The Globalization of the National Basketball Association
Measuring the Impact and Valuation of Foreign Players
Ian Goldberg
Haverford College Economics Department
Senior Thesis
April 2012
Advisor: Professor Anne Preston
Abstract
This paper attempts to analyze the increasing trend of successful foreign players
in the NBA for the 2002-2009 seasons. By controlling for numerous factors that affect a
team’s winning percentage, the impact of foreign players can be isolated. Furthermore,
this study addresses the salary gap which exists between international players and
American born players. It provides insight into the level of success reached by foreign
players and NBA executives’ valuation of them compared to their domestic counterparts.
Rather than including all foreign players which have entered the NBA, this study looks at
players, both foreign and domestic, who have played significant minutes for their teams.
Foreign players do positively impact a team’s winning percentage, especially those whom
did not attend college in the United States. We also witness a larger increase in salary
during free agency for foreign players than for domestic players, supporting the idea that
foreign players are undervalued upon entering the NBA.
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Table of Contents
I.
II.
III.
IV.
V.
VI.
VII.
VIII.
Introduction…………………………………………………………………....4
Literature Review……………………………………………………………...6
Data Overview………………………………………………………………..12
Methodology…………………………………………………………………20
Part 1: Foreign Players Effect on Winning Percentage……………………...20
Part 2: Measuring Salary Change with Free Agency………………………...22
Results………………………………………………………………………..24
Conclusion…………………………………………………………………...30
Data Appendix……………………………………………………………….34
Bibliography…………………………………………………………………37
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I.
Introduction
The National Basketball Association (NBA) has seen a tremendous increase in its
global fan base over the past twenty years. The 2011 NBA Finals between the Miami
Heat and the Dallas Mavericks was televised in a record 215 countries in 46 languages
(Rossman, 2011). The NBA attributes 30% of its licensed sales to overseas markets
(Lefton, 2009). Europe followed by China have been the leading market supporters;
however, new nations have shown interest in the NBA brand. Arabic, Indian, Brazilian,
and African broadcasting networks brought the NBA to homes for the first time during
the 2011 Finals. How has the NBA been able to reach the previously untapped fan base?
Technology and the Internet have made it feasible for people to access the NBA and get
introduced to basketball. Another explanation is that international fans want to support
players from their home countries. The 2011 Finals alone featured eight foreign players
from six different countries. The NBA brand has truly expanded across the world and
interest has grown dramatically. In 2011, there were a record high 86 international
players from 40 different countries on NBA rosters (Rossman, 2011). Foreign players
accounted for 19% of the total players in the NBA (Schachtel, 2011). That is a staggering
statistic compared to the mere 12 international players in 1990. There has been much
debate over why there has been such a growth of foreign players in the NBA.
As the popularity of the NBA has risen in the past twenty years, foreign players
have had the opportunity to master fundamental skills. The NBA has truly untapped a
talent pool that rivals the American college players in the United States. The biggest
critique with American players nowadays is there lack of fundamental skills and
defensive mentality. NBA executives have increasingly shifted their drafted preferences
4
towards foreign players for these very reasons (Papile, 2011). A total of three foreign
players were selected in the 1995 draft, with only one of the three selected in the (late)
first round. The 2011 draft had four foreign players selected out of the first seven, and 14
players taken total. As the NBA has transformed into a global brand through a
conglomeration of cultures, I want to look at the value of these foreign players in
comparison to American born players. I will answer the following research questions:
1. What effect do foreign players have on the success of their teams?
2. Do foreign players receive comparable compensation to U.S. born players?
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II.
Literature Review
There exists a substantial amount of empirical literature surrounding the
performance of international players in the NBA. Furthermore, several economists have
attempted to measure salary discrimination by nationality.
Kevin Salador (2004) looks at what NBA executives value when selecting
international players in the draft. More specifically, he analyzes which skills translate
from foreign leagues to the NBA and the most significant international statistics that
correlate with success in the NBA. His data set consists of every foreign-born player who
has played in the NBA up until the midpoint of the 2009-2010 season. However, he
excludes all foreign-born players who went to college in the United States because they
can be scouted like American players. Not only do foreign players have a different skill
set but seeing them play against other foreign players of all ages is quite different from
watching them play against American college players in the same age range. Salador’s
methodology is to first regress draft order on a number of international per game
statistics. This explains what executives look for in selecting foreign players from their
home nation leagues. Next, he measures success in the NBA with four variables: NBA
awards won, career length, career NBA PER (player efficiency rating), and minutes per
game. PER was developed by John Hollinger to measure a player’s per minute
performance. It takes into account positive events such as made field goals, free throws,
assists, steals, and blocks as well as negative events like missed shots, missed free
throws, and turnovers. The league average every season is 15.00, which serves as a
benchmark for success. These variables are regressed on the same independent
international statistics to measure which have an effect on success in the NBA. He uses a
6
Tobit regression model because NBA award index has most of its observations at the
minimum value of zero.
Salador finds that certain statistics such as assists, rebounds, blocks, steals, and
shooting percentage all have positive effects and are transferable skills from the
international leagues to the NBA. However, due to increased competition which players
face in the NBA, scoring statistics such as points per game, field goals made, and free
throws made all have insignificant correlations. Salador concludes that executives value
bigger players who can block shots, since blocks per game had the strongest correlation
to draft order. Finally, those international players who have won awards overseas will
have the most success in the NBA. As a follow up to his results, Salador suggests a study
comparing the determinants of success in the NBA of American players and international
players to examine whether NBA teams should fundamentally alter the way they scout
and evaluate international players compared to college players.
