Does an Agent Make a NBA Star Rich?
By: Wayne M. Croley
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INTRODUCTION
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LITERATURE REVIEW
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THE BUSINESS OF THE NBA
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Economic Characteristics of the League
Winner Take All
The Cartel vs. The Union
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Putting It All Together: Why Players Make Millions
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THE ECONOMETRIC MODELS
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Model 1: Salaries and Player Performance
Dependent Variable: Player Salaries
Performance Explanatory Variables:
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Model 1 Results: Salaries and Player Performance
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Model 2: Sports Agents vs. Other forms of Representation:
Dependant Variable: Player Salaries
Performance Explanatory Variables
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Model 3: Highly Regarded vs. Ordinary Representatives
Why Might it Matter: The Teams’ Problem
The Players’ Attempt at a Solution: Hire a Representative to send a Signal
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REVIEW OF MODELS 2 & 3
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Model 2: Sports Agents vs. Other Forms of Representation Results:
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Model 3: Highly Regarded vs. Ordinary Representatives Results:
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Lessons from the Three Models:
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FUN WITH THE RESIDUALS:
Which Players Should Ask For a Raise: The Best Values in the NBA
Best Values in the NBA, Accounting for the Maximum Salary Restrictions:
Players Who Should Be Most Happy with their Contract:
Players with Big Contract-Extensions to Sign in the Future: Rising Stars
The 2006 Free Agent Class
TEAM INVESTMENT: WINS AND REVENUE
Model 1: Payroll and Wins:
Dependant Variable: Win %
Independent Variables
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Determinants of Winning Results:
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Model 2: Payroll and Revenue:
Dependant Variable: Total Revenue
Independent Variables:
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Determinants of Annual Revenue Results:
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Lessons from the Two Team Models:
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KEY CHANGES IN THE 2005 CBA: A PRELIMINARY ASSESSMENT
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2005-2006 RESULTS:
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Model 1: Sports Agents vs. Other Forms of Representation Results:
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Model 2: Highly Regarded vs. Ordinary Representatives Results:
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HAS 2005-2006 CHANGED THE UNDERLINING RELATIONSHIPS?
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Model 1: Sports Agents vs. Other Forms of Representation Results:
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Model 2: Highly Regarded vs. Ordinary Representatives Results:
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COMPARE THE CBA’S:
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Model 1: Sports Agents vs. Other Forms of Representation Results:
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Model 2: Highly Regarded vs. Ordinary Representatives Results:
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LIMITATIONS OF THE MODEL
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CONCLUSIONS:
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ACKNOWLEDGEMENTS:
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BIBLIOGRAPHY
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Abstract
National Basketball Association (NBA) players earn, on average, $4
million a year. Most players are represented by an agent or attorney who
negotiates salaries and benefits on behalf of their clients. In this thesis I evaluated
the effectiveness of these negotiators by evaluating the NBA collective
bargaining agreement (CBA) and developing a dataset that allowed me to
examine the factors that explain player salaries. I conclude that revenue sharing
and the NBA CBA allow players to earn high salaries, and that a player's choice
of agent does not usually affect level of salary. However, representatives with a
good reputation tend to have a greater affect on their clients' salaries than
representatives with lesser reputations. I find that variation in player salaries is
best explained by variation in their performance. I also find that star players
receive very large salaries because team owners believe they generate fan
excitement and additional revenue.
Introduction
Former NBA player Latrell Sprewell had a checkered career. Sprewell
was selected late in the 1st round of the 1992 NBA Draft by the Golden State
Warriors, and as a Warrior, flourished and became one of the NBA’s best guards.
In 1997 Sprewell was suspended for 68 games after he strangled his coach P.J.
Carlesimo during a practice. Sprewell did not have many supporters, but one of
his supporters was Arn Tellem, a powerful agent. Tellem had negotiated a 4
5
year $32 million contract for Sprewell, and after Sprewell’s suspension was lifted,
the Warriors immediately traded him to the New York Knicks (NC Hoops).
Sprewell switched to sports agent Robert Gist to negotiate his next contract and
in 1999 got a 6 year $61.9 million contract-extension (NC Hoops). Sprewell
played well during his six seasons with the Knicks, including one year where he
led them to the NBA Finals. In the summer of 2003, when Sprewell had two
years remaining on his contract, the Knicks traded him to the Minnesota
Timberwolves, where he sought a new contract with the team.
Sprewell complained during contract negotiations with the Timberwolves
at the beginning of the 2004-2005 Season, “Why would I want to help them
(Minnesota] win a title?''…They're not doing anything for me. I'm at risk. I have a
lot of risk here. I got my family to feed”… (Youngblood, 2004). Sprewell and
Gist believed that the 3 year $27 million dollar contract-extension offered by the
Timberwolves was below Sprewell’s true market value; they calculated that he
would receive better offers once he becomes a free agent. Sprewell entered the
free agent market, but there was not much demand for a 35 year old swingman
whose skills were declining, and today he is without a contract.
Sprewell’s complaint alienated many Americans who believe professional
athletes are overpaid and greedy. Sprewell and other NBA player salaries place
them in the top 1% of all income earners in the U.S. NBA players are both
athletes and entertainers who help their team win and generate millions of
dollars in revenue for their team and billions for the league. The entertainment
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aspect of NBA games should not be underrated. Dallas Mavericks owner Mark
Cuban argued that the NBA is one of many entertainment options for people
when he wrote,
“Reality is that basketball is not the business of the NBA. Entertainment is
the business of the NBA. Every single night of the week we battle movies,
books, restaurants, TV and Cable programs, talking a walk, everything
and anything that is an alternative to going to or watching an NBA game”
(Cuban, 2005).
Like many entertainers, most NBA players are represented by someone
who negotiates deals for them. Some of these representatives are able to
negotiate large contracts, which lead us to ask these questions: Why are NBA
player representatives able to negotiate, on average, for their clients multimillion
dollars salaries per season? Given that many of these representatives are sports
agents; does an agent have a greater affect on their clients’ salaries than other
representatives?
In this thesis I will explain why NBA players earn so much money and
analyze the qualities of certain types of player representatives, including their
relative impacts on player salaries. I will also answer why NBA team owners are
willing to pay their players multimillion dollar salaries since players could not
make millions without their cooperation. I will also examine the structure of the
NBA business and compare the impact of player representatives versus
performance and non-performance factors that affect player salaries.
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Literature Review
Within the NBA, there is a wide disparity between the highest and lowest
paid players, as shown by Cook and Frank’s (1995) book, The Winner Take All
Society. Rosenbaum (2003) argued that the introduction of a rookie salary pay
scale in 1995 penalized non-veterans, young players and transferred large rents
to veteran players. As a consequence, rookies became underpaid and veterans
became overpaid relative to their performance. Groothuis and Hill (2001)
acknowledged the skewed distribution of player salaries, but also noticed a
recent decline in this disparity because lower-paid players received the greatest
relative salary gains from 1999 NBA Collective Bargaining Agreement (CBA).
Studies that seek to find determinants of a NBA player’s salary focus on
performance and non-performance factors. Early studies asked whether there
was wage discrimination among African-American players (Kahn and Sherer
1988, Dey 1997). Siegler (1997) was one of the first to focus on the impact of
player performance on salaries, testing three performance measures, not
including past performance. Siegler did not include qualitative characteristics
such as a player’s agent, but noted that they may have a significant impact on a
player’s salary. Eschker, Siegler, and Perez (2004) suggested that foreign players
lost their salary premium as NBA teams learned to better project how a player
will perform in the NBA. Stiroh (2004) suggested that both owners and players
recognized that player performance influences player salary. Since players
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recognize this, they have an incentive to increase their effort in the final year of
their contracts so they can obtain a larger contract at the end of the season.
Agesa, Agesa, and Toshkova (2005) evaluated salary determinants for players at
different positions, and found that teams pay players at certain positions to do
specific things.
Rosen and Sanderson (2001) argued that athletes are paid more than
teachers because they serve a much larger audience. However, their analysis
was directed towards sports in general and did not account for the complexities
of the NBA’s salary structure. The literature provides some clues to performance
and non-performance factors as determinants of an NBA player’s salary, but
does not analyze the impacts of player representatives. In this study I will close
some gaps in the literature by investigating if NBA player representatives have
any impact on their clients’ salaries, if players earn more when they are
represented by sports agents, and if owners receive a return for spending more
money on their players.
The Business of the NBA
Basketball is the second most popular of the four major sports in United
States and arguably one of most popular sports in the world. Professional
basketball is a segment of the $213 billion sports industry, which is more than
twice the size of the US auto industry (The Sports Industry). In the 2004-2005
Season the NBA generated $3.037 billion in revenue, which placed it third among
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professional sports leagues in the US (Coon, 2005). Each season the NBA
sanctions several exhibition games, an 82 game regular season, an All-Star
Weekend, and a nearly two month long postseason where playoff teams compete
for the NBA Championship.
The NBA is a marketing machine that promotes the game of basketball
along with its brand name. The NBA promotes individual players and individual
match-ups between superstars, which makes the league more dependent on the
quality of its star players than the National Football League (NFL), which
promotes teams rather than individuals. The NBA thrived when its top player
was Michael Jordan, but lost some of its popularity after he retired because the
next generation of stars did not have Jordan’s appeal to the public. The league’s
public image took a serious hit in December 2004 when several players charged
into the stands to attack fans during a game. The media condemned the event
and spotlighted other recent transgressions committed by NBA players. The
NBA has tried to counter the negative press with public service announcements,
ads highlighting charitable works by NBA players and teams, and a promotional
campaign featuring celebrity entertainers who explain why they are big fans of
the NBA. The league’s most recent effort to improve its public image was to
impose a mandatory dress code on its players.
Economic Characteristics of the League
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Winner Take All
The NBA is a classic example of Cook and Frank’s (1995) “winner take all
market”. Winner-take-all-markets occur where top performers earn a
disproportionate amount of income, and are common throughout professional
sports, the entertainment industry, and the corporate world. The top performers
in these markets often have incomes so high that they attract other people to the
industries where these markets exist (Cook and Frank 1995 4).
Three notable characteristics distinguish the NBA as a winner-take-allmarket: a large portion of player salary in the NBA is concentrated in the hands
of the highest paid players, free agency serves as a mechanism for rising stars to
convert their potential and increased production into larger contracts, and
players are judged by relative performance.
Uneven Salary Distribution
The salary distribution of an NBA roster is typically uneven. In most
cases, most of a team’s payroll is in invested in the team’s top players, so that a
star player can make more than his teams’ entire bench or have a salary that is
nearly one-third of the league’s designated salary cap for the team. In the 20042005 Season Seattle Supersonics’s guard Ray Allen earned $14,625,000, 27% of the
team’s $54,184,357 payroll and more than the combined salaries of the Sonics’s
nine lowest paid players! While the salary of the highest paid player on a team
occupies a large portion of payroll, the rest of the players often make far less;
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sometimes only a 10th as much. Allen’s salary was over seven times greater than
the median salary on the Supersonics.
Free Agency
Free agency provides players with an opportunity to benefit from the
demand for their services. Since only twelve players are eligible to play each
game and five players can play at one time, each player on a team’s roster has a
role in determining a team’s success. The most dramatic example of a couple of
players’ importance to a team is illustrated by the Phoenix Suns’s acquisition of
guards Steve Nash and Quentin Richardson in the 2004 off-season. After a 29-53
record, Phoenix signed Nash and Richardson in free agency, and their record
soared to 62-20 the next season, the third largest improvement in NBA history
(Beacham). With a long 82 game season, a team needs roster depth to rest its
starters during games and fill in for its injured players. When the playoffs arrive,
a team has to rely on its starters to play most of these game and its superstars to
win games.
The limited number of roster spots available and the need for skilled
players compels teams to find ways to acquire them. NBA teams have three
ways to acquire a player: trade, draft, or sign them in free agency, the preferred
way for teams to acquire players. Free agency helps a player in two key ways: a
player who is about to become a free agent has increased leverage in contract
negotiations since the team runs a risk in losing the player to another team.
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Second, if a player becomes a free agent they will have the opportunity to receive
bids and go to the team that gives them the best offer.
Stars and rising stars entering the free agent market often receive large
contracts. Since stars are hard to develop and obtain from other teams, a team
with an opportunity to lure one is willing to pay more money than they would
for an average player. Average and below average players in the league often
have to accept significantly lower salaries because most teams at the start of free
agency have their payrolls at or above the salary cap. This limits the amount of
money most team can offer, so most players have to accept small salaries since
most of the money will go towards any available stars. In addition, average and
below average players are subject to the forces of supply and demand. There is
often a large supply of these types of players available for teams to sign because
most players do not have the talent stars have (and players that do have this
talent often do not live up to their potential). As a result, these players can only
exhibit a small amount of market power.
Performance is Relative
No two basketball players are the same, so teams have to compare players
to find those who best fill their needs. Teams always seek improvement, no
matter how good they are. The San Antonio Spurs won the 2005 NBA
Championship and were considered the favorites to win the 2006 championship
even if they did nothing in the off-season. Nonetheless, the Spurs sought to
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improve themselves by signing prolific scorers Nick Van Exel and Michael
Finley.
Teams use several measures to compare specific players they need and
want. In addition to physical measures such as height and weight, teams can
also use performance statistics to quantify a player’s productivity. Teams can
use both physical and performance measures to gauge a player’s worth to their
team (or to another team if they wanted to trade for them). Each team has their
own specific way to evaluate a player’s value, but each team does assign higher
salary values to players with desirable physical characteristics, high levels of
productivity, and position of need. Once a team determines their reservation
price for a player they can offer the player a contract in the free agency or re-sign
the player as long to a salary equal to or less than their reservation price.
Players also use relative performance to justify their contract demands.
Players generally desire to be paid at least as much as their peers. One recent
example was former Washington Wizards’s guard Larry Hughes. Hughes spent
the first seven years of his career developing gradually. In his final year with the
Wizards, Hughes demonstrated that he was a rising star by averaging over 22
points per game and being voted onto the All-NBA Defense 1st Team.
Season
2004-2005
Player
Larry
Hughes
Points/Game
22.0
Rebounds/Game
6.3
Assists/Game
4.7
Steals/Game
2.9
The Cleveland Cavaliers aggressively pursued Hughes in free agency to give
young superstar LeBron James a sidekick. Hughes reportedly used his teammate
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Gilbert Arenas, who had comparable performance statistics, as an indicator for
what he should be paid (Lee, 2005). The Wizards had Arenas under contract for
6 years $65 million, which Hughes hoped to receive or he would leave.
