1. An Examination of NBA Player Productivity and
Salary
1. Introduction
The NBA is the top men’s professional basketball league in North America, and is widely
considered the top professional basketball league in the world. The NBA is able to attract the top
players in the world for two reasons. First of all, NBA players are the top paid sportsmen in the
world earning on average just under $4.2 million (Lahman, 2014). Secondly the success of the
United States national team, currently the top ranked team in the FIBA World Rankings and
reigning Olympic and FIBA World Cup gold medalists, shows that the United States produces
the top players in the world. The success of the league can be seen in its increasing value. The
average NBA franchise is worth $634 million, up 25% over last year. Collectively the 30
franchises are worth $19 billion (Badenhausen, 2014). A 2012 report estimated revenue at $5
billion, an increase of about 20 percent from the league’s previous full season in 2010-11
(Associated Press).
When the NBA was first formed not every team benefitted equally from money being
generated. Big market teams are able to generate more revenue because they are from cities that
are well-populated. That means these franchises have larger fan bases, which translates into more
TV viewers, higher TV ratings, and better recognition. Small market teams are the exact
opposite. Without even distribution of revenue the big market teams hold a clear advantage over
the small market teams. The result was the league’s current revenue-sharing system that is aimed
at redistributing wealth among its teams. The system moves money through a formula that shifts
a percentage of the financial wealth of big-market teams to the league’s neediest teams.
Although it does not completely close the financial gap between high-revenue and low-revenue
teams, it provides a more equal setting (Lombardo, 2012).
The NBA currently operates under a soft salary cap. A soft cap allows for exceptions to
be made when teams sign players or make trades that exceed the cap under certain conditions.
For the 2014/15 season the salary cap, as reported by the NBA, is $63.065 million, a 7.5%
increase over last year. Since it is only a soft cap, there also exists a luxury tax. The luxury tax is

2. mechanism that helps control team spending. It is paid by high spending teams, those with a
team salary exceeding a predetermined tax level, $76.829 million for the 2014/15 season. Teams
pay a penalty for each dollar their team salary exceeds the tax level (Coon, 2014). This
discourages organizations from spending over the tax level. Organizations must fill out a roster
of 12 active players, while keeping within the bounds of the cap. Not every player is paid equal
salary though. Each player signs a unique contract with their organization that specifies their
salary. This is a result of how each franchise values players. It should follow that players with
high levels of production would be better compensated for their work, as opposed to a player
who is producing at lower levels. It is unclear as to how valuable production in specific aspects
of the game are to each organization.
Using factors of production that are statically measured in the NBA and a regression
analysis, I first aim to identify the effect the set of production statistics has on a player’s salary.
That is to say, does a player’s salary reflect his production? Given the results of the regression
analysis it will be possible to see what the NBA, as a whole, values in terms of skill set and
playing style. Secondly, by applying my final estimation equation to each individual player’s set
of statistics, I will be able to generate a salary that reflects the player’s production. By comparing
a player’s real salary with his generated one, I can come to a conclusion as to who the most
overpaid, underpaid, and accurately paid players are in the league.
2. Literature Review
Research on the topic of player performance and salary originates from Scully (1974). He
was able to demonstrate that a professional sports organization that wants to maximize its profits
will try to select a combination of player skills and non-player inputs that set a player’s salary
equal to the marginal financial value of his contribution to the team minus any tax that the team
is able to impose on the player. This theory suggests an estimating equation for NBA player
salaries that includes both individual player and team characteristics. Koch and Vander Hill
(1988) and Kahn and Sherer (1988) applied this to the NBA and came up with similar results as
to how a player’s salary can be determined. This theory suggests an estimating equation for NBA
player salaries that includes both individual player and team characteristics. They hypothesized
that a NBA player’s salary was a function of his performance, in both college and NBA, as well
as specific team and franchise characteristics. This early work came to a number of conclusions