The tremendous increase in foreign players sparked an interest in potential salary
gaps between international players and American-born players. The salaries of foreign
players might differ from those salaries of United States players when both groups have
similar skills and characteristics. A negative salary gap might arise if executives are
unable to effectively evaluate foreign players. Alternatively, famous foreign players may
be paid a premium because the NBA relies on them to grow its international fan base.
Eschker, Perez, and Siegler (2004) look at salary determinants to compare the
compensation of international players to athletes born and trained in the United States.
They estimate a log-linear model relating the natural log of player salaries in a season
with performance statistics from the previous season as independent variables. Eschker et
7
al. include a binary variable equal to one for players born outside of the United States as
well as a binary variable equal to one if the player did not attend college. They find that
international players are paid premiums relative to other players of similar skill levels for
the 1996-1997 and 1997-1998 seasons. They use salary data and performance measures
through the 2001-2002 season to test if the premium lasted. The results showed that the
premium disappeared after the 1997-1998 season. Eschker et al. attribute the wage
premium to a “winner’s curse” phenomenon. They conclude that in a free agent auction,
executives are inexperienced bidders and poor at evaluating talent. They bid up the salary
above the players marginal revenue product. The period between 1996-2002 saw a
substantial increase in foreign players drafted. Eschker et al. believes that initially NBA
executives had little experience evaluating international talent and vastly overpaid the
players. As time progressed, teams devoted more resources towards scouting of
international players and the premium disappeared.
While Eschker et al. support the “winner’s curse” hypothesis, Yang and Lin
(2010) take an alternate stance. As opposed to a wage premium for foreign players, they
provide evidence of salary discrimination by nationality. Yang and Lin feel that NBA
executives are more conservative in payroll allocation amongst foreign players when
there is difficulty evaluating future performance. They believe the bargaining power is in
the hands of NBA owners because there is low demand for players outside of the NBA
and they can offer moderate salaries. Foreign players are more susceptible to accepting a
lower salary than an American player because the salaries they receive from a foreign
league are much lower than a moderate NBA salary. They collect an unbalanced panel
data set of 618 NBA players between the 1999-2000 and 2007-2008 seasons. First, to
8
estimate which statistics impact salary the greatest, Yang and Lin model a salary equation
with the logarithm of yearly salary as the dependent variable. A vector of player statistics
and characteristics serve as the independent variables. Next, they estimate the individual
wage premium among players and regress this predicted value on nationality, foreign
market size, race, and a vector of macro variables. After controlling for salary
determinants, they find that international players receive a 13-18% lower salary on
average than U.S. born players. Finally, Yang and Lin conclude that those foreign players
coming from a large economy, such as Spain, will likely receive a wage premium
compared to those foreign players from a small economy, such as Poland.
Finally, Roberto Pedace examines earnings, performance, and nationality
discrimination in the English Professional Soccer League (Pedace, 2008). In his empirical
model, Pedace chooses team performance as the dependent variable. His independent
variables are nationality variables and team indicators. The nationality variables measure
the number of foreign players who appeared in at least one league match, categorized by
eastern and southern Europeans, western and northern Europeans, Africans, South
Americans, the British Isles, and other. The team indicators include league division, total
of all salaries, and team manager. The team manager variable accounts for stability in the
coaching staff. As a measure of team chemistry, he suggests that the number of players
will negatively affect performance. An important control is attendance and the correlation
between player appearances for some nationalities. Pedace witnesses a high correlation
between team payroll and team productivity. Furthermore, he concludes that players from
South America are overpaid in the Premier division of the English professional soccer
league. This is in response to an increased attendance when there are more appearances
9
by South American players. Pedace implies that marginal revenue effects are important
factors in hiring and playing decisions.
This paper will add to the existing literature in a couple of distinct ways. First, it
will focus on the impact of foreign players on the teams’ winning percentages. The
majority of previous literature has focused on individual player performance and how
performance affects salary. I am concerned with foreign players who have made an
impact on a team’s performance. I am not interested in looking at foreign players who
enter the NBA, fail to have in-game experiences, and exit the league quickly. I adopt
Pedace’s (2008) dependent variable of team performance to analyze foreign players’
return to the team.
Next, I’d like to provide evidence on the salary gap between foreign and
American players. As explained above, the existing literature has opposing claims:
foreign players receive a wage premium upon entering the NBA versus foreign players
are discriminated against in comparison to American-born players. Both papers use the
log of salaries as the dependent variable and a vector of player performance measures and
characteristics. My model will use similar control variables; however, I will use the
percentage change in salary of the year before free agency and year post free agency. A
player’s performance in the last year of his contract is the best indication of the
magnitude of his next contract. I want to regress percentage change in salary on NBA
player characteristics in the previous year and a dummy variable for foreign. New
contracts isolate the value of the player based on performance. If the percentage change
in salaries for foreign players and U.S. born players is not the same, after controlling for
player characteristics, we can address the issue of underpayment or overpayment at the
10
time of hiring when only foreign data is being evaluated. Further, I will regress the new
player salary on the previous year statistics to determine if the players are being paid as
they should be.
11
III.
Data Overview
I have collected data for two complementary datasets, one at the team level and
the other at the individual level for the 8 NBA seasons from 2002-2009. Some data at the
individual level have been collapsed and merged into the team level data set. The two
main sources for the data are Basketball-Reference.com and Rodney Fort’s Sports
Economics- Sports Business Data. Basketball-Reference contains all individual player
statistics and characteristics across the 8 year period. I collected per game statistics for
each individual player on a given team as well as age, height, and weight. For each
player I establish whether he played basketball at a United States university and whether
he was born outside of the United States. For each team and year, I collected these
statistics for the nine players with the highest average minutes per game.
