwelling in the region of many marginal players; and he is not taking as good care of the ball this year as he did last.

But what might be even more revealing about the two players’ alternative statistics is that they challenge one of the conclusions that the traditional statistics would have you believe, which is that Evans was the better player last year.  Yes, Evans scored more, tallied more assists, and collected more rebounds – but these statistics probably reflect the fact that Evans simply demanded the ball more than Gordon did.  In reality, Gordon was clearly the more efficient player last season:  Gordon’s SPR was virtually the same as Evans’; Gordon’s TAPPS was significantly higher than Evans’; and Evans did, indeed, take better care of the ball.  The sum of these differences – and the growth from year to year – can be best explained by a comparison of the two players’ Earnings (E), weighted Cumulative Earnings (wCE), Earnings Growth (EG), and weighted Earnings Growth (wEG):

                                    E          wCE     EG          wEG

Gordon, ’09-’10           2.44     1.83     --            --

Gordon, ’10-’11           2.53     1.99     3.69%    8.74%

Evans, ’09-’10              2.38     1.84     --            --

Evans, ’10-’11              2.24     1.76     (5.88%)  (6.52%)

These measures – all derived from the previous statistics we have discussed – shed quite a bit of light on the comparison between Gordon and Evans.  Last year, Evans, the ballyhooed Rookie of the Year in the NBA, had less unweighted earnings than Gordon did, though the wCE (which takes playing time into account) was virtually identical between the two players.  This year, however, Evans has had a 5.88% decrease in his unweighted earnings, while his weighted earnings fell even more, 6.52%, as the result of his slightly diminished playing time.  Meanwhile, Gordon has grown impressively – 3.69% in EG and 8.74% in wEG (which reflects his significant increase in playing time). 

And these numbers seem to make more intuitive sense, in terms of their scale – Gordon probably really is about 8% better than he was last year (as opposed to the 40% his scoring average would indicate), whereas Evans probably has declined at about a 5-6% rate.  Furthermore, Evans and Gordon were equally good players last year (as opposed to Evans being twice as good as Gordon), whereas this year Gordon is about 13% better than Evans is (a significant difference).  There may be logical explanations for these statistics – Gordon is now benefiting from the presence of Blake Griffin, opponents have caught on to the fact that if you can stop Evans you can stop all of Sacramento, etc. – but this seems to be what the numbers between the numbers say.

It was at this point in the post that I intended on discussing PEG and wPEG – in other words, taking the players’ salaries and dividing them by the growth in their earnings.  But as I was about to do this, something occurred to me:  Though players have value, and no one with an eye on the salary cap wants to overpay for that value, absolutely no team would take on a player just because he might grow from being marginal to mediocre.  That strategy might make sense for a stock trader, who benefits from appreciation, real or perceived, of just about any kind – but for a professional basketball general manager, growth is useless if it does not represent absolute excellence. 

Furthermore, since you cannot sell a player back to anybody for a higher price than you paid – alas, there are no transfer fees in American sports, as there are in European soccer (perhaps FIFA should come up with its own PEG), and NBA basketball players are not true commodities on an open exchange – you can’t simply move a player from, say, the Wizards to the Rockets, and then collect on the difference in that player’s value from when you obtained him to when you dealt him.  And so I discarded the PEG (and the wPEG) before I ever calculated its value on a single player.  Unlike P/E and wP/E, which might help you determine a fair salary for a given player, the PEG does not give you information that is in any way practical or actionable.

But the growth statistics, as illustrated by the example of Gordon and Evans, appear to be very useful – at least for young players who have not yet hit free agency.  It shows you who is getting better and by how much, and, in a world in which a multi-year contract might be structured to have annually increasing salaries, it can guide you in determining the slope of those pay escalations.  Furthermore, for older players, the growth statistics (EG and wEG) might tell you when the plateau of his career has begun, and when he has begun to descend the other side of the mountain.  Last, a comparison of composite earnings – here referred to as E and wCE – appears to be a far better measure of player performance than traditional statistics.

Below is a table of the alternative Basketball I.Q. statistics, including earnings and growth, on eight other young players who will be re-negotiating their contracts in the next couple years.  Traditional statistics are published in multiple other venues, and so have been excluded for the purpose of brevity.  The first line for each player represents the ’09-’10 season, and the second line represents the current ’10-’11 season.