3. regarding production and salary. Teams create expectations for their players when they are
deciding a salary. A player who is a high draft pick will be expected to produce at higher levels.
This is a rough measure of the quality of human capital that the NBA feels the player has brought
to the league. The results also indicate that scoring, minutes played, and position, specifically
being a center, are positive indicators of a player’s salary. Most significant of these was points,
which shows the value of an offensive skill set.
The empirical work of Berri, Brook, and Fenn (2011) and Coates and Oguntimein (2010)
expanded upon the earlier work. Using similar variables and statistics on production, accounting
for both college and NBA performance, they further confirmed the aforementioned results.
Which was that players who score at higher levels are more likely to receive a higher contract. In
addition to that, their results suggest that players who accumulate steals and rebounds will not
see their efforts pay off.
At some point in a player’s professional career, his performance in college cannot be
used as an accurate measure of future production. Players continue to develop even as they play
in the NBA. This development may be positive or negative, but after a certain time in the NBA, a
player is no longer like his college self. Removing variables associated with college results in
measuring salary through player characteristics and NBA performance. Even in the absence of
these variables the results confirm the early findings on production and salary. Scoring has a
significant effect on a player’s salary (Bodvarsson and Brastow, 1998; Dey, 1997; Hofler and
Payne, 2006). Though the work of Berri (1999) is similar in design and data, it came to the
conclusion that a player’s ability to acquire and maintain possession is the most important factor
for winning. This would suggest that rebounding, steals, and turnovers are significant factors,
which is contradictory to the results which include college performance.
Since there have been few developments in the advancement of NBA statistics, much of
the data used in my research is the same as previous works. The estimating equation considered
below includes NBA performance statistics like points, rebounds, assists, as well as data on
player efficiency and demographics. Performance statistics from college are not included since
this research is using cross-sectional data. Similarly to previous literature, a linear regression is
being used to analyze the data. Through this method, this research will hope to produce an
accurate projection of each player’s salary.

4. 3. Data and Methods
Player productivity, and efficiency data along with positon and age, as of the 2013/14
season, was extracted from the dataset compiled by Sean Lahman of basketball-reference.com,
while data for player salaries was extracted from the dataset compiled by Sean Deeks of
ShamSports.com. The productivity statistics that were extracted are representative of a player’s
per game production. These statistics were transformed from per game to per minute played. Per
minute played statistics will more accurately reflect a player’s production, as it only accounts for
the time the player spent on the court, not the entire game. The available productivity data
resulted in a dataset of 477 players, excluding those who had been traded during the season. Out
of the 483 players with salary data, only the 443 players with salaries above $490,180, the CBA
minimum annual salary, were included. Salary data was transformed to be expressed in millions
of US dollars. Upon further analysis of the salary data, Kobe Bryant was eliminated from the
data set because his salary was $9 million more than the next highest. The two datasets were then
consolidated into one. This resulted in a complete dataset of 348 players with salary,
productivity, and demographic data.
A number of variables were compiled from the available productivity statistics: points,
assists, steals, blocks, rebounds, turnovers, free throw percentage, two-point field-goal
percentage, three-point field goal percentage, age, and position. Position was transformed into a
dummy variable, and is coded as “0” for non-guards and “1” for guards. From the salary data a
player on the last year of his contract was identified through a dummy variable, with “0”
representing a player on a non-expiring contract and “1” a player on an expiring contract.
Points, steals, blocks, rebounds, and percentages are all measures of a player’s
productivity, while turnovers can be considered a measure of how unproductive a player was. A
player’s age was included because of the process of player development. A young player is
expected to see increases in productivity the first few years as he develops and learns. Once a
player has reached middle age his productivity should have peaked, and should plateau for a few
years. As a player enters the twilight of his career a decrease in productivity can be expected
since he is physically no longer capable of doing the things he could do when he was younger.
Position is included as well because not every team equally values each position, or values
certain productivity statistics more for a position.

5. In order to gain a better understanding of the data set, histograms of a number of
variables were generated. Figures 1-4 show the distribution of salary, points, assists, and
rebounds per game in the data set. The histograms were created using per game statistics
because, as descriptive statistic, per game is easier to understand and see the distribution of than
per minute statistics. Figure 1, the distribution of salary, shows that nearly three-fifths of the
dataset was paid $4.0 million or less, with two-thirds of those players making $2.0 million or
less. While the mean salary of the data set is $4.56 million. Figure 2 represents the distribution of
points per game in the league. It shows a wide distribution of scoring throughout the league. This
shows that scoring points is common skill. The average points per game in this data set is 9.0,
with the majority of players averaging between two and fourteen points per game. Figure 3
represents the frequency of assists per game. The distribution shows that attaining higher levels
of production in assists is not common. The average player in this data set averages 1.9 assists
per game, and the majority of player’s average less than 2.0 assists per game. Figure 4 represents
the distribution of rebounds per game. This figure is more comparable to points per game than it
is assists per game. A higher frequency of players are able to produce at higher levels of
rebounds per game, with the average player grabbing 3.9 rebounds per game.
Using these variables and dataset, an OLS estimation of regression coefficients will be
applied utilizing EViews statistical software. This research tests to see if salary reflects
production, so the dependent variable is salary, and variables which measure production,
efficiency and demographics are the independent variables. The resulting estimating equation
would be as follows:
Salary =  + 1Points + 2Assists +3Steals +4Rebounds +5Blocks +6Turnovers
+72-pointFG% +83-pointFG% +9FreeThrow% +10Age +11Position +12ExpiringContract +
t
I would expect points, assists, steals, rebounds, blocks, 2-pointFG%, 3-pointFG%, and
ExpiringContract to have positive signs. Higher levels of production indicate a more successful
player, who would garner a higher salary. I would also expect players in the last year of their
contracts to produce at a higher level because they are about to enter free agency, and are going
to be offered their market value. So producing higher levels of statistics in the final year would