Rodney Fort’s website provides team level data such as winning percent and
payroll, as well as all individual player salaries. I collapsed the mean height and age from
the individual level data for each team year and merged this with the team level data to
create team averages. I also take the standard deviation of salaries for each team year.
Basic descriptive statistics of the collected data are very instructive. Figures 1-3
show the number of foreign players (all foreign players and those who did not go to
college) who are among the 5 players with the most minutes per game for their team, are
the 6th and 7th players in terms of minutes per game, and the 8th and 9th players in terms of
minutes per game respectively. Figure 4 displays the increase of all foreign players who
are among the nine team members with the highest minutes per game. I isolate the top5
because five players are on the court at all times and the five playing the most minutes
will usually have the largest impact on the team’s success. For the 6th and 7th players,
12
NBA teams usually play seven players for a substantial amount of minutes per game. The
intuition is that every team has a main backup guard and back-up forward. Those teams
that rely on a more balanced effort will play nine players at the most, which is why I have
accounted for the 8th and 9th players (two backup guards and two forwards). A player
must have competed in at least one quarter of the team’s contests for the season to be
placed in the top5, top67, or top89. A player who enters the top5 for a 6-game span when
three players are injured would not have a significant impact on the teams overall
winning percentage.
I am less concerned with demonstrating the increase of foreign born players over
8 years as I am with demonstrating the increase of successful foreign born players in the
NBA. Figure 1 displays the sharp increase of foreign born players who were in the top
five average minutes for their respective team. As shown, there are 14 foreign born
players in the top five for 2003. This number doubles to 28 by 2006 before dropping to
25 players by 2010. The number of foreign players who did not attend college steadily
increases over the 8 year period. Not only is there an influx of international players, but
these players are having quantifiable success with minutes played serving as a proxy.
Figure 1: # of foreign players in top five
minutes per game average for their team
30
25
20
15
10
5
0
2003
2004
2005
2006
Top Five
2007
2008
2009
Top Five No College
13
2010
Below in Figure 2 is the same formatted graph with respect to the number of
foreign players who receive the 6th and 7th most minutes on their teams. There is less of a
visible upwards trend, especially in the case of players who did not attend college, where
we witness sharp declines in 2005 and 2009.
Figure 2: # of foreign players who are
6th and 7th in minutes per game
average for their team
15
10
5
0
2003
2004
2005
2006
6th and 7th
2007
2008
2009
2010
6th and 7th No College
Next, Figure 3 demonstrates the number of foreigners with the 8th and 9th most
minutes played. There is a more noticeable upwards trend compared to Figure 2.
Figure 3: # of foreign players who are
8th and 9th in minutes per game
average for their team
25
20
15
10
5
0
2003
2004
2005
2006
8th and 9th
2007
2008
2009
8th and 9th No College
14
2010
Figure 4 below is a graphical representation of the increase of foreign born
players among the top nine most minutes per game average. We witness an obvious
positive trend over time, proving that foreign players are in fact increasingly important
members of their teams. Foreign players are not simply joining the NBA and not getting
playing time; they are coming in at greater numbers and making an impact.
Figure 4: # of foreign players among the 9
players with most minutes per game average
for their team
60
50
40
30
20
10
0
2003
2004
2005
Top Nine
2006
2007
2008
2009
2010
Top Nine No College
Table 1 gives team level descriptive statistics. The variable win is a team’s
winning percentage, with the mean being 50.085% over the 8 year period. The variables
top5,top67, and top89 tell us the number of foreign players in the respective groups.
These numbers represent all foreign born players, regardless of whether they went to
college or not. The same labeled variables followed by ‘College’ or ‘No College’
represent the foreign born players who did or did not go to college respectively. If we
interpret the mean of top 5, there are .712 foreign players in the top 5 minutes per game
average on a team for a given year. Log_pay is simply the log of team payroll. Std_sal is
the standard deviation of salaries as a proxy for team chemistry. The reason std_sal has
fewer observations is because I have not included salary data for the 2009-2010 NBA
15
season1. The next control variable, star, is the number of NBA All-Stars on a team from
the previous year. Finally, I have included average height and age.
The trend of top5(college), top67(college), and top89(college) represent the area
between the red and green lines in Figures 1,2, and 3 respectively. The number of foreign
players per team is larger for foreign players that did not attend college than foreign
players that did attend college. One might think that foreign players who go through the
United States college system will have more success after competing against the nation’s
top NBA prospects; however, these statistics suggest the opposite. International leagues
must be competitive and prime foreign players for NBA competition.
Table 1: Team Level Data (n=236)
Variable
win%
top 5
6th and 7th
8th and 9th
top 5 (No College)
6th and 7th (No College)
8th and 9th(No College)
top 5 (College)
6th and 7th (College)
8th and 9th(College)
log_pay
std_sal*
star
height
age
*n=207
Mean
Std. Dev.
Min
Max
50.085
14.916
15
82
0.712
0.795
0
3
0.356
0.514
0
2
0.492
0.649
0
2
0.504
0.712
0
3
0.220
0.425
0
1
0.314
0.541
0
2
0.208
0.491
0
2
0.136
0.363
0
2
0.178
0.391
0
2
7.793
0.097
7.369
8.102
4134280 1219668 1470207 8395152
0.678
0.776
0
4
78.878
0.708
76.667
80.667
27.009
1.72
22.889
32.1
The following Table 2 includes summary statistics of the individual level data.
Column All, containing 2270 observations, represents each player who is among the 9
with the most minutes played per game within his team. Column Foreign includes the
374 foreign-born players, regardless of whether they attended college, and the 1896
I ran OLS and fixed effects regressions with and without std_sal. Results did not change, thus I chose
to include std_sal as a control variable.