                        SPR      TAPPS  TOT     E          wCE     EG%     wEG%

Rose                .574     .920     .092     2.40     1.84     --          --

Rose                .590     .929     .087     2.43     1.92     1.25     4.34

M. Gasol          .587     1.035   .073     2.55     1.90     --          --

M. Gasol          .564     .939     .088     2.42     1.67     (5.10)   (12.11)

Gallinari          .562     1.026   .061     2.53     1.79     --          --

Gallinari          .584     1.073   .055     2.60     1.89     2.77     5.59

S. Curry           .598     .941     .099     2.44     1.84     --          --

S. Curry           .618     .993     .100     2.51     1.75     2.87     (4.89)

Collison           .599     .878     .113     2.36     1.37     --          --

Collison           .574     .868     .098     2.34     1.48     (0.85)   8.03

Jennings          .546     .818     .083     2.28     1.55     --          --

Jennings          .537     .832     .078     2.29     1.58     0.44     1.94

B. Lopez           .539     .971     .080     2.43     1.87     --          --

B. Lopez           .503     .937     .083     2.36     1.69     (2.88)   (9.63)

Westbrook       .573     .807     .096     2.28     1.63     --          --

Westbrook       .585     .887     .095     2.38     1.76     4.39     7.98

A review of the above statistics allows for a few, brief conclusions:

Marc Gasol is not a growth prospect.  Besides being the oldest and most experienced player on this list, Gasol has demonstrated the greatest decline this season among this focus of players.  To be fair, Gasol had an excellent 2009-10 campaign, and this season might simply reflect a return to earth.  But when a player’s wEG is smaller (or, in this case, more negative) than unweighted EG, it means that the player is getting less playing time than he did the previous year – which is an important statement about what your coach thinks about you.  Gasol becomes a restricted free agent after this season, but I would be wary of offering him too much, lest he emerge as the second coming of Jon Koncak.

The Brandon Jennings hysteria of 2009-10 should be put on hold.  Jennings’ EG in the last year is essentially flat, and the slight increase in wEG is a reflection of increased playing time, rather than improved play when he is on the court.  A review of his last two seasons’ statistics demonstrates that he is essentially the same player both years.  There are many Knicks fans, of course, bemoaning the fact that Jennings was taken just after the Knicks took Jordan Hill in the 2009 draft.  Though Jennings is almost certainly better than Hill, it should be noted that Hill begat the expiring contract of Tracy McGrady; which begat the cap space that created Raymond Felton; who begat two years of a more-than-serviceable Chauncey Billups; whose retirement will beget either Deron Williams or Chris Paul.  And who would you rather have – Chris Paul/Deron Williams, or Brandon Jennings?

Derrick Rose is darn good, and getting better – but look out for Stephen Curry.  By the alternative statistics, Derrick Rose was a great player last year, and even better this year – a perception confirmed by his traditional statistics, and bolstered by the media speculation of his MVP candidacy.  He appears deserving of the praise.  But look at Curry:  With the exception of his TOT, all of his alternative statistical parameters were superior to those of Rose (wCE was even for both players last season, and Rose gets the edge this year, but that is only because Rose plays more minutes).  And not only are his statistics better than Rose’s, but he is demonstrating greater growth (the decrease in Curry’s wEG is entirely due to the fact that his minutes are down almost 7% this year; last year his minutes benefited from the absence of Monta Ellis, who missed 18 games – but when Ellis plays, he has led the league in minutes per game both last year and this).  It is equally impressive that Ellis’ return to full strength this year has not had an adverse effect on Curry’s game – despite the fact that Ellis and Curry are similar players, Curry has gotten better with a full season of Ellis, which I think tells you something about how unselfish Curry’s game is.  Golden State management should take notice: if they ever have to choose between Ellis and Curry, Ellis may be appropriately valued, but Curry (not yet eligible for free agency) is under-valued.          

The Knicks gave up a lot when they traded Danilo Gallinari.  Look at the numbers.  Absolutely everything he does got better from last season to this – and he was pretty good last season.  He has demonstrated growth in EG and wEG – which means that his performance improved even with increased minutes.  He is still on the steep end of the learning curve.  And Denver has fewer super-models than Manhattan, and so look for this trend to sustain itself. 