6. give teams confidence to pay a player. I am expecting turnovers and age to have a negative
coefficients. A player with a high per game turnover rate will be detrimental to the team. The
older a player gets the less likely they are to perform at their previous high and more likely to see
their production drop, and would thus be not as deserving of a high contract.
The strengths of the data set lie in the productivity and efficiency data. The statistics
being used are common measures in the NBA. They are basic metrics, each specifically tracking
one production factor. This allows for analysis of a single aspect of the game as it applies to
salary. Besides blocks and steals, the data set is weak in productivity statistics that measure
defensive performance. More advanced statistics, like efficiency ratings and wins-above-
replacement (WAR) could be utilized to further explain productivity. The data set could also
benefit from having more demographic variables, like college choice. Instead of using age,
experience could be used, as it is a better indicator of a player’s development than age.
4. Results
As noted in the data and methods section, player productivity and efficiency data from
the 2013/14 season. The production statistics were transformed from per game to per minute
played in order to get a more accurate view at a player’s production. These statistics will reflect a
player’s production while he is on the court. Salary data was transformed to be expressed in
millions of US dollars. This allows for an easier interpretation of the estimated coefficients.
The final estimating equation is as follows:
Salary =  + 1Age + 2Assists +3Expiring_Contract +4Games Started
+5Guard +6Points + 7Steals + 8Offensive Rebounds +9Defensive Rebounds +10Field Goal
% +112-point field goal % +123-point field goal % + t
Turnovers, free throw percent, and blocks were omitted from the final equation due to their
statistical insignificance. Total rebounds was further broken down into offensive and defensive
rebounds to gain a better sense of where teams value rebounding. Games started and field goal
percent were added to the equation. The number of games started by a player indicate his role
and value on the team, as starters are typically paid more than reserves. Field goal percent was

7. added in order to further explore the effect of player efficiency, his ability to consistently make
shots.
Table 1 shows the estimated regression coefficients, t-statistics, and the degree of
significance of the t-statistic. Age, games started, points, assists, defensive rebounds, and field
goal percent all have positive coefficient signs. All these variables have t-statistics which are
significant. Of the variables with positive signs defensive rebounds has the greatest magnitude
followed by assists, points, age, field goal percent, and games started. The coefficients of
defensive rebounds, assists, and points have the biggest positive impact on salary, with a one
point increase over a minute of play having the effect of millions of dollars in additional salary.
Expiring contract, steals, offensive rebounds, three-point field goal percent, and two-point field
goal percent have negative signs, and have t-statistics which are significant. The greatest
magnitude of the variables with negative coefficient signs is steals, then offensive rebounds,
expiring contract, guard, two-point field goal percent, and three-point field goal percent. When
analyzed together, steals has the greatest effect on salary, followed by offensive rebounds,
defensive rebounds, assists, and points. Overall this model is a good fit and explains 51.10% of
the variance in salary. It is statistically significant, as there is a less than 1% chance all the
regression coefficients are simultaneously zero.
I originally hypothesized that all the production and efficiency statistics would produce
positive coefficients. My results show that this is not the case. Teams do value a player’s ability
and consistency to score, create opportunities for others, and transition the ball from the
defensive end to the offensive end. What they do not value though are players who produce at
the defensive end, specifically those who accumulate a lot of steals and blocks. Most surprising
was the effect offensive rebounds has on salary; teams do not value second chances. Yet, a
forward should expect to have a higher salary than a guard producing at the same level. It also
shows that teams value a player who can make shots consistently from both inside and outside
the three-point line, not just a specialist.
Naturally, an older player will have played more seasons in the NBA than his younger
counterparts, as a result, the positive sign of age provides evidence that teams value experience
over youth. Players who are on the last year of their contract should expect to be making a
significant less amount of money than those players with more than one year remaining on their