However, the Wizards only offered Hughes a 6 year contract for only $54
million, which upset him (Lee, 2005). Hughes left the Wizards to join the
Cavaliers when they offered him a contract for 6 years $65 million.
The Cartel vs. The Union
The Cartel
The early days of professional basketball began with unsuccessful
professional basketball leagues until two of them merged to form the NBA. In
1898 the National Basketball League (NBL) became the world’s first professional
basketball league and included teams in Pennsylvania and New Jersey
(Basketball History). Other leagues formed throughout the Northeast in the
early 20th century, but none were very successful (Basketball History). Between
the mid 1930’s and mid 1940’s two important leagues that would shape the
future of professional basketball formed. The first league adopted the old
National Basketball League name and second league was called as the Basketball
Association of America (BAA) (Basketball History). The NBL and the BAA were
the two dominant professional leagues in the late 1940’s. These two leagues
competed with one another for top players, which allowed some of these players
to earn large salaries (Bradley, 2005). However, most players only earned $4,000
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to $5,000 per season. In 1947 the NBL and BAA merged to form the NBA, which
became the only viable professional league for players to play in.
The new league faced two great challenges during the first three decades
of its existence: contraction and competition from a rival league. The NBA began
with 17 teams, but fell to 11 teams in its second season and 8 teams by 1957
(Staudohar, 1986 91). During this period, fledgling teams faced financial
problems and many were forced to fold (Rosner, 2002). The American Basketball
Association (ABA) was created in 1968, and aimed to attract fans away from the
NBA by expanding into as many non-NBA cities as quickly as possible
(Staudohar, 1986 92). In addition to competing with the NBA for fans, the new
league increased the competition for players and salaries rose. ABA teams
attracted top collegiate players with lucrative contracts and forced NBA teams to
do the same. For example, the ABA’s Denver Rockets signed junior college star
Spencer Haywood a six year $1.9 million contract, but after failing to restructure
his contract, Haywood left the Rockets to sign a six year $1.5 million contract
with the NBA’s Seattle Supersonics (Friedman, 2005).
The competition between the NBA and ABA hurt both leagues’ financial
well-being as increased player salaries drained the profitability of many NBA
teams while the ABA (as a league) lost money (Staudohar, 1986 93). In 1977 the
ABA merged with the NBA after several years of negotiations and player
resistance in court. Four of the six surviving ABA teams were allowed to join the
NBA while players from the other two teams were distributed to NBA teams
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through a dispersal draft (Friedman, 2005). The ABA’s impact was dramatic for
NBA player salaries as the salary of an average player rose from $35,000 in 1970
to $180,000 by 1980 (Friedman, 2005).
A long period of growth and prosperity for the NBA began in the 1980’s.
The NBA grew popular with the rise of individual superstars like Michael Jordan
and the numerous appearances by the Los Angeles Lakers and the Boston Celtics
in the NBA Finals. In the 1990’s the league gained more popularity as Jordan’s
Chicago Bulls became one of the best dynasties in professional sports history.
The league’s increased popularity resulted in a large influx of new revenue for
the league, including from major television contracts that they had with NBC
and Turner worth over $1 billion (NBA TV Contracts). However, many
television games were not broadcasted during the 1998-1999 Season, including
the All-Star Game, due to a nearly six month lockout. The 1998-1999 Season was
saved at the last moment and the league held a truncated 50 game season.
Since the 1998 lockout the NBA has worked to regain the popularity that
had earlier. The NBA has attempted to rebuild its image with its fans through
various marketing campaigns, lowering ticket prices, and increasing fan access to
players. The NBA has managed to recover much of its U.S. fan base while
attracting more fans from other parts of the world, which has helped to increase
league revenue. The league achieved one great success in 2002 when Disney and
Turner signed separate television contracts that resulted in the league receiving
$4.6 billion over the next six years (NBA TV Contracts). In the 2004-2005 Season
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a cloud of uncertainty reigned over the league as another lockout appeared very
likely at the end of the season. The owners sought to reduce the share of revenue
the players would receive and the maximum length of contracts from seven
years to six years. The players wanted to maintain the status quo and stated they
were prepared to endure another lockout. When it appeared that a lockout was
certain the players and owners unexpectedly agreed to a new CBA that
maintains the status quo.
The NBA is a competitive, 30 team league that resembles a cartel rather
than a monopoly. Unlike the Major League Baseball, the NBA is not immune to
antitrust laws (Staudohar, 1986 101), and has domestic competition. The United
States Basketball League (USBL), the Continental Basketball Association (CBA),
and the American Basketball Association (ABA) compete with the NBA as
professional basketball leagues in the United States, but these leagues are very
small compared to the massive strength of the NBA. The best players in the
world dream of playing in the NBA, so the NBA does not have to compete with
the USBL, CBA, and ABA for quality players. Rosenbaum (2003) accurately
describes other characteristics that make the NBA a cartel: “the league seeks to
maximize its profits through limiting output (in this case the number of teams)
and through reducing costs in which it exercises market power (such as in the
market for player talent)”. The NBA is careful and selective when it decides to
add teams, forming an expansion committee to assess the viability of adding a
team in a candidate city. Ultimately the NBA Board of Governors, the league’s
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executive body, votes whether to approve the committee’s recommendation to
award a candidate city a NBA franchise.
The market power the league exercises is exhibited when it is time to
negotiate a new CBA. Team owners put their differences aside and negotiate as
one unit to offset the power of the union. However, teams in the league do not
openly collude with one another in the player’s market. Teams have an incentive
to cheat in the market for players since the team that gives the best offer often has
the best chance to acquire the player they need and weaken another team at the
same time. In addition, the CBA prohibits teams to collude with one another in
regards to negotiating deals for players, etc. The league maintains its
competitiveness by trying to provide an equal playing field for all its teams. It is
important for the NBA to have competitive teams in all its markets; not only to
ensure competitive balance, but also to ensure the economic vitality of its teams.
In its attempts to ensure an equal playing field, league representatives
(Commissioner, etc…) bargain on behalf of the owners. League representatives
seek to create a salary structure that minimizes player costs for owners and
allows for fair competition amongst its teams.
The Union:
The players organized themselves into a labor union, though their union
was weak during its early stages. In 1954 Boston Celtics guard Bob Cousy
organized the National Basketball Players Association (NBPA), but the league
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was reluctant to negotiate with this new union or even recognize them. It was
not until the 1964 Season when the players threatened to not play in the first ever
televised NBA All-Star Game that the league recognized the union. The NBA
owners recognized the NBPA as “the exclusive collective bargaining
representative of all NBA players” (NBPA History).
The NBPA is a democratic institution in which each team has at least one
player representative who “votes on policy issues and can also serve as a liaison
between the union and his teammates” (NBPA FAQ). The NBPA is headed by
an executive committee, which “makes certain decisions in the operations of the
union” (NBPA FAQ). Though the NBPA provides many services for its
members its primary role is to serve as the sole negotiator for the players when it
is time to negotiate a new CBA. In contrast to the league, which seeks to
minimize player cost, the union seeks to maximize rents, that is, “get the best
deal possible for all players” in CBA negotiations (NBPA FAQ). Players are
involved during the negotiations process along with other union officials. Once
an agreement is reached between the league and union representatives the
agreement is subject to a vote of approval by all the players. The union and
owners have had several series of confrontations throughout the history of the
NBA.
The 1967 CBA
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In 1967 the owners and the NBPA agreed to the first collective bargaining
agreement in sports (Staudohar, 1999). This CBA dealt primary dealt with the
issue of player’s pensions, which the owners caved into the player’s demands
(Staudohar, 1986 98).
The 1976 CBA
In the 1976 CBA the owners and players agreed to an early form of free
agency. Before this agreement, teams had monopsony power over their players
because players were obligated to their team even when their contract was up.
Under the new CBA, players would no longer be restricted to one team
throughout their entire career, which gave them much greater bargaining power.
Teams that signed players from different teams had to give compensation to the
player’s former team, but this practice was abolished in 1980 (Staudohar, 1998).
Also in 1980 teams were given the right of first refusal for their free agents, so
that teams could match offers made by other teams as long as they were within a
certain dollar range.
The 1983-1988 and 1988-1995 CBA’s
The league was in trouble in the early 1980’s when many teams faced a
financial crisis. Some teams had incurred large losses while others had to defer
payments to players to stay afloat (Bradley, 2005). In one season, teams incurred,
on average, $700,000 in operating losses (Staudohar, 1986 109). The danger was
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that many small market teams would fold, which for the players was also a
problem because many player jobs would be lost. The owners and players had
an opportunity to save the league when it came time to negotiate a new CBA in
1982. The owners wanted to impose a salary cap that would restrain the growth
of player salaries. The players resisted the owners’ demands because they
wanted to sustain salary growth. One day before a player imposed deadline in
1983, the owners and the players hammered out a landmark CBA; under which
the owners agreed to share league revenues with players in exchange for a salary
cap (Staudohar, 1999). Players were guaranteed between 53%-57% of league
revenue through a salary cap, revenues that included “gate receipts, local and
national television, radio revenue, preseason and postseason revenue, and
(some) licensing revenue” (Bradley, 2005).
One problem with the new salary cap was that some players had salaries
near or above the initially set salary cap (Staudohar, 1986 110). The league
settled this problem by creating a rule that would allow teams in some situations
to exceed the salary cap in order to re-sign their own player, and such salaries
would not count against the salary cap. This rule became known as the Bird
exception after the Boston Celtics used this exception to re-sign Larry Bird. This
exception allowed teams to retain their own free agents and allowed the player
to benefit from the free agency process. At this time there was no restriction on
the amount of money a Bird exception player could sign for.
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The 1988 CBA maintained much of the status quo in the NBA. The share
of gross revenues that the players were to receive remained the same. The one
major change from this new agreement was a reduction of the NBA Draft from
seven rounds to two rounds (Straudohar, 1998).
The 1995-1998 CBA
Labor problems appeared in the NBA as the owners instituted a lockout of
its players during the 1995 off-season. With help of sports agents, star players
such as Michael Jordan and Patrick Ewing led an effort to decertify the union.
However, Jordan and Ewing’s efforts were unsuccessful and the union agreed to
a new CBA. The players and owners agreed to a new 6 year CBA in the summer
of 1995, but the owners had an option after the third year to terminate the
agreement if basketball related income rose to a specific level (Staudohar, 1999).
One important innovation in this CBA was the introduction of a rookie salary
pay scale to cap rookie salaries. The scale is a graduated where the higher the
player is drafted, the more they are paid, but much less than Glenn Robinson’s 11
year $80 contract (Rosenbaum, 2003). A player had to honor their rookie scale
contract for two years before they could gain restricted free agency status
(Rosenbaum and Stein, 2003). Another important change limited the rate of
salary growth for a Bird exception player to 12.5% each season.
The 1999-2005 CBA
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After the 1995 CBA, total salaries continued to grow, so that by the 19971998 Season the players received 57% of basketball related income.
Disagreement between players and owners nearly cost the NBA the 1998-1999
Season. Owners wanted to install a hard salary cap like that established by the
NFL and reduce the amount of basketball related income received by the players.
The players wanted to defend their share of basketball related income and resist
a hard salary cap. On January 6, 1999 the owners and players reached an
agreement over a new CBA. The new CBA created a tougher salary cap,
restricted maximum salaries, increased minimum salaries, increased the
maximum length of a rookie contract, created new salary cap exceptions, and
introduced a complicated luxury tax system and an escrow (tax) system.
Rookie Contracts Extended:
A rookie contract was extended to three years and included a team option
in the fourth year.
Salary Exceptions: Midlevel, Minimum, Veteran:
Salary exceptions permitted teams at or above the salary cap to sign
players. Each team had an unlimited number of minimum salary exceptions,
which allowed a team to sign a player to a minimum salary. The minimum
salary is determined by the player’s experience in the league and rises with
experience. Each team above the salary cap was also entitled to a mid-level
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exception each season, which allowed them to pay one player the average salary.
The average salary is not necessarily the mean salary found in the NBA but a
salary derived from the formula below:
(∑ Total Salaries Paid) x 1.08
----------------------------------------------------------# of Teams x 12.5
Source: Coon 1999
Finally, each team at or above the salary cap also could receive a veteran
exception once every two years.
Maximum Contracts:
After contracts began to rise out of control, the league was able to
negotiate a cap on maximum player salaries. Players signed with the use of the
Bird exception in the future could no longer receive any salary they negotiate
with a team. The maximum salaries that a player could receive each year were
negotiated by the owners and the union with salaries rising with experience.
Luxury Tax:
The 1999 CBA introduced a luxury tax after an agreement between the
players and owners crumbled in 1995. The luxury tax served as a penalty to
teams with large payrolls and was triggered whenever player compensation
exceeded a certain level of league income (Coon, 1999), which was determined
after the end of each season by the league. If the league determined that player
25
compensation exceeded a certain level of income they then calculated the luxury
tax threshold and taxed all teams above it (Coon, 1999). Teams just above the
threshold were taxed a percentage that they exceeded the threshold by while
teams far above the threshold were taxed the exact amount (Coon, 1999). For
example, if the New York Knicks had a payroll that exceeded the luxury tax
threshold by $30 million they would owe the league $30 million in taxes. The
uncertainty about the tax, the threshold, and the potential for a severe, financial
penalty made most team owners cautious about having payrolls above the salary
cap. Teams under the luxury tax threshold received a 1/29 share of the proceeds
from the tax while teams nearby received a smaller portion (Coon, 1999).
Enter the Player Representatives
Player representatives had little practical value until the reforms of the
1976 CBA were implemented. Before the 1976 CBA, a team owned player rights,
which limited their options to play for the team or defect to the ABA. This
limited mobility significantly hampered the leverage players and agents had in
contract negotiations. Team owners had so much leverage that sports agents like
Bob Woolf had to negotiate one of his client’s contracts via telephone outside an
owner’s office (Povtak, 1995). With the introduction of limited free agency, an
agent’s negotiating skills became useful to players since they had increased
leverage in contract negotiations. The demand for agents rose so that less than
25 years later, almost every NBA player is represented by an agents.