1
16
observations for Domestic are those players born in the United States. Players that remain
in the NBA are counted in the data for as many years as they remain in the top nine
minutes played for their team. Although height and weight should not alter, his in game
statistics will change from year to year. All variables are season averages. There are
several significant differences and interesting conclusions drawn from the descriptive
statistics. Please follow the key for levels of significance directly under Table 2. Foreign
players are on average around 1.5 years younger than domestic players, as highlighted in
orange. This could be because domestic players have longer lasting careers and thus more
experience. Another hypothesis is that international players can play professionally
starting in their early teens and can enter the NBA draft at age 19. Domestic players can
enter at the same age; however, most college players enter the draft after a couple of
years at a university. Thus, they would enter the league at around 21 or 22 years old.
A player in the team’s top nine with respect to minutes played means that he is a
significant member of the team and is a valuable contributor. With this being said, the
variables highlighted in green below have led me to believe that the more successful
foreign players are forwards or centers rather than guards. Compared to domestic
players, foreign players shoot a lower three point percentage, rebound better, have low
assist averages, block significantly more shots, are taller, and weigh more (all are
significant at the .01 level). To clarify, guards have higher assist totals than big players
because they are the facilitators on the court. Surprisingly, the point per game averages
highlighted in purple are quite even amongst both groups of players. Points per game is
the most coveted stat and measure of success. Foreign players proportionally produce a
17
similar amount of points per game as domestic players within the top nine minutes
played.
Table 2: Individual Level Data
All (n=2270) Foreign(n=374) Domestic(n=1896)
Variable
Total Games
Games Started
Minutes
Points
Field Goal%
Three Pt%
Free Throw%
ORebounds
DRebounds
Assists
Steals
Blocks
Turnovers
Fouls
Mean
62.19
Mean
66.96**
Mean
64.9
(17.7)
(16.34)
(17.98)
41.7
41.7
41.72
(28.57)
(28.04)
(28.68)
28
26.63***
28.22
(7.17)
(6.64)
(7.24)
11.68
11.14**
11.78
(5.58)
(5.18)
(5.65)
0.453
0.472*
0.449
(0.05)
(0.05)
(0.05)
0.264
0.227***
0.271
(0.162)
(0.193)
(0.155)
0.754
0.749
0.755
(0.096)
(0.103)
(0.09)
1.27
1.45***
1.24
(0.875)
(0.886)
(0.868)
3.49
3.77***
3.45
(1.72)
(1.71)
(1.72)
2.55
2.15***
2.63
(1.91)
(1.84)
(1.91)
0.866
0.708***
0.897
(0.417)
(0.327)
(0.425)
0.539
0.733***
0.5
(0.571)
(0.664)
(0.543)
1.63
1.58
1.64
(0.738)
(0.671)
(0.75)
2.37
2.48***
2.35
(0.627)
(0.633)
(0.624)
27.01
25.87***
27.24
(4.06)
(3.52)
(4.18)
Height
78.889
81.08***
78.457
(3.666)
(3.78)
(3.484)
Weight
217.533
230.59***
214.971
Age
(27.92)
(28.58)
(27.072)
**mean of foreign players is significantly different than mean of domestic players at .05 level
***mean of foreign players is significantly different than mean of domestic players at .01 level
Within the individual level data, I am most concerned with looking at salary
change from the year pre-free agency to the year post-free agency for foreign and
domestic players. Below, Table 5 gives summary statistics of salary change among
players in the top nine minutes played under the age of 31. NBA players reach their peak
level of performance at the age of 25 and performance declines noticeably by the age of
18
302. I don’t include the change in salaries for players in the declining stages of their
careers. Holding performance constant, we can look at wage changes of foreign players
vs. domestic players at the peak of their professional careers. The variable pre-salary is
the dollar salary of the player in his final contract year. Post-salary is the dollar salary of
the player for the year after he signs a new contract. I calculated the variable change as
follows:
–
Change= (post-salary
pre-salary)
pre-salary
I took the log of both pre-salary and post-salary before generating lnchange. Table 3
demonstrates that on average foreign players experience a sharper increase in salary in
relation to domestic players because the mean of change for foreign is 2.01 compared to
1.744 for domestic. The mean salaries for foreign players are lower than domestic
players, possibly lending to the theory that NBA executives undervalue foreign players.
Once they prove themselves in the league, they realize a sharper increase in salaries
compared to American born players. The only significant difference between foreign and
domestic players is lnchange at the .10 level. I will examine this relationship in my
regression analysis so I can control for performance.
Table 3: Foreign vs. Domestic Players
Variable
pre-salary
post-salary
change
ln_presalary
ln_postsalary
lnchange
Obs
28
28
28
28
28
28
Foreign Players in Top 9
Mean
Std. Dev.
Min
2864718
2986549
366000
5607379
2661392
688000
2.010
1.844
(0.371)
14.480
0.879
12.810
15.381
0.658
13.441
0.900*
0.673
(0.464)
Max
14600000
10800000
6.084
16.498
16.195
1.958
Obs
127
119
119
127
119
119
Domestic Players in Top 9
Mean
Std. Dev.
Min
3266633
3885898
349458
5123323
3593737
563000
1.744
2.668
(0.756)
14.455
1.023
12.764
15.158
0.835
13.241
0.672
0.824
(1.410)
Max
20200000
14900000
15.656
16.819
16.516
2.813
*mean of foreign players is significantly different than mean of domestic players at .10 level
Dave Berri (2010) examined every player from 1977-2008 and determined that NBA player
performance starts declining at age 25. Performance after this point is not much different until
noticeably declining after the age of 30. At age 35, he found that players begin costing teams wins.