8. contracts. This can be explained by looking at how a player’s contract is structured. Those
players on expiring contracts will undoubtedly have been in the NBA for a number of years, so
there is evidence to suggest that teams prefer to front load contracts to older players. That means
a player’s contract is structured so that he receives a peak salary in year one, which decreases
each year, ending in the player receiving his lowest salary under that contract in the final year.
This might show that teams taper off their expectations as a player ages.
By applying the estimating equation to each player’s set of statistics individually, salaries
were generated. Table 21 highlights the most overpaid, underpaid, and accurately paid players in
the league. A common trend among the most overpaid players in the NBA is inclusion in the All-
Star game. The All-Star game is an exhibition between the best players, as voted on by the fans,
in the Eastern and Western Conference. The most underpaid players tend to be those who are
nearing the end of their rookie contract; the NBA operates under a rookie scale contract, which
means there is a limit on rookie salaries with the top overall pick earning the highest, followed
by the second pick, then third pick, and so on. The players who are most accurately paid are role
players, guys coming off the bench to fill a specific need.
5. Conclusion
In this analysis of the NBA, relating player salary with production, data was collected on
player contracts, production, age, and position. An OLS estimation of regression coefficients was
applied with salary as the dependent variable. All variables had t-statistics which were
significant. Age, games started, points, assists, defensive rebounds, and field goal percent all had
positive coefficient signs, meaning an increase in these would result in an increase in salary.
Expiring contract, guard, steals, offensive rebounds, three-point field goal percent, and two-point
field goal percent have negative coefficient signs which results in a decrease in salary if one of
these statistics increases. The largest effect on salary is seen with steals, closely followed by
offensive rebounds, both of which have negative signs. Defensive rebounds has the largest
magnitude for a variable with a positive sign, and third greatest overall, followed by assists and
points. The remaining variables have magnitudes which effect salary marginally compared to the
four mentioned above.
1 Data set availableupon request from pkerber@iwu.edu

9. My results support the findings of Berri, Brook, and Fenn (2011) in a number of ways.
Players who are able to score more points are more likely to be offered a higher contract. The
magnitude for the coefficient of points is one of the higher positive coefficients, so it provides
strong evidence supporting this. Also, players who focus on accumulating steals, should not
expect to see a positive effect in their salary due to their production of this statistic. A negative
sign on the coefficient for steals provides evidence supporting this. Berri, Brook, and Feen
(2010) do come to a conclusion that is further refined by my results. Their research led them to
conclude that players who focus on rebounding should not expect to secure a major contract.
Since I broke total rebounds down into offensive and defensive rebounds, I can more specifically
say that players who focus on accumulating offensive rebounds should not expect to see higher
salary as a result. On the other hand, players who accumulate more defensive rebounds should
expect to see an increase in their salary. Not only was defensive rebounding significant, it had
the largest positive magnitude of the variables used. This result partially supports the conclusion
of Berri (1999), that the most important factors for winning are a player’s ability to acquire and
maintain possession. One way of acquiring the ball is through rebounds. As a result teams should
be willing to spend more money on rebounders. A player’s ability to acquire and maintain
possession can also be measured with steals, which is his ability to acquire the ball. My findings
on steals show that they do not have a positive effect though.
Previous literature by Berri, Brook, Fenn, Frick, and Vicente-Mayoral (2005) and Kahn
and Sherer (1988) concluded that the height of a player is an important factor. Since you cannot
teach height, and each position requires a certain height to play, they concluded that teams’ value
forwards more than they do guards. My results support conclusion. The significance of the guard
variable shows that teams will pay more for a player who is tall despite that player producing at
lower levels than a player of smaller stature. Which leads me to believe that quality forwards are
of greater importance or harder to come by than guards are.
What my findings show is that the NBA has placed more significance in playing offense
than defense. Players are rewarded for points, assists, defensive rebounds, and high field goal
percent, but they see no dividends from producing higher levels of steals or blocks, two statistics
measured on the defensive side. Defensive rebounds can be considered an offensive production
statistic. Players who grab more defensive rebounds are giving their team more opportunities on