26
The NBPA considers any representative that negotiates a contract on
behalf of a player an “agent”: “Persons who wish to represent NBA players in
individual contract negotiations must comply with the Regulations and become
certified as an NBPA Player Agent before they are eligible to participate in such
negotiations” (NBPA Regulations). As of 2001 there were 350 player
representatives certified by the NBPA, but less than a hundred had clients
(Shropshire and Davis, 2002 15). The most dominant NBA player representation
firm has been Washington D.C. based sports agency SFX, which at one time was
estimated to have nearly 1/6 of the NBA’s players as clients (Shropshire and
Davis, 2002 40). SFX boasts among its large list of clients some of the NBA’s best
players, including Mike Bibby and Elton Brand. Agencies like SFX gain many of
their clients through word of mouth from satisfied clients (Shropshire and Davis,
2002 36). However, the ultimate determinant of whether a player will keep an
agent is if the agent and agency provides the player valuable services.
Many players hire agents because they provide the most “valuable
services for athletes who are enmeshed in increasingly complex business
activities on and off the field” (Shropshire and Davis, 2002 20). The needs of an
individual player depend on numerous factors, including a player’s desire to
have all their financial needs serviced by one firm. Recently a few sports
agencies have begun to offer clients a variety of services. Two superpower
sports agency, SFX and Octagon, now offer their clients one stop shopping for all
of their clients’ needs (Shropshire and Davis, 2002 29). Meanwhile, smaller
27
agencies have struggled to match the services provided by larger agencies.
Many small agencies have been bought out by larger agencies while others have
merged with one another so they can achieve economies of scale.
A recent phenomenon in the NBA has been the rise of attorneys that
negotiate contracts for players. The most prominent attorney representative is
Williams and Connolly LLP’s Lon Babby who has star clients such as Tim
Duncan, Grant Hill, and Ray Allen. Unlike traditional sports agents, attorney
representatives charge hourly fees to their clients. An agent that negotiates a 5
year $10 million dollar may receive $80,000 per year in fees, while an attorney
that negotiates the same contract in 60 hours at $500 an hour would charge a one
time fee of $30,000! Though attorney representatives may be able to replicate
many of the same services as agents at a lower cost, players have been reluctant
to switch to them. Players instead continue to show loyalty to traditional sports
agents who are under the same restrictions as attorneys.
The NBPA places many restrictions on player representatives. First, the
union permits player representatives to negotiate the salaries of its clients as long
as the union delegates salary negotiation responsibilities to its member-players
(NBPA Regulations). To protect players who want a representative, an NBPA
committee screens potential representatives and regulates current
representatives. Current representatives must comply with audit requests and
provide the auditor “all relevant books and records relating to any services
provided” to their client (NBPA Regulations). The NBPA restricts
28
representatives from charging more than 4% of the value of a client’s contract in
fees (NBPA Regulations), and representatives for players who make the league
minimum can receive only a $2,000 per season (NBPA Regulations). Though
player representatives’ activities are regulated by the union, they still are able to
make their presence felt within the union.
Player representatives attempt to indirectly influence the outcome of CBA
negotiations through communication with their clients. During the 1995 CBA
negotiations, for example, union leaders and owners agreed to a luxury tax and
hard salary cap, but agents convinced many players to vote against the tentative
agreement; causing it to be rejected and leading to a lockout (Heisler, 1995).
Agent David Falk, who represented superstars Michael Jordan, Alonzo
Mourning, and Patrick Ewing wanted to decertify the union. Falk’s ploy caused
Donald Royal, a vice president of the NBPA at the time, to complain that the
“agents are trying to dictate policy and business in our league…it’s almost scary
to think that the agents could be in control” (Povtak, 1995).
In the CBA negotiations after the 1997-1998 Season, sports agents became
extremely concerned about the owners’ attempt to restrict maximum salaries. If
maximum salary restrictions were in place top players would not require a
traditional agent to negotiate their contract. Instead these players could hire an
attorney to negotiate the contract or do the negotiations themselves. This would
take away a lot of their future income from negotiating mega deals like Michael
Jordan’s 1 year $33 million contract and Kevin Garnett’s 6 year $126 million
29
contract-extension. During the six month long 1998 NBA lockout, agents
attended bargaining sessions between the owners and players and were accused
of “having corrupted the negotiating process (Bruton, 1999)”… The agents lost
the battle in 1998, but are still able to attract clients and get them, on average,
multimillion dollar salaries.
Putting It All Together: Why Players Make Millions
NBA team owners participate in a revenue sharing program with their
players and this enables players to earn multimillion dollar salaries. The method
in which the owners distribute league revenue is spelled out in the NBA CBA.
Since the landmark salary cap of 1983, the CBA has described the process
of how league revenue is distributed each season. The key issue of every CBA
negotiation since 1983 pertains to the distribution of basketball related income
(BRI). Basketball related income is “any income received by the NBA, NBA
Properties, or NBA Media Ventures” ranging from ticket sales to merchandising
(Coon, 2005). The players and owners haggle over the distribution of BRI because
it reflects the amount of league revenue received by each side. The share of BRI
the players are supposed to receive is reflected through the salary cap, which the
league sets and is the same for every team. This share that the players receive
from the owners covers their salaries and their benefits (Coon, 2005). Each
season’s salary cap level is determined by the league’s BRI forecast made before
the upcoming season (Coon, 2005). The proportion of BRI players are supposed
30
to receive is a predetermined percentage that was agreed on by the union and the
owners during CBA talks*. The salary cap is roughly calculated by the following
formula:
(%BRI Share Negotiated x Projected BRI – Projected Player Benefits)
-------------------------------------------------------------------------------# of Teams
Source: Coon 2005
Total player compensation (salaries and benefits) for a given season can easily
exceed the predetermined amount set by the salary cap because the salary cap is
“a soft cap”. A soft cap allows teams to exceed the salary cap through various
salary exceptions. Since many teams have payrolls above the salary cap, the
players receive a greater share of BRI than was negotiated in the CBA. The
figure below shows that the players received a greater share of BRI each season
than was agreed to in the 1999 CBA negotiations.
Season
Negotiated BRI
Actual % of BRI Received by Players†
Salary Cap
1998-1999
X
59%
$30,000,000
1999-2000
48.04
62%
$34,000,000
2000-2001
48.04
65%
$35,500,000
2001-2002
48.04
57%
$42,500,000
2002-2003
48.04
60%
$40,270,000
2003-2004
48.04
57%
$43,840,000
2004-2005
48.04
60%
$43,870,000
Source: NBPA History, Coon 1999, and Bender
The league imposes an escrow tax system on the players, so total player
compensation does not exceed a maximum threshold agreed on by players and
owners. Each season a portion of a player’s salary is withheld in an escrow
*
In the 1998-1999 and 1999-2000 Seasons the salary cap was a fixed number rather than a number derived
from BRI.
†
This percentage is before the reduction of salaries from the escrow tax is taken.
31
account (Coon, 2005). The owners and players negotiated in the CBA a
maximum percentage of BRI that if breached would allow the league to recover
lost revenue from the escrow account. The league has discretion to use any
recovered revenue for specific purposes, which includes returning some of the
money to the owners (Coon, 2005). The amount of money the league extracts
from the escrow account depends on the difference between the dollar amount
that players received in compensation for the season and the dollar amount that
represents the maximum percentage of BRI (Coon, 2005). If total salaries and
benefits exceed the amount of money designated by the BRI percentage, the
league removes money from the escrow account to bring total compensation
down to the maximum threshold, and any remaining funds are returned to the
players (Coon, 2005). If total salaries and benefits do not exceed the amount of
money designated by the BRI percentage the players will have their money
returned to them from the escrow account. This means players do not always
receive the salary that their contract indicates they do. Since the escrow tax
system has been in effect, the players have lost some of their salary each season.
Season
2001-2002
2002-2003
2003-2004
2004-2005
Source: Coon
Actual % of BRI Received by Players
57%
60%
57%
60%
Escrow % of BRI Threshold
55%
55%
55%
57%
The players have agreed to a salary cap in exchange for the guarantee that
they will receive a significant portion of league revenues each season for a few
reasons. The main reason the players are willing to accept a salary cap is that it
32
is an adequate, compromise between a hard salary cap and no salary cap. The
salary cap is an adequate compromise because it often provides players a
majority of league revenues and the exact amount they receive depends on the
leagues success. Since the 1983 CBA negotiations, the owners have proposed a
hard salary cap, which the players have fiercely resisted. The players proposed
in the 1988 and 1995 CBA negotiations to eliminate the salary cap system
altogether, which the owners never considered plausible. Another reason the
players have agreed to a revenue-sharing salary cap is that they know they need
to in order to protect their livelihoods. In the 1998 lockout the players lost
several months of salary in their attempts to resist maximum salary restrictions
and a stronger salary cap. As the lockout continued the players realized they
would never receive an NBA paycheck again until they agreed to the owners’
demands. Financially strapped, lower paid players-most of the players’ unionapplied great pressure on their leadership to accept the owners’ demands. As a
result, the union capitulated to the owners’ demands and the players began to
receive their paychecks again with the end of the lockout.
The livelihoods of the players are also protected with their acceptance of a
salary cap since player jobs have hinged on the cap’s acceptance. NBA history
shows that the total number of player jobs depends on the health of the league.
In the 1950’s and early 1980’s the league struggled financially, and as a
consequence teams folded and were in danger of folding. When the NBA is
financially healthy the league has increased its total number of teams, which
33
results in an increase in the number of player jobs. As a product of league’s
success under a salary cap players have come to accept it as a mechanism that
has brought them increased number of job opportunities, increased job security,
and an adequate share of league revenue. Therefore, it was not a surprise to see
the players union agree to retain a salary cap in the new 2005 CBA.
The owners have agreed to give more half of the league’s revenue to the
players in exchange for a salary cap for several reasons. First, by distributing a
majority of league revenues to the players, owners satisfy the players’ sense of
entitlement to the money they help generate from playing games. Though
owners distribute this money through a soft salary cap, owners realize it is a
viable way to keep salary growth under control. When the league lacked an
effective salary cap salary growth was so out of control that total salaries jumped
from 30% of league revenue in 1967 to 75% of league revenue by 1983
(Staudohar, 1986 99). The relative salary growth control the league gains in their
deal with the players helps them maintain a competitive balance. The salary cap
places a great constraint on large market teams to buy any player they want,
which is a problem in baseball. In baseball large market teams, such as the New
York Yankees and the Boston Red Sox’s can almost sign any free agent they want
while other teams struggle to re-sign their own players. However, unlike in
34
baseball, the NBA has a minimum payroll rule‡ that prevents small market teams
from pocketing a lot of their revenue.
The significant share of league revenues the players receive from the
league gives them an additional interest in seeing the league succeed financially.
Players become concerned about the league’s financial health whenever they feel
it may affect their ability to receive paychecks from their teams. A prime
example of this came in the early 1980’s when the league got major concessions
from the players after the players realized many of their jobs would be lost if
nothing changed. With a salary cap tied into league revenues, players become
large stakeholders in the league’s success and possess a great financial interest in
seeing their league succeed. The more revenue the league intakes the higher the
salary cap will be the following season and therefore, more rents available to
spread among all players. If the league struggles financially players may see
their salary growth slowed or even contract with a reduced salary cap. The
revenue-sharing salary cap gives players a vested interest in the league’s success
while it gives owners increased certainty about their future in the league.
The owners’ commitment to give a large, generally fixed share of league
revenues to the players increases their certainty about future costs. Team payroll
is a significant operating expense for NBA team owners each season. In the 20032004 Season, payroll made up more than 65% of a team’s total expenses. The
‡
This rule applies for team’s who have payrolls beneath the salary cap. Teams with payrolls
beneath the salary cap are required to have a payroll of at least 75% of the league salary cap each
season (Coon, 2005).
35
salary cap helps slow the growth of player salaries, so the cost of obtaining or
keeping a specific player will not change drastically as long as it is in effect. This
makes it easier for owners to predict salary and payroll growth than in an
environment without a salary cap. Without a salary cap, salaries can spiral out of
control as willing spenders bid up the price for players up to unfathomable
levels. This auction like environment would make it extremely difficult for
owners to guess how fast their payroll costs would grow from season to season.
However, since owners can estimate the rate of payroll growth, they can have
confidence in forecasts of their respective future payroll levels. With increased
certainty about payroll growth and payroll levels, owners and team management
can plan better strategies. They can determine whether to increase or reduce
payroll in upcoming seasons or whether they should rebuild their team or reload
for a championship run. Whatever a team’s owner and management decide to
do, they must acquire the players that fit their plans, and the process they do this
by helps sustain the NBA’s winner-take-all-market.
The CBA facilitates the NBA’s version of the winner-take-all-market. The
salary cap forces teams to decide how they going to fill their rosters with limited
funds. Though these funds are limited, the share of BRI that the players are
allowed to have is so large that if all teams distributed their funds evenly all
players would earn multimillion dollar salaries. However, teams do not
distribute their funds evenly, but instead decide how much to allocate to a player
they want based on a player’s performance and non-performance characteristics.
36
NBA free agency allows players on the market to go to the team that gives them
the best deal. Since quality players are so hard to obtain, teams are willing to
allocate large portions of their salary cap money (space) to these players. In
contrast, lower quality players are much easier to come by, so teams do not need
to allocate a large portion of their salary cap to have these players. Nevertheless,
lower paid players are still paid so well compared to other professions that
players who are a long shot at best to make the league continue to strive in hopes
of a future opportunity. Whether these hopefuls will earn big money in
professional basketball will depend on how they perform and their nonperformance characteristics, characteristics that will be discussed in the next
section.
The Econometric Models
I have created a unique dataset that includes eleven seasons of data.
Player performance statistics are from Steele’s website, salary information from
the 2001-2002 through the 2004-2005 Seasons from the USA Today’s Basketball
Database, and prior salary information is from Fort’s website. Physical nonperformance characteristics were taken from Bender’s website, and specific
contract information and transaction information was found on the NC Systems
Hoops website. Player representative information was compiled from various
newspapers searches through the Infoweb database. The eleven year database
captures the impact of performance and non-performance variables under two
37
salary regimes (1995 and 1999) and allows comparisons with the new CBA
(2005).
I assume that owners and general managers use recent performance
information to evaluate a player’s value, so I lag a player’s performance data one
season behind their salary observation. Rookies or players who did not play in
the league the previous season are deleted from the dataset because their
performance came at a lower level of competition. Players under contract who
did not play for an entire season due to injury are also not included in the dataset
because performance data from the previous season cannot explain why a player
receives a salary the following season. Finally, players without sufficient player
representative information are deleted from the dataset. The final dataset has
3787 observations covering eleven seasons.