2
19
IV.
Methodology
Part 1: Foreign Players Effect on Win Percentage
I begin my analysis with a model which attempts to describe winning percent of
teams and the extent to which foreign players contribute to winning. I estimate the
following Ordinary Least Squares regression:
Wit = 0 + 1top5it + β2top67it + β3top89it + Xit + it
where Wit is the winning percentage of team i in year t
I am interested in coefficients β1, β2, and β3 because they reflect foreign players
impact on winning percentage. The variables top5, top67, and top89 serve as measures of
participation of all foreign born players, regardless of attending college. For further
clarification, top5 is the number of foreign players out of the five players on the team
with the highest minutes per game average. Top67 is the number of foreign players out of
6th and 7th players on the team with the highest minutes per game average. Top89 is the
number of foreign players out of the 8th and 9th players on the team with the highest
minutes per game average. My hypothesis is that the coefficient on top5 will demonstrate
the strongest positive relationship with winning percentage, followed by top67 and then
top89. Foreign players that play more should have a stronger correlation with the success
of the team. If there is not a significant correlation between foreign players and team
success, we must attribute the success to U.S. born players.
I will run the same regression using top5_coll, top67_coll, top89_coll, top5_no,
top67_no, top89_no which represent foreign players who did and did not attend college
in the respective sections. I estimate the following OLS regression:
Wit = 0 + 1top5_noit + β2top67_noit + β3top89_noit + 4top5_collit +
β5top67_collit + β6top89_collit Xit + it
where Wit is the winning percentage of team i in year t
20
In the equations above, Xit is a vector of factors affecting winning percentage that
may vary over time. My goal is to control for as many factors that affect a teams winning
percentage so I can isolate the impact of foreign players’ participation. Controls in vector
Xit include payroll, star, standard deviation of salaries, average height, average age, and
average age squared. The variable Payroll explains team monetary sufficiency and value.
I expect there to be a positive correlation between payroll and winning percentage.
Teams with higher payroll should have better players and win more games. Star is a
necessary control variable because the presence of an All-Star should increase a team’s
winning percentage. I chose to include the standard deviation of salaries as a variable
because it serves a proxy for team chemistry. Teams with higher levels of deviation
amongst salaries could have issues that reflect on court performance. Players on the lower
end of the salary spectrum might feel like they are being undervalued, especially if they
are producing as much as a player receiving a large salary. The std_sal attempts to
quantify the social relationships between teammates. I expect Age to have a positive
relationship with winning percentage, yet Age squared to have a negative impact on
winning percentage. I believe that teams too young or too old do not achieve great
success. There are exceptions to the rule, yet winning teams will have a combination of
youth and veterans. I predict a positive correlation between height and winning
percentage.
I include a fixed effects regression model by team, and team and year to see if on
average foreign players effect winning percentage within a team, and within a team for a
given year.
21
Part II: Measuring Salary Change with Free Agency
The analysis continues as I model the percentage change in salary of free agents
with the equation:
ΔlnSalaryit = 0 + 1Foreignit +β2Collegeit + β3Foreign*Collegeit+ Pit + it
The sample includes all free agents from the 2002 to 2008 NBA seasons who
played in the top nine most minutes for the given season. Restricted and Unrestricted free
agents are pooled together in the data set. An unrestricted free agent can sign with any
team, while for a restricted free agent, the current team has the right to match any offer
by other teams and retain the player. The dependent variable is the log percentage change
in salary for the player at the time of free agency. Explanatory variables include a binary
for foreign born, a binary for attending college, an interaction term between college and
foreign, and vector Pitβ of all important performance statistics and individual
characteristics such as height, weight, and age. I have restricted age to include free
agencies during a player’s prime years of growth and effectiveness. Thus, I include free
agencies when the player is under the age of 31.
The coefficient β1 on foreign tells me how being foreign affects the change in
salary, holding all performance measures constant across nationalities. β1 will allow me
to test whether foreign-born players or U.S. born players experience a larger change in
salary and in turn which group is undervalued. I have included an interaction term
between foreign and college to see if the effect of foreign changes if the player attended
college. The coefficient on β3 will control for the international players who came over to
the United States before entering the NBA.
22
All statistics are taken from the last year in the players existing contract. I believe
the year before free agency is the best determinant for measuring a players level of
success and greatly impacts the salary offered the following year. Besides Turnovers,
which should decrease the value of a player, all other statistics should have a positive
correlation with change in salary. These include points, rebounds, assists, steals, blocks,
field goal percentage, three point percentage, and free throw percentage. As mentioned, I
am looking at free agencies for all players under the age of 31, and I have included age
and age squared variables to control for decreasing return to age. I hypothesize that NBA
executives will value foreign players and domestic players evenly. I do believe that
teams are more interested in bigger foreign players and look at certain statistics to
evaluate the players through a different model than they would use to look at American
players.
I will run a second salary regression with post-free agency salary as the dependent
variable:
lnPostSalary = 0 + 1Foreignit +β2Collegeit + β3Foreign*Collegeit +Pit + it
This equation allows me to determine if there is any premium paid to foreign or
domestic players after free agency. The independent variables and control variables will
be the same as the initial change in salary regression above.
23
V.