10. offense. The NBA rewards those who can facilitate or put the ball in the basket themselves and
penalizes those who protect the basket. The effect of offensive rebounds shows that teams would
strongly rather have players retreat back onto defense than crash the basket and attempt to grab
an offensive rebound. Furthermore, this provides evidence that teams value players who can
slow down the opposing team’s transition into offense.
The majority of overpaid players are All-Stars, which shows that organizations are
paying these players for more than what they do on the court. These players are the faces of their
respective franchises. They are the ones fans come to see play, and they are the ones the
organization financially benefits from the most through merchandise sales, ticket prices, and
popularity. The common theme among the most underpaid players in the league is that they are
players nearing the end of their rookie contracts. These players are the future faces of their
franchises, stars in the making. The most accurately paid players are role players. They are not
asked to do too much by their team. As their name suggests, they are asked to fill a role. Which
could mean the player is given a specific assignment or is a specialist.
The implications of this research project have the ability to effect the game of basketball
at the professional level. Since this research only concerned itself with performance in the NBA,
it shows which skills are worth money and which are not. Subsequently, when players practice,
workout, develop their skills, the incentive is there for them to focus their efforts more on their
offensive skill set. This could potentially lead to higher scoring and faster paced games.
Historically the NBA has pushed for more offensive output, hence the implementation of
goaltending, the three point line, and a 24 second shot clock. Both these changes have allowed
for more offense and an increased pace of play.
This research can be refined to include more production statistics as well as demographic
variables. Very few statistics exist which measure the quality of a player’s defensive play, so the
addition of an advanced metric measuring this could help better value defensive players. There
also exists statistics that measure a player’s efficiency or performance relative to the team he
plays for. The inclusion of this variable would provide us with evidence of a player’s value to his
team specifically. Demographic variables could help explain a player’s youth, high school, and
college development. Finally, including variables for region and school would give insight into
where the best players in the country are coming out of and developing at.

11. Appendix
Figure 1: Salary Distribution
0
10
20
30
40
50
60
70
80
0 2 4 6 8 10 12 14 16 18 20 22
Frequency
Salary (expressed in millions of US dollars)
Figure 2: Points per Game Distribution
0
5
10
15
20
25
30
35
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28
Points per Game
Frequency

12. Figure 3: Assists per Game Distribution
0
10
20
30
40
50
60
70
80
0 1 2 3 4 5 6 7 8 9 10 11
Assists per Game
Frequency
Figure 4: Rebounds per Game Distribution
0
10
20
30
40
50
0 2 4 6 8 10 12 14
Rebounds per Game
Frequency

13. Table 1: Tabulation of Regression Results for Salary
Dependent Variable: SALARY (N = 348)
Constant -6.744238***
(-3.876081)
Age 0.291902***
(6.726927)
Expiring Contract -1.786196***
(-4.910230)
Games Started 0.047212***
(7.269045)
Guard -1.386162**
(-2.413453)
Points 11.61564***
(6.894642)
Assists 12.46329***
(2.864644)
Steals -24.65773*
(-1.711711)
Offensive Rebounds -20.22697**
(-2.154129)
Defensive Rebounds 13.61560**
(2.544835)
Field Goal Percent 0.083698*
(1.804477)
Two-point Field Goal Percent -0.100498**
(-2.493935)
Three-point Field Goal Percent -0.039036***
(-2.687321)
Overall model goodness of fit and significance
Adjusted R squared = 0.511038
F-statistic = 31.22216***
Note: * Denotes degree of significance of the t-statistic (in parenthesis below): *** = 99%, ** =
95%, * = 90%

14. Table 2: Comparison of Real and Generated Salaries for 2013/14 Season
Over valued Under valued Correctly Valued
Amare Stoudmeire
$ 8,722,307
($ -12,957,585)
Demarcus Cousins
$ 12,248,949
($ 7,331,975)
Draymond Green
$ 863,823
($ -11,176)
Ben Gordon
$ 2,077,401
($ -11,122,598)
Chandler Parsons
$ 8,095,615
($ 7,169,115)
Reggie Bullock
$ 1,171,782
($ 22,782)
Deron Williams
$ 7,645,944
($ -10,820,185)
Paul George
$ 9,955,059
($ 6,673,056)
Greg Oden
$ 999,365
($ -28,058)
Chris Bosh
$ 9,805,238
($ -9,262,261)
Lance Stephenson
$ 6,788,444
($ 5,858,444)
Kosta Koufos
$ 3,050,599
($ 50,599)
Carmelo Anthony
$ 12,502,809
($ -9,177,083)
Chris Kaman
$ 8,969,912
($ 5,786,912)
Ishmael Smith
$ 1,006,107
($ 54,644)
2013/14 Generated Salary
(Generated Salary – Real Salary)

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