I will run all of my models using ordinary least squares on Shazam
econometric software. Coefficients that are statistically significant will be
denoted with asterisks. Most models are log-linear models, which demand a
different interpretation of the reported coefficients than if they came from a
strictly linear model. The proper way to interpret a log-liner coefficient is to
multiply the coefficient by 100%, and this represents the average percentage
change in the dependent variable (salaries) when the independent variable
changes by one unit. I report both R-Square and Adjusted R-Square values,
which will be in parentheses below the R-Square row of each table. If you cannot
38
find a column reporting standard errors for each coefficient then look for them at
the parenthesis below.
Model 1: Salaries and Player Performance
LN(Salaries)= β1+ β2PPG + β3RPG + β4APG +β5BPG + β6SPG + β7Win% +
β8Season + e
Where salaries are in millions of dollars
Dependent Variable: Player Salaries
The intent of this model is to measure how much a player’s salary is based
on their on the court performance. I assume in this model that a player’s salary
is determined by a combination of how they perform on the court and some
randomness.
Performance Explanatory Variables:
A player’s productivity is measured by their performance statistics.
Performance statistics in this model include games played (GP), points per game
(PPG), rebounds per game (RPG), assists per game (APG), blocked shots per
game (BPG), and steals per game (SPG). I expect that each variable has a positive
affect on player salaries.
A player’s performance statistics are affected by the team they play on.
An average player on a bad team may have better statistics than if he played on a
39
good team since he gets more playing time on a bad team. This phenomenon
makes players on bad teams seem underpaid when they play on a bad team or
sign with a new team. A recent example occurred when forward Shareef AbdurRahim, the best player on some of the worst teams in the league history, entered
the free agent market in the 2005 off-season. Abdur-Rahim’s statistics suggested
he was an All-Star caliber player, but since he played on a bad team his statistics
were discounted by observers. The Sacramento Kings signed Abdur-Rahim to a
bargain-basement 5 year $29 million contract
Season
2004-2005
Player
Shareef
Abdur-Rahim
Salary
$5,000,000
Win %
.329
Points/Game
16.8
Rebounds/Game
7.3
Assists/Game
2.1
I use the team winning percentage of the last team a player played on
before they received a new contract to control for team quality. I expect players
who play on bad teams to have incurred a salary discount compare to players
who play on good teams.
Season:
The salary cap has increased over time due to the increases in league
revenue. This provides teams with more money to spend on their players each
season. Each season in this sample period of ten seasons has a dummy variable
associated with it. Since league revenues have increased the salary cap over
time, I expect that player salaries have also increased from season to season.
Model 1 Results: Salaries and Player Performance
40
Dependent Variable: Natural Log Player Salaries
1995-1996 to 2004-2005
Constant
Games Played (GP)
Points Per Game (PPG)
Rebounds Per Game (RPG)
Assists Per Game (APG)
Blocks Per Game (BPG)
Steals Per Game (SPG)
Team Win %
This Year is 1996
This Year is 1997
This Year is 1998
This Year is 1999
This Year is 2000
This Year is 2001
This Year is 2002
This Year is 2003
This Year is 2004
R-Square (Adj)/Standard Error
F-Statistic
*Significance at 10% Level
Estimated Coefficient
-1.5203***
0.0027***
0.0637***
0.0908***
0.0801***
0.2386***
0.0146
0.7013***
0.0259
0.1421**
-0.1068*
0.4610***
0.6457***
0.6811***
0.7121***
0.7912***
0.7998***
0.542 (.5399)
252.727
**Significance at 5% Level
Standard Error
0.0695
0.0007
0.0037
0.0083
0.0108
0.0319
0.0429
0.0808
0.0586
0.0581
0.0588
0.0586
0.0572
0.0579
0.0578
0.0581
0.0574
0.74282
T-Ratio df=3417
-21.8900
3.7960
17.3500
10.9800
7.4390
7.4810
0.3407
8.6820
0.4417
2.4460
-1.8170
7.8660
11.3000
11.7700
12.3100
13.6300
13.9300
*** Significance at 1% Level
R-Square of .542 indicates that just over half the variation in players’
salaries can be explained by the variation in their performances. As expected,
every performance variable, except steals per game, is statistically significant at a
1% level. This suggests that the more points scored, rebounds retrieved, assists
given, and shots blocked per game, the higher the player’s salary. The most
valuable performance statistic is blocks per game: players who raise their
average by one blocked shot should, on average, earn 23.86% more in salary.
Also every win a player is part of during a season, on average, should yield them
0.855% more in salary (1/82x0.7013x100%).
Model 2: Sports Agents vs. Other forms of Representation:
41
LN(Salaries)=β1+ β2PPG + β3RPG + β4APG +β5BPG + β6SPG + β7Win% +
β8Championship + β9All-Star + β10Age + β11Age2 + β12Experience + β13Height +
β14Race + β15 Foreign + β16 Forward + β17 Center + β18 1st Round + β19 1st Round
95 + β20 Contract-Extension + β21Resign-Restricted + β22Sign-Restricted + β23
Sign-Unrestricted + β24Contract-Year + β25Agents + β26SmallMkt + β27LargeMkt
+ β28Season + e
Where salaries are in millions of dollars
Dependant Variable: Player Salaries
The intent of this model is to determine if agents have a greater impact on
player salaries than other type of representatives. In order to isolate the effects of
agents, other performance and non-performance factors that may explain
variation in player salaries must also be included. In this model, player salaries
are the dependent variable, and it is assumed to be a function of recent
performance, non-performance characteristics, and some randomness.
Performance Explanatory Variables
I use the same player productivity measures that were used in the first
model, and I expect every measure to have a positive impact on a player’s salary.
Additional Performance Variables: Career Achievements
Championship:
42
Some teams are attracted to players with championship experience
because they can help guide other players. Former player Steve Kerr played for
the Chicago Bulls when they won three consecutive titles from 1996-1998, and
was often was on the floor when the game was on the line. Kerr was a solid
backup player with a great three point shot, but his most marketable trait was his
championship experience. After the Bulls broke up, Kerr signed a five year $11
million contract with the San Antonio Spurs, a team that historically was good.
Season
1997-1998
Player
Steve
Kerr
Salary§
$1,081,600
Championships
3
Points/Game
7.5
Assists/Game
1.9
Minutes/Game
22.4
Although Kerr did not play as often as he did when he was with the Bulls, he
helped the Spurs win an NBA Championship in the truncated 1998-1999 Season.
Teams close to winning a championship may pay a premium to acquire a
player with championship experience. However, the available supply of players
with championship experience is often small since championship teams usually
try to keep their players together in order to make more title runs in the future.
All-Star:
Players who are named to the NBA All-Star team are among the best
players at their position. The All-Star Game is played between Eastern and
Western Conferences just after the half way point of each season, and each team
fields twelve players. Players can qualify by being voted on by fans to start,
§
Kerr’s salary is prorated due to the shortened 50 game season in 1999.
43
voted on by coaches as a reserve, or get selected by the commissioner to replace
an injured player. Many of the league’s best players get selected to play in the
All-Star Game multiple times. Vin Baker early in his career was considered one
of the best, young power forwards in the game. He made the All-Star team four
times, representing both conferences in his career, but after the 1998 lockout
Baker’s skills deteriorated. When Baker went from team to team, he used his AllStar legacy to garner sizeable salaries. In the 2004-2005 Season, Baker signed a
two year contract, which yielded him $3,500,000 in the first year.
Season
2003-2004
Player
Vin
Baker
Salary
$3,500,000
All-Star
4 Times
Points/Game
9.8
Rebounds/Game
5.2
Blocks/Game
0.6
I expect that players who made the All-Star team at some point in their
career to receive a salary premium for the rest of their career. Players who make
the All-Star team have shown that they have the potential to be among the best
players in the league each year. Teams that sign former All-Stars may be willing
to pay a premium for these players because they believe these players can return
to being All-Star or attract fans who recall these player’s past.
Non-performance Explanatory Variables:
Non-performance metrics are characteristics that do not involve on the
court performance or career achievement, but reflect both physical and
nonphysical characteristics that may be relevant in determining a player’s
performance or value of a player’s contract. Physical characteristics include a
44
player’s height, position, age, race, experience, and country of origin, and
nonphysical characteristics include who represents them, how they were
acquired (restricted free agent, unrestricted free agent, or re-signed), what round
they were drafted, and whether they were in their final year of their contract in
(the contract year) the previous season. Other characteristics that lie outside a
player’s control that may affect the value of their contract, including the market
size of their team and the overall increases in salaries each season.
Age:
Age can have a positive impact on a young player’s salary. Youth may
entice teams to pay more for a young player because they believe the player has
potential to become a very good player. With plenty of years left to go in a
player’s career, they will have time to develop their skills. One of the youngest
players ever to enter the league, Tracy McGrady, became a free agent in the
summer of 2000 at the age of 21. McGrady skills had improved year to year, but
his performance before he became a free agent did not justify his 7 year
$92,880,000 contract that he got from the Orlando Magic.
Season
1999-2000
Player
Tracy
McGrady
Salary
$9,660,000
Age
21
Points/Game
15.4
Rebounds/Game
6.3
Assists/Game
3.3
Orlando paid McGrady for his potential since he was very young and had
showed great improvement. In this case, McGrady did develop into one of the
premier players in the NBA, but Orlando did take a gamble nevertheless.
45
At some point age catches up with all players and their skills begin to
deteriorate and they are not as productive as they were when they were younger.
Teams offer a contract that discounts for the expected decrease in a player’s
productivity due to the player’s deteriorating skills.
I expect that age positively affects a young player’s salary up to a
maximum point before it negatively affects salary as the player skills’
deteriorates. To account for this decline I have created another variable known
as “Age Squared”.
Experience:
Some veteran players earn a roster spot on a team even though an
inexperienced player may be more productive. Teams may prefer to have a
veteran player on their team compared to an inexperienced player because the
veteran player may provide leadership to inexperienced players on the team.
Veteran leadership is important to the development of inexperienced players
since they can show them ways to improve their skills and help them adjust to
the NBA lifestyle. For instance, the Atlanta Hawks had many rookies on their
team in the 2004-2005 Season. The Hawks needed a center and looked to the free
agent market for one. The Hawks chose to sign reserve center Kevin Willis who
had just completed his 18th year of service.
Season
2003-2004
Player
Kevin
Willis
Salary
$1,300,000
Team
Atlanta
Years Experience
19
Games Played
48
Minutes/Game
7.7
46
In a press conference to introduce Willis to the Atlanta media, General Manager
Billy Knight explained how Willis would improve the Hawks: “He will be an
important presence on the court as well as in the locker room, and we’ll look to
him especially to set an example for our younger players. His experience and
leadership will be invaluable” (Hawks).
The NBA CBA rewards veteran players for the years of service they put
into the league. The CBA calls for minimum and maximum salaries to rise with
experience and provides teams one veteran exception where they can sign a
veteran player to the veteran minimum. In Willis’s case he signed for the veteran
minimum, which was for $1.3 million since he had more than ten years of
experience in the league. Given the CBA’s rules to reward experience players, I
expect player experience to have a positive impact on their salary.
Height:
Nearly all NBA players are tall, but the tallest players can use their size to
outperform smaller opponents. Utah Jazz center Greg Ostertag is often maligned
by fans because he does look like a professional athlete when he is on the floor.
He is slow, has poor hand-eye coordination, and is usually out of shape, yet
received a 2 year $8.4 million contract to play basketball in 2004.
Season
2003-2004
Player
Greg
Ostertag
Salary
$4,000,000
Height
7’2”
Points/Game
6.8
Rebounds/Game
7.4
Blocks/Game
1.8
47
Greg Ostertag is not the first player in NBA history to linger in the league
because he is tall. 7’5” Shawn Bradley played twelve years in the NBA while 7’
7” Gheorghe Muresan lasted six years. An old cliché used by NBA analysts to
explain why tall, unskilled players can remain in the league year after year is
“you cannot teach height”. I expect height to have a positive impact on player
salaries, and I measure player height in inches.
Player Position:
Many NBA players are versatile players who can play two, three, or even
four positions. For example, Los Angeles Lakers star Lamar Odom is 6’10” and
is skilled enough to play point guard, shooting guard, his listed small forward
spot, and power forward. This suggests that the best way to classify players by
position is to create many positional categories can accommodate versatile
players and limited players. However, creating these many categories results in
severe multicollinearity problem that will offset any benefits derived from using
a more accurate position classification system. I will instead adopt Eschker,
Siegler, and Perez’s player classification system. I assign a dummy variable for
each category and ask whether a player fits into a particular positional category.
To avoid the dummy variable trap, I omit players who play guard, so if a player
does not register a 1 in these positional categories he is a guard. The positions
that will be considered are players who play forward and center. I expect both
centers and forwards to make, on average, more than guards.
48
Race:
Each study on player salary has considered the role of race to test for
discrimination. Kahn (1988) found significant evidence of strong wage
discrimination against black players. Dey (1997) later retested Kahn’s hypothesis
to see if there was still evidence of wage discrimination among black players,
and found that the pay gap between white and black players for their
productivity narrowed.
African Americans make up the majority of NBA players, but there has
been recent influx of black players from Europe and other continents: Tony
Parker, John Amaechi, and Tariq Abdul-Wahad are recent players from Europe
while Nene Hillario and Luis Flores are recent players from Spanish speaking
countries. I assign dummy variables to each player and ask whether they were
black or white.
Foreign:
Eschker, Siegler, and Perez (2004) found that there was a salary premium
paid to foreign players, but it disappeared as teams began to improve their
evaluation of players. Their data covered a period (1996-1997 to 2001-2002)
before a major influx of foreign players into the NBA, and while talented foreign
players like Dirk Nowitzki were still in their rookie contracts. Many foreign
players have recently fulfilled their rookie contracts and have received new
49
contracts. One of these players is Radoslav (Rasho) Nesterovic, who entered the
league as a rookie in the 1998-1999 Season and received a new contract in the
2003 off-season from the San Antonio Spurs.
Season
2002-2003
Player
Rasho
Nesterovic
Salary
$5,600,000
Country
Slovenia
Points/Game
11.2
Rebounds/Game
6.5
Blocks/Game
1.5
Today Nesterovic is perceived by observers to be overpaid, and lost his starting
job to another player. Clearly Nesterovic is not living up to the expectations that
the Spurs had when they signed him in the 2003 off-season.