Results
Table 4: OLS Regression Results
Effect of Foreign Players on Winning Percentage in NBA
(Dependent Variable is winning percent; standard deviations are in parentheses)
(1)
Independent Variable
All Foreign Players
Top 5
(2)
Coefficient
5.375***
(1.017)
Top 6-7
2.470*
Top 8-9
2.326*
Constant
1405.801
(1.484)
(1.236)
(932.609)
Foreign No College
Top 5 (No)
6.167***
(1.171)
1.748
Top 6-7 (No)
(1.883)
4.352***
Top 8-9 (No)
(1.483)
Foreign College
Top 5 (College)
3.413**
(1.656)
3.078
Top 6-7 (College)
(2.067)
-0.518
Top 8-9 (College)
(1.977)
1348.961
Constant
(926.868)
*Significant at .10 level
**Significant at .05 level
***Significant at .01 level
The team level results above display significant results for the regression
including all foreign-born players. The coefficient on Top 5 is fairly large (5.375) and
statistically significant at less than the .01 level. We can interpret the coefficient on Top 5
as an increase of one foreign player in the top five most minutes played will increase a
team’s winning percentage by 5.375 percentage points. As I predicted, the effect on
winning percentage decreases as we look to the Top6-7 and then further decreases for the
Top8-9 variable. Top6-7 is significant at around the .10 level. Since the minimum amount
24
of players in the top6-7 is 0 and the maximum is 2, each value is weighted heavily. An
increase of one foreign player will increase a team’s win percentage by 2.47 percentage
points. Finally, an increase in one foreign player in the Top8-9 will increase a team’s win
percentage by 2.326 percentage points, as the coefficient is significant at less than the .10
level.
Next, we can look at results for foreign players that entered the NBA straight
from an international league. The Top 5(no) variable has a coefficient of 6.617, similar in
magnitude to the coefficient of Top5 of all foreign players, and is significant at less than
the .01 level. The coefficient on Top6-7(no) is not significant at the conventional levels;
the 6th and 7th players do not impact winning percentage. Unexpectedly, the coefficient on
Top8-9(no) is very high (4.352) and much higher than the coefficient for all foreign
players. It is significant at less than the .01 level. This supports a conclusion that foreign
players who did not attend college have a stronger impact on a team’s winning
percentage compared to foreign born players who did attend college for the 8th and 9th
men.
The results for the foreign players who attended college before the NBA are not
as significant as those players who did not attend college. We see that the coefficient on
Top 5(college) is the only significant result of the three variables. Although insignificant,
the trend of Top 8-9(college) is negative, which would suggest that these players impact
winning percent for the worse. The second regression (2) demonstrates that the more
successful foreign players in the NBA come directly from overseas as opposed to
attending college beforehand. We can attribute the significant coefficients on Top 5, Top
25
6-7, and Top 8-9 mostly to the foreign players with no college rather than the foreign
players with college experience.
Please refer to Data Appendix A to see the control variables that impact team
winning percentage. For both OLS regressions, the star variable is significant at the .01
level as I predicted. An increase in one All-Star on a given team yields a 9.2 percentage
point increase in winning percent. For the regression with foreign (no) and foreign(coll)
variables, the log of payroll was significant at the .10 level. This is surprising because
payroll was expected to have a greater positive impact on winning percentage
Table 5: Fixed Effects Regression Results
Effect of Foreign Players on Winning Percentage in NBA
(Dependent Variable is winning percent; standard deviations are in parentheses)
(1)
Independent Variable
All Foreign Players
Top 5
Top 6-7
Top 8-9
(2)
(3)
(4)
Coefficient
3.191**
3.748***
(1.333)
(1.409)
0.937
1.278
(1.645)
(1.681)
1.583
2.094
(1.340)
(1.469)
Foreign No College
Top 5 (No)
3.552**
4.049**
(1.698)
(1.762)
Top 6-7 (No)
0.609
1.159
(2.076)
(2.146)
Top 8-9 (No)
3.606**
3.975**
(1.715)
(1.777)
2.959
3.230
Foreign College
Top 5 (College)
(2.104)
(2.189)
Top 6-7 (College)
2.000
1.960
(2.222)
(2.272)
Top 8-9 (College)
-1.157
-0.590
(2.180)
Team Fixed Effects
Year Fixed Effects
Yes
No
(2.269)
Yes
No
Yes
Yes
Yes
Yes
**Significant at .05 level
***Significant at .01 level
Table 5 gives results from regressions including team and year fixed effects. I ran
regressions with the same model exhibited in Table 5; however, I did team fixed effects
26
and team year fixed effects for All Foreign Players, as well as team fixed effects and
team year fixed effects for Foreign No College and Foreign College. The coefficient on
top 5 and top 5(no) are significant across all results. For team fixed effects, the
coefficients are 3.191 and 3.552 respectively. Fixed effects for team and year yield
coefficients of 3.748 and 4.049 respectively. The positive coefficients convey that foreign
players, and specifically foreign players who did not attend college, have a positive effect
on win percentage within their teams as well as within their teams for a given year. Top 5
for team year fixed effects is significant at the .01 level. The only other variables of
significance are Top 8-9(no) for team fixed effects and team year fixed effects. We can
interpret these positive coefficients in the same fashion as top 5 and top 5(no). Using the
fixed effects model, we see that foreign born players who attend college do not have a
significant impact on a team’s winning percentage, for the team and team year models.
These results seem logical because the coefficients on the foreign variables for ‘No
College” are greater than the coefficients on all foreign players. The insignificance of
foreign players who did attend college is pulling the coefficients on all foreign players
down.
The variables of significance for foreign players who did not attend college are in
line with the OLS results. However, for all foreign players, the fixed effects by team and
team year display insignificance for top6-7 and top8-9. The interpretation of these
findings is that for top6-7 and top8-9, the impact foreign players have on winning percent
varies within the team and within the team and year. The OLS results tell us that overall,
these players do have an impact on winning percent. Furthermore, while the variable Top
27
5(coll) is significant for OLS, it is insignificant for fixed effects which means that foreign
players’ impact is not conclusive after isolating by team and year.