Darren Collison is a good example of how increased minutes can skew statistics.  I like Darren Collison – I think he is a pretty good player.  He filled in ably for Chris Paul last season, when Paul was injured, which is why he was a coveted player in the off-season and ended up at Indiana.  He scores more than last year, he rebounds more than last year – but he also plays more than last year.  If you look at his alternative statistics, he is slightly worse this year than last.  His EG is a little bit negative, but close enough to zero that we can call it “flat.”  But his wEG is way up – all due to a 7% increase in playing time.  Collison is who he is – which is pretty much the same player as last year, who just spends a little more time on the court this year.

Russell Westbrook is a good, yet over-rated, player – but he is getting better.  There is very little that Westbrook does that is as good as two of his peers, Rose and Curry.  He takes slightly better care of the ball than Curry, and his wCE is essentially equal to Curry’s (as a result, again, of increased playing time).  There is nothing that he does as well as Rose.  Still, Westbrook is very good, very young, and very adaptive – he has the most impressive EG on this list, and his wEG is right near the top.

Brook Lopez is not exactly Bill Walton.  Unless he breaks his foot and moves to San Diego.

Value Investing: Player Contracts at the Dawn of a Hard Salary Cap

This week, we will identify the concept of value in a professional basketball player – in other words, what a player’s skills offer, taken in the context of what that player costs.  To make such an analysis more accessible, I will start with a glossary of statistical terms, which can be referred to by the reader:

SPR = [2PFGM + 1.5(3PFGM) + (FTM/2) + AST]/[FGA + (FTA/2) + AST + TOV]

TAPPS = PTS/[FGA + (FTA/2) + TOV]

TOT = TOV/[TOV + FGA + (FTA/2) + TRB + STL + AST]

SSI = FTA/FGA

SAR = [FGA + (FTA/2)]/AST

3PR = 3PFGA/FGA

3PS = 3PFGM/FGM

It is fun to imagine what we might do if given unlimited resources: buy that Gulfstream; vacation homes in Telluride and Tuscany; fix the New York Mets; et cetera, et cetera.  But the problem with a game like this is that it is too easy.  Provided an infinite cash flow, there isn’t anything we might not do, assuming such a thing does not live outside our moral code: If the money just keeps coming, and I do not find the objects within my sights reprehensible, then over time I would acquire just about anything.  There is no need for discretion when you have a bottomless wallet, at least not fiscally.

But if we live on a budget – and everybody, even the very wealthy, lives on a budget – we must purchase things based on the value they bring us:  The Gulfstream, for example, is not going to bring us joy if we cannot pay to fill it with gasoline, and the vacation home in Telluride is not worth much if its title leaves us with no money to buy a lift ticket or a mountain bike.

With the current collective bargaining agreement between the NBA and its players’ union about to expire, the concept of value in the context of player contracts has grown in importance.  The current rules governing team payrolls, which include a “soft” salary cap, place significant emphasis on contract value:  Since there is a limit, though flexible, to the amount of money a team can spend on payroll, any dollar given to Player A is one less dollar you might be able to offer to Player B, and so teams must be careful – if you pay too much for the Gulfstream, you might not have enough money left over to hire a pilot who can fly it (please refer to the New York Knicks from 2000 to 2010, who never even bought a Gulfstream, but overpaid for a used Suburban and a couple two-cylinder Yugos).

In the next collective bargaining agreement between the league and its union, it appears that the cap on team salaries may be lower, and it will almost certainly be a “hard” cap – in other words, there will be no flexibility that allows a team to exceed that cap limit, even by a temporary pittance.  If the NBA indeed implements such a system, some teams that have so shrewdly managed the current rules – for example, the Los Angeles Lakers of the last four years – will have a difficult time competing with their current model, since their discretionary spending depends so much on the flexibility of exceeding the cap.

And so teams will have an even greater internal mandate to seek out value – not talent at any cost, but relative talent at relative cost.  In the world of equity trading (i.e., stock trading), the concept of a stock’s value is often referred to in terms of its Price/Earnings Ratio (P/E) – how much does the stock cost in comparison to the profits that its company earns.  In this manner, Company X may be one-tenth as profitable as, let’s say, Apple – but if one share of stock in Company X costs only one-twentieth the price of a share in Apple, then Company X is the better value (it is, by this measure, twice as valuable).

With this investment paradigm in mind, I wanted to come up with a P/E rating for professional basketball players – a ratio of their salaries in comparison to their “earnings” on the court.  I figured that if you could monetize the relative value of professional basketball players, then you would be better positioned to spend more wisely for them when you are on a fixed budget – or, in this case, a hard salary cap.