The reason to question whether foreign players receive a salary premium
is that owners and general managers may overestimate the ability for foreign
players to adjust to the NBA. Although the quality of international basketball
has greatly improved in recent years, there are still major differences (rules, style,
etc.) between international and American basketball. A foreign player is defined
as a player who was born outside the US and never played high school or college
basketball in the US before entering the NBA. This means that a player may be
foreign-born, but is not considered a foreign player if they played in the US
before they entered the NBA. Similarly, a player who was born in the US, but
came to the NBA after playing overseas is not a foreign player. I assign a
dummy variable to each player and ask whether a player is a foreign player or
not.
1st Round: Before & After 1995
50
The evolution of 1st round draft pick rookie contracts illustrates how the
CBA can shape player salaries. Before 1995, the value of a draft pick’s rookie
contract was solely determined by the negotiations between the team and the
draft pick’s representative. These negotiations often resulted in a young player
earning a larger salary than his veteran teammates. After the 1995 CBA the value
of a 1st round draft pick’s rookie contract is mostly “determined (by players’
union and owners) for all picks in all seasons when the CBA is written” (Coon
2005). The higher a player is drafted the more they receive, however it is far less
than they would receive if there were not restrictions. Although the salary of a
draft pick should receive is predetermined, the player’s representative and team
are allowed to negotiate a salary anywhere within 20% of the defined value
(New NBA CBA). The contract is guaranteed, so the player is paid if they are on
the team or not. In contrast, 2nd round draft picks are not subject to the rookie
scale, so they do not have the security that 1st round draft picks have and are also
paid less than first round picks. A consequence of the rookie scale is that a
young superstar can be underpaid while others who rarely play in the first few
years of their careers can be overpaid.
Rosenbaum (2003) found that the rookie scale negotiated by the players
and the league in 1995 has punished the young players coming into the league.
Players on their rookie contracts, on average, are underpaid relative to their
performance. One of the most blatant examples of a player on his rookie contract
being underpaid is superstar LeBron James.
51
Season
2004-2005
Player
LeBron
James
Salary
$4,621,800
Points/Game
27.2
Rebounds/Game
7.4
Assists/Game
7.2
Steals/Game
2.2
Based on LeBron James’s performance statistics he should be among the highest
paid players in the league. Nonetheless, James is obligated to his rookie contract
and must accept the salary assigned to him by the CBA until his contact expires.
It is no surprise that the Cleveland Cavaliers exercised their option on James and
he will remain a Cavalier to at least to the end of his 4th season at a great bargain.
I assign two dummy variables to each player to distinguish players who
played under rookie contracts negotiated before and after the 1995 CBA.
Those with contracts negotiated before 1995 should had received a salary
premium in their rookie contracts since young players were typically paid more
than veteran players. However, those with contracts negotiated after the 1995
CBA should receive a salary penalty because rookie salaries are set at low salary
levels.
Resigned Contract-Extension:
Teams can re-sign their player to a new long-term contract before the
player enters free agency. The CBA determines what types of players qualify for
an extension, the length of a contract-extension, and the salary a player can
receive in first year of the extension. For example, a player in the 4th year of his
rookie contract can negotiate a contract-extension for up to the maximum
contract term and the player’s maximum salary (Coon, 1999). Risk-adverse
52
teams may overpay a player when they sign their player to a contract-extension
because they fear exposing him to free agency. The Utah Jazz was afraid their
young, star player, Andre Kirilenko, would leave Utah if he entered free agency.
Before Kirilenko could become a free agent the Jazz secured him with a 6 year
$86 million contract-extension. Although Kirilenko is a unique player, his recent
performance indicates he is overpaid.
Season
2004-2005
Player
Andre
Kirilenko
Salary
$12,000,000
Points/Game
15.6
Rebounds/Game
6.2
Assists/Game
3.2
Blocks/Game
3.3
I assign dummy variables to each player and ask whether a particular
player signed a contract-extension from their team. I expect players who sign
contract-extensions to receive a salary premium. These players appear to sell the
freedom that they would have as a free agent to their original team in exchange
for additional compensation.
Resigned Restricted Free Agent:
A team has an opportunity to re-sign its own player, if they are coming off
the 4th year of a rookie contract or have been in the league less than three
seasons, by giving them a qualifying offer (Coon, 1999). A qualifying offer is a
one year contract for a salary determined by the CBA and the player’s
circumstances (i.e. draft position for 1st round picks or their previous salary for
others) and is treated as a salary exception. However, if another team makes an
**
The Grizzlies relocated from Vancouver to Memphis in the 2001-2002 Season.
53
offer above the qualifying offer the original team has to use their cap space or
another exception to re-sign their free agent or they will lose the player (Coon,
1999). If the original team matches the other team’s offer then the player must resign with the original team for the terms stated on the other team’s contract. A
player may be overpaid even though the original team matches an offer because
other team would not had made the initial offer unless they believed their offer
was above the original team’s reservation price. One case occurred when the
Miami Heat offered Clippers’s forward Elton Brand a 5 year $82,170,000 contract
with the hopes that they would not match. The Clippers are notorious for being
cheap and letting their best players leave for other teams. However, the Clippers
surprised the Heat and matched the offer because it surprisingly was below the
Clipper’s reservation price.
Season
2002-2003
Player
Elton
Brand
Salary
$10,960,000
Points/Game
18.5
Rebounds/Game
11.3
Blocks/Game
2.5
Minutes/Game
39.6
If a player is not as fortunate as Brand they can still sign the qualifying offer and
return to their team.††
I assign dummy variables to each player and ask if they were a restricted
free agent who re-signed with their team. I expect a team that re-signs its
restricted free agents to pay a premium for them.
††
This happens rarely and will not be considered in this study (not enough observations)
Market size data for Canadian teams is from Canada’s 1991 and 2001 population surveys.
§§
This happens rarely and will not be considered in this study (not enough observations)
‡‡
54
Signed: Restricted Free Agent:
I consider in this category players who went to other teams because their
original team could not match another team’s offer, did not want to pay the
money to match another team’s offer, or felt it was better to trade them. Teams
who covet another team’s restricted free agent have an incentive to bid above
what the other team can afford, and in some get caught in a bidding war if the
player is a high quality. This happened when former Golden State Warriors
guard Gilbert Arenas was a restricted free agent in the 2003 off-season. Arenas
was voted the NBA’s most improved player in the 2002-2003 Season after
significantly increasing his production as a starter. The Warriors made a
qualifying offer to Arenas and hoped they could somehow find a way to keep
him. The Denver Nuggets, the Clippers, and Washington Wizards also wanted
Arenas and had offers ready for his services. The Warriors had little chance to
keep Arenas because they could not offer him a lot of money. Arenas had less
than three years of experience in the league, so the Warriors could only offer him
$3 million. Meanwhile, Denver, Los Angeles, and Washington had plenty of
salary cap space and made original bids above what the Warriors could afford.
With the Warriors eliminated a bidding war ensued. Soon Denver felt they
could not compete with Washington and Los Angeles and pulled out of the
running. The Wizards ultimately prevailed over the Clippers and signed Arenas
to a 6 year $65 million contract.
55
Season
2002-2003
Player
Gilbert
Arenas
Salary
$8,533,333
Points/Game
18.3
Rebounds/Game
4.7
Assists/Game
6.3
Minutes/Game
35.0
Bidding wars for free agents such as Arenas may not always happen, but teams
may still overpay to make sure the original team cannot or does not want to
match their offer.
A team over or under the salary cap can acquire another team’s restricted
free agent because the CBA allows teams to re-sign their own players for the
purpose of trading them to another team. This is beneficial to all parties
involved because the player can receive a larger salary, the original team receives
compensation for losing their player, and the player’s new team can acquire
them even though they may be above the salary cap. This happened when
young star Joe Johnson became a restricted free agent in the 2005 off-season.
Phoenix vowed it would match any offer made by another team since he was a
key part of their success in the 2004-2005 Season. However, Phoenix was
reluctant to give Johnson anything close to a maximum contract because they
were financially constrained. The Atlanta Hawks knew Phoenix’s plight and
offered Johnson a massive 5 year $69,673,704 contract. Phoenix publicly stated
that they would match the offer, but hinted that they would participate in a signand-trade with Atlanta if a deal was satisfactory. Atlanta and Phoenix agreed to
a blockbuster trade where Johnson signed with Phoenix and then was traded to
Atlanta for player Boris Diaw and two conditional first round draft picks.
Season
Player
Salary
2004-2005
Joe
Johnson
$12,000,000
Points/Game
Rebounds/Game
Assists/Game
Minutes/Game
17.1
5.1
3.5
39.5
The Hawks were not only ridiculed for paying so much for Johnson, but for
trading away valuable assets to guarantee themselves the right to overpay
Johnson. However by giving Johnson such a large contract they dissuaded
Phoenix from keeping Johnson.
I expect teams who sign a restricted free agent from their original team or
acquire a restricted free agent through a sign-and-trade to pay a premium for the
player. The salary premium is given to the player so the original team is
dissuaded from matching the offer. I assign dummy variables to each player and
ask whether a particular player was a restricted free agent when they were
acquired by another team.
Signed: Unrestricted Free Agent
A player is an unrestricted free agent when their original team can do
nothing to prevent them from signing with another team. Any team that wants a
player can utilize their salary cap space or salary cap exceptions to sign the
player, but the player decides where they want to play. In many cases several
teams are interested in a player, and this can sometimes result in a bidding war
for one player. One lesser known bidding war came in 2002 when the Los
Angeles Lakers and Philadelphia 76ers competed for the services of unrestricted
57
free agent guard Greg Buckner. Buckner was a reserve guard for the Dallas
Mavericks who performed adequately when he was called on to play. The
Mavericks fell out of the running for Buckner after the Lakers and 76ers drove
his price out of their price range. Buckner’s agent, Steve Kauffman, confirmed
this when he explained: “It’s been a great relationship between all parties in
Dallas, but I think Buck is in greater need with other teams…they (Lakers and
76ers) just have a greater need for him and the kind of things he does” (Garcia).
Kaufmann and Buckner measured the need of interested teams by their
reservation price. The 76ers proved to have the greatest need for Buckner since
they were willing to sign him to a 6 year $18 million contract.
Season
2004-2005
Player
Greg
Buckner
Salary
$2,400,000
Points/Game
5.8
Rebounds/Game
3.9
Assists/Game
1.1
Minutes/Game
20.1
Like restricted free agents, a team over the salary cap can still acquire
another team’s unrestricted free agent because the CBA allows teams to signand-trade their unrestricted free agents. One recent example came when former
Golden State Warriors’ center Erick Dampier opted into free agency in after a
strong season 2003-2004 Season.
Season
2003-2004
Player
Erick
Dampier
Salary
$7,700,000
Points/Game
12.0
Rebounds/Game
12.3
Blocks/Game
1.9
Minutes/Game
32.5
Dampier throughout his career has been known as a very inconsistent player and
an underachiever, but this did not stop teams from making large offers. The
Dallas Mavericks desperately needed a center, but had little to offer Dampier
58
because they were well over the salary cap. The Atlanta Hawks, a team under
the salary cap, offered Dampier a 6 year $50 million contract, but the Mavericks
and Warriors agreed to sign-and-trade deal with Dampier where the Warriors
would sign Dampier to a 7 year $65 dollar contract and then trade him to the
Mavericks along with two other Golden State players for four Dallas players, two
future first round draft picks, and cash considerations (NC-Hoops).
I consider unrestricted free agents who sign with another team as
unrestricted free agents who are signed outright or are acquired through a signand-trade. I assign a dummy variable to each player and ask whether a player
was acquired as an unrestricted free agent. I expect an unrestricted free agent
who signs with another team to receive a salary premium due to their exposure
to market forces.
Resigned Unrestricted Free Agent:
To avoid the dummy variable trap I omit the re-sign unrestricted free
agent category from the model. I use this category as the reference group, so the
other free agent and contract-extension coefficients will be relative to this group.
Contract Year:
Stiroh (2004) found that players, on average, do increase their effort and
production during their contract year. Players recognize that the value of their
next contract will be affected by their recent performance. Improvement
59
displayed young players in their contract year can show the owner and general
manager that they are developing and have potential to be a very good player.
Veteran players may also give additional effort in their contract year because
they want to play for their last, long-term contract. I assign dummy variables
each season and ask if a player has received a new contract. Since players
increase, on average, their effort and production, I expect the contract year to
have a positive impact on a player’s salary.
Agents:
In an ideal world, player salaries would be based on performance.
However in the NBA, the person negotiating a player’s contract seems to also
have an affect on a player’s salary. A player can either negotiate their own
contract or hire a representative to negotiate on their behalf. For example, former
Utah Jazz star Karl Malone used to personally negotiate his contracts, and never
entered the free agent market, instead extending his contract half way through
the season (Jorgensen, 1998). In the 1997-1998 Season, Malone won the NBA’s
most valuable player (MVP) award, yet was paid the 32nd highest salary in the
league. In this same season, Michael Jordan, who had Falk negotiate his contract,
earned $33,140,000, which was over six times more than Malone’s $5,119,000.
Season
1996-1997
1996-1997
Player
Michael
Jordan
Karl
Malone
Salary
$33,140,000
Points/Game
30.4
Rebounds/Game
6.6
Assists/Game
4.3
Representation
Sports Agent
$5,119l00
27.4
9.9
4.5
Self-Representation
60
After the season Malone realized that it was time for him to hire an agent, saying
in an interview, “It hurt me some. As bad as it may sound, the people that have
the agents seem to be the people who get the contracts” (Jorgensen, 1998).
Malone’s new agent, Dwight Manley, negotiated for his client a four year
$66,500,000 contract before the start of the next season.
Player representatives have intrinsic and extrinsic incentives to maximize
the value of a client’s contract. Most agents work on a commission basis,
receiving a small percentage of the player’s annual salary, so their incentives are
aligned with the players they represent. Attorneys, who do not work on a
commission basis, also have an incentive to maximize the value of a player’s
contract during negotiations because they want to maintain a good reputation to
attract future clients. Both types of hired representatives also want to do a good
job for their clients not only for financial gain, but also for the intrinsic benefit of
friendship. Regardless of how motivated a representative is, their ability to
perform in the future will determine if a player will hire them or take another
route.
A Player will hire the person who provides the best service, usually the
person who negotiates the best deal. Players may value other services provided
by a firm that the representative belongs to, but most consider these extra perks
secondary compared to negotiating the best deal. Agent Joel Bell explains that
players who look to hire an agent prefer to hire a quality agent rather than enlist
a brand name agency (who may have mediocre agents):
61
“In this industry there is no big advantage to being with a big
agency…athletes follow people, not a company name. If any top agents in
the big companies moves to his own office across the street, the athlete is
going to get the exact same service. That’s cause a good agent is
experienced at marketing their people and negotiating contracts”
(Sandoval, 2004).