Data appendix B demonstrates that star is the only control variable which
significantly impacts team performance. The increase in one All-Star player with fixed
effects increases win percentage by 10.1 percentage points at the .01 level.
Table 6: OLS Regression Results
Effect of Foreign Players on Change in Salary (Standard deviations are in parentheses)
(1)
Independent Variable
Free agents with age<31
Foreign Dummy
(2)
Coefficient
Dep: Log change in salary
College Dummy
Foreign * College
Constant
Dep: Log post-year salary
.508*
.153
(.267)
(.136)
.351
-0.110
(.242)
(.147)
-.348
-0.165
(.354)
(.215)
106.501
-90.346
(80.774)
(56.11)
*Significant at .10 level
We shift to the individual level data for the analysis on salary gaps amongst
foreign players and American players. In Table 6 above, the only variable of significance
at less than the .10 level is Foreign, with a coefficient of .508. With the dependent
variable being the natural log change in salary, the interpretation of Foreign is that
foreign born players have a higher change in salary than American born players by 50.8%
percentage points3. Holding all individual statistics and characteristics constant from the
year prior to free agency, foreign players experience a larger salary gap. We can attribute
this gap to the undervaluation of foreign players as they enter the NBA. Once free agency
occurs and they have proved themselves, their salary gets recalculated in line with
3
This might seem like a big gap; however, the change in salaries for some players is upwards of 250
percentage points.
28
domestic players. Although the interaction between foreign and college is not significant,
the negative coefficient suggests that foreign players who attended college experienced a
negative change in salary.
Finally, I ran a post-free agency salary regression to determine if foreigners were
being paid similarly to domestic players after free agency. The lack of significance of the
variables of concern supports the interpretation from the change equation (1) in Table 6.
Players are being paid as they should be post free agency. Foreign players do witness a
positive shift in salary compared to American players, because they are undervalued
when they get drafted into the NBA.
Data appendix C displays all of the performance controls for the regressions. We
see that Field Goal percentage, significant at the .01 level, positively impacts the log
change in salary. As predicted, turnovers have a statistically negative impact on salary
change. These were the only two significant controls worth noting.
29
VI.
Conclusion
The aim of this study is to demonstrate the increasing trend of influential foreign
players in the NBA, discuss whether they directly impact the success of their teams, and
to investigate the dispute regarding the valuation of foreign players. Previous literature
has stated that more and more international players are signing with NBA teams and a
rising number of international players are drafted each year (Du, 2011). This paper goes
one step deeper and reveals whether these foreign players are contributing to a team’s
winning percentage. NBA executives have clearly shifted their draft preferences and have
spent more time scouting international players. The gap between foreign players and
American born players is closing rapidly.
I found that there has been an increase in the number of foreign players making
beneficial contributions to their teams. There has been a distinct upwards trend in the
number of foreign players among the nine players with the most minutes per game
average for their team from the 2002-2009 seasons. Even more impressive, the number of
foreign players in the top five minutes played per game average for their team has risen
sharply. Foreign players are staying in the NBA and more and more are receiving
increased playing time, further perpetuating the notion that foreign players contribute
positively to NBA franchises.
Salador (2004) concluded that executives value bigger foreign players who can
block shots. My individual level data supports his claims because of the distinguishing
and significant differences between foreign players in the top nine minutes average per
game and American born players in the top nine minutes average per game. The foreign
born players shoot a lower three point field goal percentage, grab more rebounds, give
30
out less assists, block a substantial amount of more shots, are taller, and weigh more. I am
inclined to believe that the successful foreign players in the NBA play the forward or
center positions.
The standard OLS regression results described a significant effect on winning
percentage for all foreign players in the top five minutes played average, amongst the 6th
and 7th minutes average, and the 8th and 9th minutes average. Since players who play the
most usually have the largest impact on a team’s success, the coefficient was largest for
top5 and lowest for top8-9. The regression including foreign players who attended
college and foreign players who did not attend college showed that foreign born players
who did not attend college have a strong impact on team’s success. While the coefficients
on top5(no college) and top5(college) are both significant, the coefficient of top5(no
college) is almost double that of top5(college).
The fixed effects regression by team and year intended to look at the impact of
foreign players on winning percentage with regard to their teams over time. I found that
foreign players in the top 5, regardless of attending college, still had a very significant
positive impact within the team, and within the team for a given year. The coefficients on
the foreign players who attended college were all insignificant. Those foreign players
who did not attend college exhibited positive significant results for top5(No) and top89(No) for both fixed effects models.
Taking into account both the standard OLS regressions and the fixed effects
regression, it is clear that foreign players overall have a strong positive effect on teams
winning percentage. Breaking down the foreign players into those who did attend college
and those who did not, we can see that foreign players who come to the NBA straight
31
from international leagues have more success than the ones who attend colleges in the
United States prior to the NBA.
In the final part of my study, I wanted to address the conflicting conclusions
established by Eschker, Perez, and Siegler (2004) and Yang and Lin (2010). As a
refresher, Eschker et al. found that international players are paid premiums relative to
other players with similar statistics for the 1996-1997 seasons. By 2002, these players’
wages decreased to the normal level as executives allocated their resources more
effectively. Yang and Lin provided evidence of salary discrimination of foreign players
between the 1999-2007 seasons. Foreign players received a 13-18% lower salary on
average than U.S. born players because NBA owners were more conservative in payroll
allocation. I decided to look at the salary gap just for foreign players in the prime of their
career, as opposed to including all foreign players like the two previous studies.