As in a stock’s P/E ratio, the numerator of the formula was pretty easy to determine:  The player’s salary for the current year steps in and takes the place of the price of one share of a company’s stock.  But a player’s “earnings” was a little harder to determine:  What would be the analogue for the profits a single share of stock earns in a given year?

I decided to combine three offensive measures that, taken together, estimate what a player earns on the court:  the Successful Possession Rate (SPR), the Turnover-Adjusted Points per Shot (TAPPS), and the Turnovers per Touch (TOT).  Since the TOT is the only statistic of the three in which a lower number correlates with excellence, for purpose of “earnings,” the TOT was subtracted from 1 (i.e., 1 – TOT).  And so the formula for earnings (E) was determined to be:

E = SPR + TAPPS + (1 – TOT)

And so a player’s P/E was determined to be:

P/E = Salary/[SPR + TAPPS + (1 – TOT)]

As I write this post, the NBA is celebrating its All-Star weekend – replete with the distractions of its upcoming negotiations with its union, and speculation on where All-Star Denver Nugget Carmelo Anthony might end up.  With this as a backdrop, I have decided to determine the value – the P/E ratios – of 11 of this weekend’s All-Stars: Dwyane Wade, Kevin Love, Kobe Bryant, Anthony, Rajon Rondo, Amare Stoudemire, Dwight Howard, Derrick Rose, Lebron James, Kevin Durant, and Chris Paul.  In addition, I have chosen to look at four players who participated in the weekend’s Rookies-Sophomores game – Landry Fields, John Wall, James Harden, and DeJuan Blair – plus one former All-Star who is still young, still mentioned in high-profile trade talks, and is probably the best player on one of the league’s lesser teams (Devin Harris).

We will start with a table of each player’s SPR, TAPPS, TOT and cumulative statistical earnings for the 2010-2011 season:

                        SPR      TAPPS     TOT    Earnings

Wade               .567     .987        .084    2.47

Love                 .570     1.036      .057    2.55

Bryant             .562     .961        .082    2.44

Anthony           .523     .956        .075    2.40

Rondo              .666     .761        .113    2.31

Stoudemire     .533     .973        .089    2.42

Howard           .527     1.004      .087    2.44

Rose                .590     .929        .087    2.43

Harris              .612     .878        .104    2.39

Fields               .602     1.040      .070    2.57

Wall                 .583     .780        .108    2.26

James              .596     .972        .087    2.48

Harden            .583     1.004      .073    2.51

Durant             .561     1.034      .080    2.52

Blair                .489     .865        .077    2.28

Paul                 .687     1.005      .075    2.62

As you can tell from the highlighted figures, there are five players among these 16 who have earnings values above 2.50:  Kevin Love, Landry Fields, James Harden, Kevin Durant, and Chris Paul (with an astronomical 2.62).  From this very small sample, one can also infer that All-Star quality is associated with an earnings value in the range of 2.40, and a solid performer earns a value of about 2.30 and above.

The immediate issue that comes to mind, however – particularly in light of Fields and Harden being included among the very best – is that the cumulative earnings statistic is derived from ratio values:  SPR, TAPPS and TOT are all averages of what a player does, and not absolute values of specific achievements.  In this manner, the earnings statistic does not give adequate weight to the time a player spends on the court:  Fields, who averages only 32.6 minutes played per game, has much higher earnings than his teammate, Stoudemire, who averages 36.8 minutes; and Harden, who plays only 25.7 minutes per game, is virtually deadlocked with his teammate, Durant, who plays a remarkable 39.6.  Two hamburger chains may sell the exact same number of burgers per minute, with the same relative overhead, but if one chain keeps its restaurants open 24 hours per day, and the other one is only open 12 . . . well, you can see the potential difference.

So it became clear that earnings, if expressed as a performance ratio, must be weighted by time spent in operation.  I figured that an adequate manner in which to weight performance would be playing time – a player’s average amount of minutes played per game (MPG), divided by 48 (the total number of minutes played in a game).  If this co-factor were multiplied by earnings, you would get a more accurate reflection of game-time performance – something I have referred to as the Weighted Cumulative Earnings (wCE):

wCE = (MPG/48) x [SPR + TAPPS + (1 – TOT)]

With this new, weighted measure, every player’s earnings changes in relation to one another:

                        MPG    Earn.    wCE

Wade               37.1     2.47     1.91

Love                 36.8     2.55     1.96

Bryant             33.9     2.44     1.72

Anthony           35.5     2.40     1.78

Rondo              37.7     2.31     1.81

Stoudemire     36.8     2.42     1.86

Howard           36.7     2.44     1.87

Rose                38.0     2.43     1.92

Harris              31.9     2.39     1.59

Fields               32.6     2.57     1.75

Wall                 36.9     2.26     1.74

James              38.3     2.48     1.98

Harden            25.7     2.51     1.34

Durant             39.6     2.52     2.08

Blair                22.2     2.28     1.06

Paul                 35.7     2.62     1.95

It is remarkable what weighting for playing time does to the interpretation of statistics:  Landry Fields, for instance, goes from being the second-best “earner” on this abbreviated list to the tenth.  LeBron James goes from number six all the way up to number two.  The six players who have a wCE of 1.90 or higher are:  Wade, Love, Rose, James, Durant, and Paul.  James Harden, who is a role player, is nowhere near the top of this list – in fact, he is near the bottom.  Rajon Rondo, whose un-weighted earnings were decent, ascends to near the top of the list – ahead of Kobe and Carmelo – on the basis of his significant playing time.

It appears that the wCE statistic rewards well-conditioned players who stay out of foul trouble and are valued by their coaches – all good things.  On the other side, an aging player with balky joints (Kobe), or one who fouls a lot (Howard), have a decreased wCE.  Players like James and, especially, Durant – who do things well, and do things all game long – are absolutely top-earners.  You can also see how a player whose unweighted earnings are so much better than another’s – Fields over Wall, for example – might have his advantage diminish when playing time is accounted.  Because playing time is a variable that can be manipulated by coaches for a variety of reasons, it is probably best, statistically, to evaluate both a player’s unweighted earnings (E) and wCE.

Likewise, a player’s P/E should probably be expressed in both an unweighted fashion, as well as a weighted fashion (wP/E):

                        Salary($mm)     P/E                 wP/E

Wade               14.0                   5.67               7.33

Love                 3.6                     1.41               1.84

Bryant             24.8                   10.16             14.42

Anthony           17.1                   7.13               9.61

Rondo              9.0                     3.90               4.97

Stoudemire     16.5                   6.82               8.87

Howard           16.5                   6.76               8.82

Rose                5.5                     2.26               2.86

Harris              9.0                     3.77               5.66

Fields               0.5                     0.19               0.29

Wall                 5.1                     2.26               2.93

James              14.5                   5.85               7.32

Harden             4.3                     1.71               3.21

Durant             6.1                      2.42              2.93

Blair                0.9                      0.39              0.85

Paul                 14.9                  5.69               7.64

A few things immediately come to mind: 1) If Kobe were a stock in your pension fund, he is an absolute “sell”; 2) rookie contracts, and contracts that were negotiated prior to a player becoming eligible for unrestricted free agency, have significant value, and thus should be considered to have commensurate trade value (are you listening, James Dolan?); 3) greatness in an unrestricted free agent seems to deserve a wP/E of around 7.50 (Wade, James, Paul, e.g.), while the limits of value seem to be pushed at about 9.00 (Stoudemire, Howard, Anthony at his current contract, e.g.).

Some other thoughts:

Kevin Love provides remarkable value at his current salary.  If he maintains his current statistics and is kept to a maximum wP/E of 7.0, his maximum contract value would come to $13.7mm per year.

If young players perform better as their salaries increase, this would bode quite well for John Wall.  Unfortunately, I don’t think you play better just because you get paid more.  That said, dollar for dollar, Wall gives you a value-adjusted performance that is remarkably similar to that of Derrick Rose, who has a couple years experience on him (and a much better team around him).

Carmelo Anthony, at his current salary, is already over-valued.  If his salary is increased to the $22mm that he wants, his wP/E increases to 12.34, which is an absolute cap-busting franchise-wrecker – and almost double what James, Wade and Paul currently earn in regards to their value.

What should we pay for valuable role players?  It looks like a very valuable team player with some limitations to his game (e.g., Rajon Rondo) is worth a wP/E of about 5.0.  At his current earnings, Rondo is appropriately paid.  So, too, is Devin Harris.  As future valuable role players, Landry Fields should probably have his salary capped at $8.75mm (but only after he is free to negotiate); James Harden at $6.7mm; and DeJuan Blair at $5.3mm.

Next post: Evaluating the contracts of young players with a P/E growth index.