I assign a dummy variable to every player and ask whether a player was
represented by a sports agent or in some other form i.e. attorney or selfrepresentation. Since sports agents have been around longer, I expect players
who are represented by sports agents to receive higher salaries than players who
are represented in some other way.
Large Market, Medium Markets, and Small Markets:
Not only do large market teams tend to have higher payrolls, they also
tend to give larger contracts to players. Large markets may overpay for a player
because they are pressured by fans to acquire the players needed to win. For the
last few years the New York Knicks have given generous contracts to players
whose performance would not seem to justify their enormous salary. A recent
Knicks’s blunder was the signing of Jerome James to a 5 year $29 million contract
even though his performance statistics show he is a marginal player.
Season
2004-2005
Player
Jerome
James
Salary
$5,000,000
Team
New York
Points/Game
4.9
Rebounds/Game
3.0
Blocks/Game
1.39
62
In contrast small market teams may have to pay more to keep their own
players or to recruit players from other teams. Small market teams must
compete with larger market teams where players can earn additional benefits
like endorsements. Players may also shy away from small markets because of
factors such as the city’s climate, nightlife, or geographic location. For example,
New York native Stephon Marbury once proclaimed he never wants to play in
Vancouver*** because it was “too close to Russia” (Whitley, 1996). Meanwhile,
forward Carlos Boozer had no problem playing in Utah, who offered him a 6
year $70 million contract. Although Boozer at the time was a promising young
player, now it appears that he was given way too much money by the Jazz.
Season
2003-2004
Player
Carlos
Boozer
Salary
$10,967,500
Team
Utah
Points/Game
15.5
Rebounds/Game
11.4
Blocks/Game
0.7
I will use the U.S. Census Bureau’s Metropolitan Statistical Areas (CMSA)
dataset to rank the teams based on market size. The top 10 ranked teams are
categorized as large market teams. The bottom ten ranked teams are categorized
as small market teams. I assign two dummy variables to each player and ask
whether they play in a large market team or a small market team. To avoid the
dummy variable trap, medium-size markets will not be considered, so if a player
does not register a value for either category they play for a medium-size market
team. For observations from 1994-1999 I use 1990 population data and for
***
The Grizzlies relocated from Vancouver to Memphis in the 2001-2002 Season.
63
observations 2000 onwards I use 2000 population data.††† Small markets should
pay a premium to keep their players from leaving them while large markets
should pay a premium because they are pressured to spend money. I expect
players playing in medium-sized markets to not receive a salary premium
because their teams do not face the pressures of smaller and larger market teams.
Season:
Like in the first model each season is assigned a dummy variable to
control for the annual increases in the overall player salary level.
Model 3: Highly Regarded vs. Ordinary Representatives
The intent of this third model is to assess whether representatives with
good reputations have any edge over those with lesser reputations in negotiating
contracts for their clients. It may or may not matter how a player is represented,
but it may matter who represents a player because the representative may help
them and teams overcome a major problem in the NBA labor market.
This model is a slight variation of the second model with the same
explanatory variables except for the Agent variable. The Agent variable is
replaced by eight variables, each representing a highly regarded representative:
†††
Market size data for Canadian teams is from Canada’s 1991 and 2001 population surveys.
LNSalaries=β1+ β2PPG + β3RPG + β4APG +β5BPG + β6SPG + β7Win% +
β8Championship + β9All-Star + β10Age + β11Age2 + β12Experience + β13Height +
β14Race + β15 Foreign + β16 Forward + β17 Center + β18 1st Round+β19 1st Round 95
+ β20 Contract-Extension + β21Resign-Restricted + β22Sign-Restricted + β23 SignUnrestricted + β24Contract-Year + β25Bartelstein + β26Goodwin + β27Duffy +
β28Tellem + β29Falk + β30Fleisher + β31Babby + β32Fegan + β33SmallMkt +
β34LargeMkt + β35Season + e
Why Might it Matter: The Teams’ Problem
Teams face a problem whenever they try to acquire a player from the NBA
free agent market. A team cannot simply sign a player, wait to see if they can
live up to expectations, and then release them if they fail for two main reasons.
First, it takes a significant period of time for a player to become fully productive.
A new player needs time to build chemistry with his teammates on and off the
court. Surprisingly, NBA teams rarely practice during the regular season due to
the high frequency of games, injuries, and demanding travel schedules. Second,
it is very expensive for a team to release a player they just recently signed. NBA
player contracts, in most cases, are guaranteed, so if a team waives a player they
would have to pay the guarantee portion of the released player’s contract and the
replacement’s salary. Risk-adverse teams, who seek to avoid these costly
consequences, take steps to minimize the probability that the player they are
considering will underachieve. Teams rely on their own methods of talent
evaluation, which include the use performance statistics. However, sole reliance
on internal information sources may not be enough for teams in the presence of
65
asymmetric information. Teams may also consider external indicators to help
them determine if players will perform up to expectations.
The Players’ Attempt at a Solution: Hire a Representative to send a Signal
Representatives develop reputations around the league through many
years of work and the type of players they represent. Player representatives
utilize a variety of tactics in their attempts to get their clients the best deal
possible. Representatives use media interviews to embellish the value of their
client(s) or overplay the demand for their client. Sometimes a representative will
engineer a sign-and-trade so their client can receive the best offer available from
a team who otherwise could not offer it to them due to salary cap reasons.
Remarkably skilled representatives are great negotiators who can consistently
get team general managers and/or owners to overestimate a client’s value and
overpay for their client. Over time some representatives develop reputations
that help them attract new clients. Players prefer to hire representatives with
good track records of negotiating good contracts for their respective clients
because they seem likelier to negotiate a better deal than someone with a bad
reputation. However, most representatives are selective in the number and type
of clients they will represent since it helps them preserve their reputation. For
example, agent Marc Fleisher usually represents the top foreign players while
David Falk was once considered the most powerful and influential agent in the NBA. Dan
Fagan represents high number of diverse clients and is considered one of the hardest agents to
negotiate with. Marc Fleisher is known for his representation of the best foreign players who
play in the NBA.
‡‡‡
66
agent Arn Tellem represents the league’s top young and established stars. If a
representative has too many clients they will not be able to devote enough
attention to their key clients, and if they are willing to represent anyone teams
may lose confidence in the credibility of the representative. Teams may not
believe a representative when they insist their client is a quality player if they did
not screen out unproductive players from their clientele. In addition to helping a
representative to maintain their reputation, this selectiveness may also help them
profit from their reputation.
A representative’s reputation may affect a team’s willingness to pay their
clients because it may serve as a signal for teams about the quality of the
representative’s client. A representative reputation may be a signal for teams
because players had to meet the standards of the representative before they
agreed to represent them. A player who seeks to communicate information to
teams about the type of player they are hires a representative who can project
this image. However, not all players can claim to be highly productive players
because highly regarded representatives have incentives to be selective about
whom they represent. If a player failed to meet a representative’s criteria the
representative would never had agreed to represent them, so teams can glean
information about a player’s quality by considering the individual who agreed to
represent them. This information reduces a risk-adverse team’s uncertainty
about the quality of a player. With reduced uncertainty, a team will be more
67
willing to comply with the representative’s (especially a highly regarded one’s)
request to offer their client a lot of money.
Players hire highly regarded representatives in an attempt to signal to
teams that they are quality players when little is known about them. This is
common among pro-prospects who hire big name representatives after they
announce they will enter the NBA Draft. For example, in 1996 high school senior
Kobe Bryant declared his intentions to skip college and enter the NBA Draft. At
the time Bryant’s decision was considered bold because 99.99% of pro-prospects
waited to at least after their freshmen year in college before they considered
entering the NBA. Since Bryant attempted to enter the league without ever
playing in a collegiate game there was a lot of uncertainty about his ability to
play in the league. However, Bryant quickly dispelled many teams’ doubts
about his ability to play in the NBA by hiring powerful agent Arn Tellem.
Tellem, who at the time also represented, all-star Reggie Miller and young-star
Antonio McDyess (who was drafted in 1995), gave Bryant’s bid to become the
first ever guard drafted directly out of high school instant credibility. Bryant’s
association with Tellem did not only appear to signal to teams that he was ready
for the NBA it also allowed him to select the team he wanted to play for. Tellem
was notorious among NBA general managers for what he did during the 1991
NBA Draft. In that draft Tellem forced the Sacramento Kings to trade third
overall pick Billy Owens to another team after he vowed that Owens would
never play for the Kings. In 1996 Tellem went beyond what he did in 1991 when
68
he declared his client would only play for one team, the Los Angeles Lakers.
Observers thought it was absurd for any player, let alone, a cocky high school
kid to make that type of demand, but with Tellem’s help Bryant got his wish.
Bryant was selected 13th overall in the 1996 NBA Draft by the Charlotte Hornets
and was quickly traded to the Lakers.
Players also try to send a signal to teams when they switch representatives
just before their contract expires. A recent example of a player changing
representatives came at the start of the 2005-2006 Season when Denver Nuggets’s
reserve center Francisco Elson switched from agent Dejan Vidicki to agent Calvin
Andrews of agency BDA. Elson explained to the Denver Post why he changed
agents at the start of his contract year: “I'm familiar with them; they have good
clients and big-name people. I need somebody that has a lot of power” (Spears,
2005). Elson believed that it is Andrew’s “power”, that is-his reputation, will
enable him to negotiate more money for him than his former agent would. This
reputation is represented by the big name clients (ex: Carmelo Anthony) that
Andrews and BDA represents. Elson sought to inform the Nuggets and any
other interested team that he is in the same class as Andrews and BDA’s big
name clients and therefore should be paid accordingly. If he did not belong than
why did Andrews/BDA agree to represent him?
I will compare the salary impacts of eight highly regarded representatives
with all other representatives to test if hiring a representative with a good
reputation makes a difference for players and for the presence of signaling. I
69
consider agent Arn Tellem, agent Aaron Goodwin, agent Bill Duffy, agent Mark
Bartelstein, and attorney Lon Babby because they made the Sports Business
Journal’s list of the 20 Most Influential People in Sports: Agents. I also include
sports agents David Falk, Dan Fagan, and Marc Fleisher because of their
reputations as being some of the top basketball agents.§§§ The representatives
listed have diverse cliental and offer different services. For example, Babby
allows me to compare the impact that a traditional sports agent has on a player’s
salary with that of an attorney who charges an hourly rate. I am also able to
compare the negotiating abilities of agent Mark Bartelstein, who mostly
represents lower echelon players, with Arn Tellem, who mostly represents top
echelon players.
I assign dummy variables to each representative and ask whether a
particular player is a client of that representative or not. I will omit all other
representatives and consider them as the reference group. If a highly regarded
representative sends signals to teams about the quality of their client then their
coefficient should significantly show that they are able to negotiate higher
salaries than representatives with lesser reputations.
Review of Models 2 & 3
Model 2: Sports Agents vs. Other Forms of Representation Results:
David Falk was once considered the most powerful and influential agent in the NBA. Dan
Fagan represents high number of diverse clients and is considered one of the hardest agents to
negotiate with. Marc Fleisher is known for his representation of the best foreign players who
play in the NBA.
§§§
70
Dependent Variable: Natural Log Salary
Variable Name
Standard Error
T-Ratio df=3392
Constant
Estimated Coefficient
-8.0692***
0.7094
-11.37
Games Played (GP)
0.0020***
0.0007
2.979
Points Per Game (PPG)
0.0514***
0.0036
14.39
Rebounds Per Game (RPG)
0.0536***
0.0084
6.392
Assists Per Game (APG)
0.0779***
0.0105
7.441
Blocks Per Game (BPG)
0.2279***
0.0305
7.469
Steals Per Game (SPG)
0.0035
0.0393
0.0879
Team Win %
0.3489***
0.0765
4.561
Championship
0.0036
0.0393
0.0916
All-Star
-0.0023
0.0093
-0.2479
Age
Age Squared
Experience
0.4104***
0.0383
10.72
-0.0078***
0.0007
-11.59
0.1191***
0.0092
12.96
Height
0.0151***
0.005
3.004
Race
0.0386
0.032
1.206
Foreign
0.0037
0.0618
0.0599
Forward
0.0175
0.0368
0.4756
Center
0.1114**
0.0538
2.069
1st Round Before 1995 CBA
0.2202***
0.0553
3.981
1st Round After 1995 CBA
-0.1173**
0.0464
-2.53
Contract-Extension
0.1516***
0.0443
3.425
Resign Restricted Free Agent
0.1341**
0.0543
2.469
Sign Restricted Free Agent
0.0859
0.0737
1.167
Sign Unrestricted Free Agent
-0.3369***
0.0296
-11.4
Contract Year
-0.3214***
0.0283
-11.37
0.0283
1.603
Agents
0.0453
Small Market
0.0468*
0.0276
1.694
Large Market
-0.0003*
0.0002
-1.704
This Year is 1996
0.0868*
0.0522
1.662
This Year is 1997
0.2255***
0.0524
4.306
This Year is 1998
0.001
0.0535
0.0187
This Year is 1999
0.5227***
0.0539
9.706
This Year is 2000
0.7350***
0.0525
14
This Year is 2001
0.7815***
0.0532
14.7
This Year is 2002
0.7979***
0.0533
14.99
This Year is 2003
0.8688***
0.0535
16.25
This Year is 2004
0.8656***
0.0532
16.27
.6463 (.6426)
0.6549
R-Square Adj/Standard Error
F-Statistic
* Significance at 10% Level
172.203 .
**Significance at 5% Level
*** Significance at 1% level
71
I have confidence in this model because most variables were significant
and had expected signs. The large F-Statistic indicates joint significance among
the explanatory variables used in the model. Like in Model 1, every performance
variable, except steals per game, was statistically significant at 1% level.
Personal characteristics, as expected, affect player salaries. The most
significant personal characteristic that affects a player’s salaries is their height.
Every inch taller a player is, on average, yields them a 1.51% increase in salary.
A personal characteristic that relates to height and was significant is the position
a player plays. Players who play center are paid, on average, an 11.14% salary
premium compared to players who play guard, perhaps reflecting relative
scarcity of tall players. A player’s age helps them make money when they are
young, but cost them money when they get older.