The OLS regression, with the log change in salary as the dependent variable, had
results supporting Yang and Lin. The log change in salary was taken at free agency for all
players under the age of 31. This marked the difference between the salary in the year
prior to free agency and the salary in the year post-free agency. I witnessed a positive
coefficient of .508 on foreign, which suggests that foreign born players have a higher
change in salary than American born players. The gap is due to undervaluation of foreign
players upon their entrance into the NBA.
The implications of this study are that the NBA will continue to become more and
more globalized. If a significant amount of foreign born players can impact a team’s
winning percentage, NBA executives will continue to shift their draft preferences even
further towards foreign players. A future study can look into the reasons behind the
32
increase of foreign players into the NBA, with possible factors being increased scouting,
a sense of false entitlement held by American players, and increased international skill
development. As international players continue to shine in the NBA and the numbers rise,
it will be interesting to see if a salary gap remains or if foreign players will eventually be
valued properly for their first contract.
33
VII.
Data Appendix
A: Effect of Foreign Players on Winning Percentage in NBA
OLS Regression
(Dependent Variable is winning percent; standard deviations are in parentheses)
Independent Variable
All Foreign Players
Top 5
Top 6-7
Top 8-9
log_payroll
Std. Deviation Salary
Coefficient
5.375***
(1.017)
2.47*
(1.484)
2.326*
(1.236)
18.802*
(10.954)
0.00000109
(0.000000893)
Star
9.235854***
(1.112)
Height
0.5374934
(1.113)
3.935537
(10.318)
-0.039
(0.189)
-0.8167253*
(0.458)
1405.801
(932.609)
Age
Age^2
Year
Constant
Foreign (College and No College)
Top 5 (No)
6.16684***
(1.171)
1.747616
(1.883)
4.352384***
(1.483)
3.413752**
1.656)
3.07766
(2.067)
-0.518
(1.977)
19.406*
(10.867)
0.00000104
(.000)
9.266***
(1.109)
0.0891
(1.122)
7.3003
(10.314)
-0.102
(0.189)
-0.795*
(0.455)
1348.961
(926.868)
Top 6-7(No)
Top 8-9(No)
Top 5(Coll)
Top 6-7(Coll)
Top 8-9(Coll)
log_payroll
Std. Deviation Salary
Star
Height
Age
Age^2
Year
Constant
*Significant at .10 level
**Significant at .05 level
*** Significant at .01 level
34
B: Effect of Foreign Players on Winning Percentage in NBA
Fixed Effects Regression by Team and Year
(Dependent Variable is winning percent; standard deviations are in parentheses)
Independent Variable
Coefficient
All Foreign Players
3.747788***
Top 5
(1.409)
1.277908
Top 6-7
(1.681)
2.093638
Top 8-9
(1.469)
11.29108
log_payroll
(16.263)
0.00000108
Std. Deviation Salary
(0.000000104)
10.1870***
Star
(1.286)
-0.183
Height
(1.322)
6.839
Age
(11.003)
-0.108
Age^2
(0.201)
-148.103
Constant
(204.380)
Foreign (College and No College)
4.049**
Top 5 (No)
(1.762)
1.159
Top 6-7 (No)
(2.146)
3.975**
Top 8-9 (No)
(1.777)
3.230
Top 5(Coll)
(2.189)
1.961
Top 6-7(Coll)
(2.272)
-0.590
Top 8-9(Coll)
(2.269)
10.313
log_payroll
(16.273)
0.00000105
Std. Deviation Salary
(0.00000104)
10.121***
Star
(1.289)
-0.296
Height
(1.330)
9.373
Age
(11.130)
-0.154
Age^2
(0.204)
-166.788
Constant
(204.778)
*Significant at .10 level
**Significant at .05 level
*** Significant at .01 level
35
C: Effect of Foreign Players on Change in Salary
OLS Regression (standard deviations are in parenthesis)
Independent Variable
Free agents with age<31
Coefficient
Dep: Log change in salary
Foreign Dummy
College Dummy
Dep: Log post-year salary
.508*
.153
(.267)
(.136)
.351
-0.110
(.242)
(.147)
Foreign * College
-.348
-0.165
(.354)
(.215)
Games Played
0.005
0.002
Games Started
Minutes Played
Points
Field Goal Pct
Three Point Pct
Free Throw Pct
Offensive Rebounds
Defensive Rebounds
(0.005)
(0.003)
0.001
-0.003
(0.004)
(0.003)
0.013
-0.001
(0.024)
(0,018)
0.007
0.0927***
(0.030)
(0.020)
5.070***
2.429
(1.908)
(1.551)
0.320
0.057
(0.603)
(0,442)
0.554
0.082
(1.036)
(0.870)
-0.073
0.060
(0.144)
(0.095)
-0.0000293
0.060
(0.084)
(0.057)
0.065
0.0891*
(0.071)
(0.053)
Assists
Steals
Blocks
Turnovers
0.238
0.024
(0.199)
(0.155)
0.109
0.195
(0.167)
(0.127)
-0.399*
-0.010
(0.217)
(0.142)
Personal Fouls
-0.072
-0.129
(0.139)
(0.095)
Height
-0.043
0.042
(0.035)
(0.028)
0.001
-0.004
Weight
(0.004)
(0.003)
Age2
-0.062
-0.277
(0.511)
(0.298)
Age^2
-0.002
0.005
(0.006)
0.0519*
(0.010)
Year
-0.051
Constant
*Significant at .10 level
** Significant at the .05 level
***Significant at the .01 level
36
(0.040)
(0.028)
106.501
-90.346
(80.774)
(56.11)
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37