Unsurprisingly, some of the ways that a player can be acquired or
retained have affects on their salary. A team re-signing a player to a contractextension pays them 15.16% more, on average, than if they had re-signed them as
an unrestricted free agent, reflecting what a risk-adverse team pays to keep their
player from entering the free agent market at the end of their contract. Drafted
players initially will have their salary affected by their draft position. Without
regulation of rookie salaries, 1st round draft picks playing under their rookie
contracts were paid 22.02% more, on average, than players not playing under
their rookie contracts. After the implementation of the rookie salary scale 1st
round draft picks playing under their rookie contracts have been paid 11.73%
72
less, on average, than players who were not playing under their rookie contracts.
This result reaffirms Rosenbaum’s (2003) finding that younger players after the
1995 CBA have been discriminated against. Finally, there is modest evidence
that small market teams pay more, on average, to sign and keep players than
medium-market teams.
Though I have confidence in this model, there are a few significant
surprises. One surprise is that players who sign with other teams are paid less,
on average, than unrestricted free agents who re-sign with their teams. Perhaps
a majority of players who signed with new teams were not very good players
and therefore did not obtain bids from teams on the open market. Another
possible reason is that teams that re-sign their unrestricted free agents are able to
outbid other teams since the CBA permits them to pay more than other teams in
most cases. Regardless of the true reason, it appears there is, on average, no
hometown discount for teams who want to re-sign their unrestricted free agents.
I am also surprised that large market teams actually pay less, on average,
than medium market teams. Perhaps the endorsement opportunities players can
find in a major market make them more willing to accept less money to play for a
major market team. The most interesting surprise is that there is very significant
evidence that players who received a new contract after their contract year, on
average, had their first year of their new contract discounted by 32%. This
implies that general managers and team owners recognize that players typically
73
increase their production during their contract year and have discounted this
when they offer a player a new contract.
There were several explanatory variables that proved to be
statistically insignificant. The Championship variable was insignificant, so there
is no championship premium. The All-Star variable was also insignificant, so
players do not receive more money each time they earn a spot on an All-Star
team. The insignificant Race variable confirms that players who are black do not
receive higher salaries on average than white players, and the insignificant
Foreign variable confirms that foreign-born players do not receive more than
domestic players. Most importantly, an insignificant Agents variable reveals that
a player who is represented by a sports agent does not receive more, on average,
than a player who is represented in another way.
I ran the model each season to see if there were any special developments
through time. A noteworthy development is that foreign players did receive a
salary premium in 1995-1996 Season, but not afterwards, as Eschker, Siegler, and
Perez (2004) observed in their study. Another noteworthy observation is that
there was modest evidence of wage discrimination in the 1995-1996 and 19971998 season. Unlike past studies that found evidence of wage discrimination
(Kahn, 1988) against black players, this wage discrimination is directed towards
white players. In the 1995-1996 and 1997-1998 seasons black players earned
more, on average, than white players.
Variables/Season
Constant
Games Played
Points Per Game (PPG)
Rebounds Per Game (RPG)
Assists Per Game (APG)
Blocks Per Game (BPG)
Steals Per Game (SPG)
Team Win %
Championship
All-Star
Age
Age Squared
Experience
Height
Race
Foreign
Forward
Center
1st Round Before 1995 CBA
1st Round After 1995 CBA
Contract-Extension
Resign Restricted Free Agent
Sign Restricted Free Agent
1995-96
-5.9329***
(2.238)
0.0035**
(0.0017)
0.0445***
(0.0087)
0.0448**
(0.0201)
0.0514*
(0.0268)
0.0306
(0.0792)
0.1128
(0.1043)
-0.1386
(0.2039)
-0.0646
(0.1037)
0.0021
(0.0272)
0.2707**
(0.1149)
-0.0060***
(0.0020)
0.1527***
(0.0292)
0.0179
(0.0149)
0.1644*
(0.0858)
0.4159*
(0.2484)
0.0975
(0.0972)
0.1914
(0.1572)
0.2523**
(0.1107)
N/A
(N/A)
-0.0294
(0.1133)
0.0783
(0.1213)
0.0695***
1996-97
-7.2742***
(2.725)
0.0025
(0.0023)
0.0600***
(0.0120)
0.0671**
(0.0274)
0.0796**
(0.0346)
0.2028**
(0.1008)
-0.1241
(0.1427)
0.6753***
(0.2578)
0.0363
(0.1282)
0.0113
(0.0324)
0.3856***
(0.1397)
-0.0075***
(0.0024)
0.1031***
(0.0369)
0.0096
(0.0206)
0.0800
(0.1115)
0.4411
(0.2889)
-0.0040
(0.1274)
0.0399
(0.1962)
0.3969***
(0.1387)
0.2365
(0.1872)
-0.0892
(0.1681)
0.2539
(0.1613)
0.6035
1997-98
-5.1977**
(2.758)
0.0009
(0.0021)
0.0578***
(0.0118)
0.0230
(0.0245)
0.0570*
(0.0346)
0.3564***
(0.1028)
0.0371
(0.1270)
0.4870**
(0.2133)
0.0773
(0.1282)
0.0179
(0.0301)
0.2406*
(0.1418)
-0.0052**
(0.0025)
0.0975***
(0.0305)
0.0170
(0.0207)
0.1981*
(0.1054)
0.3666
(0.3090)
-0.0371
(0.1347)
0.0814
(0.2018)
0.0232
(0.1472)
-0.3925**
(0.1751)
-0.2367
(0.1659)
-0.0124
(0.1891)
-0.3560
1998-99
-7.0068***
(1.894)
-0.0022
(0.0019)
0.0592***
(0.0101)
0.0660***
(0.0240)
0.0821***
(0.0316)
0.2898***
(0.0881)
0.0411
(0.1237)
0.3317*
(0.1965)
0.0354
(0.1205)
-0.0652**
(0.0269)
0.4079***
(0.1175)
-0.0074***
(0.0020)
0.0970***
(0.0296)
0.0003
(0.0074)
-0.0295
(0.0985)
0.0586
(0.2587)
0.0538
(0.1059)
0.3155*
(0.1614)
-0.0685
(0.1606)
-0.1076
(0.1427)
0.1793
(0.1399)
0.3305
(0.2836)
0.1716
1999-2000*
-13.033***
(2.827)
0.0169***
(0.0039)
0.0508***
(0.0161)
0.0700**
(0.0356)
0.1183***
(0.0449)
0.2349*
(0.1281)
0.0068
(0.1496)
0.6882**
(0.3109)
-0.1665
(0.1554)
-0.0244
(0.0361)
0.5849***
(0.1357)
-0.0110***
(0.0024)
0.1414***
(0.0348)
0.0412*
(0.0243)
0.1633
(0.1221)
0.2098
(0.2976)
-0.0486
(0.1554)
0.0785
(0.2227)
-0.2180
(0.3483)
0.1636
(0.1735)
0.3894**
(0.1791)
0.5230
(0.4516)
0.4034
2000-01
-5.8069***
(2.377)
0.0047**
(0.0020)
0.0493***
(0.0113)
0.0535*
(0.0286)
0.1065***
(0.0331)
0.1700**
(0.1015)
-0.1103
(0.1249)
0.5292**
(0.2446)
0.0726
(0.1175)
0.0297
(0.0277)
0.2872**
(0.1219)
-0.0059***
(0.0022)
0.1148***
(0.0286)
0.0189
(0.0184)
-0.0778
(0.0991)
-0.1182
(0.2250)
0.0048
(0.1125)
0.2117
(0.1694)
-0.0683
(0.2804)
-0.3137**
(0.1445)
0.2663**
(0.1341)
0.3521
(0.3114)
0.5437
2001-02
-10.168***
(2.231)
0.0038**
(0.0018)
0.0310***
(0.0099)
0.0610***
(0.0231)
0.0609**
(0.0309)
0.1826**
(0.0850)
-0.0562
(0.1154)
0.0995
(0.2330)
-0.0058
(0.1162)
0.0620**
(0.0250)
0.6266***
(0.1149)
-0.0119***
(0.0021)
0.1448***
(0.0268)
0.0197
(0.0183)
0.0294
(0.0903)
-0.2688
(0.1806)
-0.0857
(0.1123)
0.0404
(0.1605)
-0.2303
(0.2727)
-0.1486
(0.1178)
0.3729***
(0.1269)
0.1453
(0.1808)
0.2593
2002-03
-9.2431***
(2.22)
0.0017
(0.0018)
0.0528***
(0.0105)
-0.0035
(0.0262)
0.0525*
(0.0295)
0.2826***
(0.0876)
0.0185
(0.1118)
0.2406
(0.2525)
0.1127
(0.1292)
0.0033
(0.0259)
0.5208***
(0.1102)
-0.0102***
(0.0020)
0.1656***
(0.0271)
0.0271
(0.0179)
0.0034
(0.0940)
-0.2064
(0.1572)
0.0455
(0.1096)
0.1303
(0.1526)
-0.0270
(0.3562)
-0.1123
(0.1304)
0.3463***
(0.1331)
0.3422
(0.1539)
0.2813**
2003-04
-9.0799***
(2.205)
-0.0024
(0.0018)
0.0560***
(0.0102)
0.0499***
(0.0262)
0.0781**
(0.0341)
0.2173**
(0.0888)
-0.1041
(0.1078)
0.2537
(0.2420)
-0.0480
(0.1114)
-0.0342
(0.0281)
0.5564***
(0.1093)
-0.0101***
(0.0019)
0.1535***
(0.0251)
0.0165
(0.0170)
-0.0304
(0.0951)
-0.0095
(0.1393)
-0.0567
(0.1067)
-0.0759
(0.1548)
N/A
(N/A)
0.1177*
(0.1204)
0.2005**
(0.1126)
0.2658**
(0.1352)
0.3737
2004-05
-7.7602***
(2.082)
-0.0001
(0.0018)
0.0479***
(0.0114)
0.0654*
(0.0265)
0.0730**
(0.0311)
0.0972**
(0.0882)
-0.0049
(0.1146)
0.4026
(0.2587)
-0.0173
(0.1071)
-0.0847
(0.1132)
0.3939***
(0.1009)
-0.0076***
(0.0018)
0.1469***
(0.0253)
0.0285
(0.0168)
-0.0397
(0.0887)
0.0519
(0.1187)
-0.0747
(0.1079)
-0.1069
(0.1579)
N/A
(N/A)
-0.0105*
(0.1188)
0.3566**
(0.1204)
0.4447**
(0.1250)
0.1507
75
*The results in the 1999-2000 Season may be a bit misleading because of the impact of the NBA Lockout in 1998. The coefficients of
the performance statistics may be adversely affected by the fewer games played in the 1998-1999 season.
Model 3: Highly Regarded vs. Ordinary Representatives Results:
Dependent Variable: Natural Log Salary
Variable Name
Estimated Coefficient
Standard Error
T-Ratio df = 3384
Constant
-8.1137***
0.708
-11.46
Games Played (GP)
0.0017***
0.0007
2.576
Points Per Game (PPG)
0.0506***
0.0036
14.06
Rebounds Per Game (RPG)
0.0544***
0.0084
6.473
Assists Per Game (APG)
0.0744***
0.0106
7.001
Blocks Per Game (BPG)
0.2259***
0.0305
7.407
Steals Per Game (SPG)
0.0072
0.0392
0.1825
0.3597***
0.0763
4.716
Championship
0.0033
0.0394
0.8374
All-Star
-0.0026
0.0093
-0.2759
Age
0.4140***
0.0382
10.84
Age Squared
-0.0078***
0.0007
-11.59
Experience
0.1143***
0.0092
12.39
Height
Team Win %
0.0144***
0.005
2.865
Race
0.0566*
0.0321
1.767
Foreign
0.0634
0.0655
0.9675
Forward
0.0181
0.0372
0.4874
Center
0.1029*
0.0545
1.886
1st Round Before 1995 CBA
0.2057***
0.0551
3.736
1st Round After 1995 CBA
-0.1324***
0.0463
-2.858
Contract-Extension
0.1472***
0.0441
3.339
Resign Restricted Free Agent
0.1470***
0.0542
2.713
0.0905
0.0736
1.231
Sign Unrestricted Free Agent
-0.3360***
0.0295
-11.38
Contract Year
Sign Restricted Free Agent
-0.3219***
0.0282
-11.44
Mark Bartelstein
0.0885*
0.0513
1.725
Aaron Goodwin
0.2238***
0.0873
2.565
Bill Duffy
0.1005
0.071
1.414
Arn Tellem
0.1766***
0.0418
4.226
David Falk
0.1925***
0.0442
4.358
-0.0024
0.1122
-0.2173
Lon Babby
0.096
0.0789
1.216
Dan Fegan
Marc Fleisher
0.1632**
0.0649
2.514
Small Market Team
0.0223
0.0244
0.9159
Large Market Team
-0.0003**
0.0001
-1.985
This Year is 1996
0.0813
0.052
1.565
This Year is 1997
0.2162***
0.0522
4.14
This Year is 1998
-0.0153
0.0534
-0.286
This Year is 1999
0.4976***
0.0538
9.257
77
This Year is 2000
0.7098***
0.0524
13.54
This Year is 2001
0.7510***
0.0532
14.11
This Year is 2002
0.7630***
0.0535
14.28
This Year is 2003
0.8368***
0.0536
15.61
0.8363***
0.0535
15.64
.6506 (.6462)
0.65172
This Year is 2004
R-Squared (Adj)/Standard Error
F-Statistic
*Significance at 10%
146.558
**Significance at 5%
***Significance at 1%
I am also confident in this model not only because many of the variables
have correct signs, but the same variables from the first and second models are
statistically significant in this model. Players should earn more, on average,
when they play more games, score more points, retrieve more rebounds,
distribute more assists, and block more shots each game. Teams should pay a
premium to keep their players from entering the free agent market by giving
them contract-extensions and to sign experienced players. The results also
reaffirm that 1st round draft picks after 1995 have had their wages suppressed
while they are on their rookie contract.
The results of this model also present the same surprises as the second
model. There is again strong evidence that teams pay less to sign another team’s
unrestricted free player than to re-sign their own. Once again, there is modest
evidence that larger market teams pay less, on average, than medium market
teams. Finally, this model shows again that teams discount the performance of
players who just came off their contract year.
The results of this model present one major surprise. There is modest
evidence of wage discrimination against white players. Black players are paid
78
5.66% more, on average, than white players. Although this is an intriguing
result, it may not be too important if the annual results show that wage
discrimination has tapered off as we approach the present.
The intent of this model was to see if player representatives with good
reputations earn their clients more, on average, than lesser regarded player
representatives because their reputation send clearer signals to teams about
quality of their clients. The results reveal that players represented by highly
regarded representatives indeed receive higher salaries than players represented
by lesser regarded representatives over a long period of time. For instance, Arn
Tellem and David Falk clients receive, on average, salaries 17.7% and 19.3% more
than representatives with lesser regarded reputations, respectively. Clients of
attorney Lon Babby receive, on average, 9.6% more than they would with a
typical representative; however this result is not significant. Since many highly
regarded representatives are able to earn their clients extra income that also
means that teams are willing to pay for it. Therefore another interpretation of these
representative coefficients is that teams are willing to pay clients of highly regarded
representatives more, on average, than clients of lesser regarded representatives. Teams
are willing to pay clients of Arn Tellem and David Falk, on average, 17.7% and
19.3% more than they would to clients of lesser regarded representatives. This
indicates that highly regarded agents do send signals to teams about the quality
of the player they are interested in. If highly regarded representatives did not
79
send signals to teams then teams would not consistently pay their clients more,
on average, than they would for clients of other representatives.
I also ran this model for each season to see if there any special
developments. The Race variable has indeed tapered off to the point where there
is parity in the pay of white and black players. The performance of highly
regarded agents like Arn Tellem has fluctuated during the observation period.
Before the 1999 CBA his clients consistently received a significant salary
premium over players represented by lesser regarded agents. After the 1999
CBA Tellem has not significantly outperformed lesser regarded agents. Some
highly regarded agents have not outperformed other agents on an annual basis.
Mark Bartelstein and David Falk both have negotiated many contracts over the
last ten years, but have not outperformed lesser regarded agents on a year to
year basis. Attorney Lon Babby has been as competitive as lesser regarded
agents and even outperformed them in the 2004-2005 Season. These results
reveal that agents are like players; their performance varies from season to
season.
Variables/Season
Constant
Games Played (GP)
Points Per Game (PPG)
Rebounds Per Game (RPG)
Assists Per Game (APG)
Blocks Per Game (BPG)
Steals Per Game (SPG)
Team Win %
Championship
All-Star
Age
Age Squared
Experience
Height (inches)
Race
Foreign
Forward
Center
1st Round Before 1995 CBA
1st Round After 1995 CBA
1995-96
-5.8293***
(2.2380)
0.0036**
(0.0017)
0.0425***
(0.0089)
0.0421**
(0.0205)
0.0524*
(0.0271)
-0.0060
(0.0811)
0.1350
(0.1057)
-0.1365
(0.2080)
-0.0741
(0.1049)
0.0003
(0.0272)
0.2671***
(0.1163)
-0.0060***
(0.0020)
0.1536***
(0.0296)
0.0187
(0.0151)
0.1887**
(0.0876)
0.3913
(0.2894)
0.1252
(0.0966)
0.2381
(0.1572)
0.2653**
(0.1108)
N/A
1996-97
-6.7055**
(2.6780)
0.0023
(0.0023)
0.0569***
(0.0119)
0.0644**
(0.0271)
0.0926***
(0.0345)
0.1888*
(0.1008)
-0.1249
(0.1425)
0.7167***
(0.2536)
0.0081
(0.1269)
0.0121
(0.0307)
0.3704***
(0.1377)
-0.0074***
(0.0024)
0.1134***
(0.0368)
0.0034
(0.0207)
0.0891
(0.1118)
0.4346
(0.3059)
0.0529
(0.1274)
0.1480
(0.1969)
0.4164***
(0.1374)
0.2504
1997-98
-5.3096*
(2.7210)
0.0001
(0.0021)
0.0564***
(0.0118)
0.0242
(0.0247)
0.0612*
(0.0348)
0.3490***
(0.1038)
0.0373
(0.1270)
0.5082**
(0.2148)
0.0540
(0.1294)
0.0162
(0.0290)
0.2578***
(0.1424)
-0.0053***
(0.0025)
0.0905***
(0.0307)
0.0154
(0.0209)
0.2165**
(0.1062)
0.3712
(0.3180)
-0.0271
(0.1360)
0.0816
(0.2048)
0.0245
(0.1473)
-0.4003**
1998-99
-6.9986***
(1.8540)
-0.0026
(0.0019)
0.0571***
(0.0101)
0.0650***
(0.0237)
0.0775**
(0.0320)
0.3038***
(0.0871)
0.0484
(0.1231)
0.3014
(0.1967)
0.0327
(0.1200)
-0.0647**
(0.0263)
0.4037***
(0.1166)
-0.0071***
(0.0020)
0.0825***
(0.0301)
0.0000
(0.0075)
-0.0127
(0.0975)
0.0068
(0.2676)
0.0336
(0.1046)
0.2973*
(0.1619)
-0.0510
(0.1592)
-0.1366
1999-2000
-12.6520***
(2.8250)
0.0164***
(0.0039)
0.0512***
(0.0162)
0.0762**
(0.0358)
0.1182***
(0.0458)
0.2290*
(0.1280)
0.0038
(0.1497)
0.6828**
(0.3116)
-0.2034
(0.1567)
-0.0209
(0.0357)
0.5678***
(0.1356)
-0.0105***
(0.0024)
0.1272***
(0.0352)
0.0356
(0.0245)
0.1766
(0.1214)
0.3511
(0.3034)
0.0103
(0.1579)
0.1226
(0.2254)
-0.1989
(0.3484)
0.1399
2000-01
-5.6816**
(2.4360)
0.0046**
(0.0020)
0.0470***
(0.0116)
0.0586*
(0.0299)
0.1116***
(0.0341)
0.1630
(0.1028)
-0.1050
(0.1272)
0.5709**
(0.2507)
0.0534
(0.1198)
0.0356
(0.0278)
0.2864**
(0.1254)
-0.0058***
(0.0022)
0.1078***
(0.0292)
0.0171
(0.0189)
-0.0650
(0.1000)
-0.0853
(0.2307)
0.0166
(0.1233)
0.2164
(0.1845)
-0.0144
(0.2838)
-0.3266**
2001-02
-10.4460***
(2.2360)
0.0038**
(0.0018)
0.0303***
(0.0100)
0.0612***
(0.0232)
0.0511
(0.0314)
0.1943**
(0.0849)
-0.0448
(0.1168)
0.0165
(0.2351)
0.0087
(0.1175)
0.0609**
(0.0250)
0.6266***
(0.1154)
-0.0116***
(0.0021)
0.1300***
(0.0271)
0.0208
(0.0184)
0.0233
(0.0907)
-0.2427
(0.1887)
-0.1042
(0.1125)
-0.0270
(0.1621)
-0.3008
(0.2746)
-0.1837
2002-03
-9.1095***
(2.2390)
0.0015
(0.0019)
0.0544***
(0.0108)
-0.0112
(0.0267)
0.0478
(0.0304)
0.3009***
(0.0891)
0.0438
(0.1131)
0.2519
(0.2552)
0.1190
(0.1316)
0.0014
(0.0261)
0.5111***
(0.1118)
-0.0099***
(0.0020)
0.1629***
(0.0275)
0.0236
(0.0186)
-0.0023
(0.0950)
-0.1520
(0.1711)
0.1055
(0.1186)
0.1642
(0.1687)
-0.0886
(0.3639)
-0.0904
2003-04
-9.1004***
(2.1600)
-0.0020
(0.0018)
0.0564***
(0.0102)
0.0456*
(0.0261)
0.0651*
(0.0341)
0.2257**
(0.0888)
-0.0819
(0.1068)
0.2167
(0.2386)
-0.0700
(0.1110)
-0.0388
(0.0280)
0.5292***
(0.1096)
-0.0098***
(0.0019)
0.1629***
(0.0255)
0.0200
(0.0168)
-0.0067
(0.0949)
0.0846
(0.1504)
-0.0857
(0.1051)
-0.1585
(0.1525)
-0.0273
(0.5542)
0.0406
2004-05
-7.8685***
(2.0620)
-0.0004
(0.0018)
0.0489***
(0.0113)
0.0675***
(0.0262)
0.0673**
(0.0316)
0.0946
(0.0887)
-0.0141
(0.1169)
0.4021
(0.2583)
-0.0187
(0.1081)
-0.1092
(0.1139)
0.3889***
(0.1012)
-0.0075***
(0.0018)
0.1470***
(0.0257)
0.0280**
(0.0166)
-0.0189
(0.0896)
0.0445
(0.1258)
-0.1130
(0.1039)
-0.1407
(0.1520)
N/A
(N/A)
0.0012***
81
Contract-Extension
Resign Restricted Free Agent
Sign Restricted Free Agent
Sign Unrestricted Free Agent
Contract Year
Mark Bartelstein
Aaron Goodwin
Bill Duffy
Arn Tellem
David Falk
Marc Fleisher
Lon Babby
Dan Fegan
Small Market Team
Large Market Team
R-Squared (Adj)
Standard Error
*Significance at 10%
(N/A)
-0.0148
(0.1139)
0.0905
(0.1229)
0.1285***
(0.2072)
-0.4273**
(0.0865)
-0.2172
(0.0881)
0.0428
(0.2177)
0.0126
(0.3252)
-0.3081
(0.3244)
-0.0377**
(0.1339)
0.2229
(0.1128)
0.2715
(0.3558)
0.2000
(0.5248)
0.2454
(0.4221)
0.0842
(0.0653)
0.0000
(0.0000)
.734
(.7028)
0.5093
(0.1856)
-0.1682
(0.1669)
0.2153
(0.1601)
0.5373
(0.3986)
-0.3824***
(0.1066)
-0.0471
(0.1034)
0.1066
(0.2441)
0.8535**
(0.3447)
-1.1250**
(0.3875)
0.2395
(0.1558)
0.1874
(0.1460)
0.4956
(0.4301)
0.0999
(0.4847)
0.6784*
(0.3938)
0.0178
(0.0826)
0.0000
(0.0000)
.6412
(.5992)
0.6586
**Significance at 5%
(0.1762)
-0.2616
(0.1669)
-0.0003
(0.1896)
-0.5913
(0.6898)
-0.3543***
(0.0986)
-0.7468***
(0.1011)
0.1010
(0.1774)
0.2609
(0.3815)
-0.3062
(0.3081)
0.3011**
(0.1444)
0.1967
(0.1400)
0.2728
(0.5074)
0.1651
(0.4004)
0.5234
(0.3436)
0.0453
(0.0840)
0.0000*
(0.0000)
.6723
(.6351)
0.6612
(0.1440)
0.1974
(0.1397)
0.3290
(0.2815)
-0.1140
(0.6163)
-0.2060**
(0.0846)
-0.1978**
(0.0855)
0.0651
(0.1605)
0.5129
(0.2830)
-0.3694
(0.2802)
0.3306***
(0.1267)
0.2009
(0.1235)
0.6303
(0.4528)
-0.0340
(0.3105)
-0.0177
(0.3480)
-0.0168
(0.0725)
0.0000
(0.0000)
.6723
(.6351)
0.8584
***Significance at 1%
(0.1731)
0.3622**
(0.1789)
0.4676
(0.4500)
0.2434
(0.6406)
-0.6720***
(0.1094)
0.0004
(0.1111)
0.3186
(0.2034)
0.0688
(0.3460)
0.2301
(0.3107)
0.4838***
(0.1679)
0.1522
(0.1704)
-0.6863
(0.6450)
0.0311
(0.3442)
0.4873
(0.4434)
0.2290**
(0.1144)
0.2167
(0.1139)
.6444
(.6074)
0.6476
(0.1473)
0.2550*
(0.1354)
0.3788
(0.3180)
0.4948
(0.4859)
-0.3635***
(0.0839)
-0.2103**
(0.0889)
0.1652
(0.1604)
-0.2328
(0.2444)
-0.0659
(0.2532)
0.1508
(0.1293)
0.1248
(0.1283)
0.0866
(0.3907)
-0.0690
(0.2293)
0.0302
(0.1993)
-0.0747
(0.0878)
-0.0490
(0.0855)
.6444
(.6047)
0.6476
(0.1186)
0.3693***
(0.1270)
0.1343
(0.1837)
0.3137
(0.2043)
-0.0983
(0.0842)
-0.6275***
(0.0857)
-0.1589
(0.1313)
0.3136
(0.2162)
0.2435
(0.1993)
0.0980
(0.1150)
0.1723
(0.1222)
0.3225
(0.6144)
0.0066
(0.1836)
0.2515*
(0.1456)
0.0390
(0.0835)
-0.0340
(0.0781)
.7019
(0.669)
0.5729
(0.1321)
0.3648***
(0.1350)
0.3478**
(0.1576)
0.3223*
(0.1789)
-0.1237
(0.0948)
-0.6736***
(0.0932)
-0.0690
(0.1359)
0.1632
(0.2251)
0.2367
(0.1924)
0.0420
(0.1123)
0.1498
(0.1317)
-0.3035
(0.2990)
0.1700
(0.1690)
0.1492
(0.1376)
0.1160
(0.0848)
0.0190
(0.0806)
.6927
(.6588)
0.5892
(0.1203)
0.1918*
(0.1127)
0.3240**
(0.1354)
0.3756**
(0.1518)
-0.1282
(0.0891)
-0.7078***
(0.0834)
0.0545
(0.1270)
0.4467**
(0.2136)
0.4786***
(0.1453)
0.0745
(0.1039)
-0.0078
(0.1355)
-0.2575
(0.2104)
0.2009
(0.1763)
0.1538
(0.1316)
-0.0879
(0.0783)
-0.0649
(0.0753)
.7285
(.6981)
0.5371
(0.1191)
0.3906***
(0.1214)
0.4754
(0.1256)
0.1471***
(0.1329)
-0.3085**
(0.0866)
-0.1836
(0.0794)
0.0823
(0.1166)
0.2611**
(0.2400)
0.2632
(0.1329)
0.0665
(0.1107)
0.1804
(0.1558)
0.1523
(0.1998)
0.2229*
(0.1740)
0.2208
(0.1270)
-0.0699
(0.0778)
-0.0007
(0.0764)
.7024
(.6718)
0.5533
Lessons from the Three Models:
The average NBA player’s salary is not completely based on how they
perform on the court. In fact, an average player’s productivity only accounts for
just over half their salary, so their non-performance characteristics are nearly as
important as their on the court productivity.
The collective bargaining agreement has repercussions on player salaries.
The agreement made by players and owners in the 1995 CBA to control the
salaries of 1st round draft picks coming into the league hurt these draft picks
financially when they signed their first contracts. Instead of receiving a salary
premium during their rookie contract, 1st round draft picks now face wage
suppression. The agreement to allow teams to exceed the salary cap to re-sign
their own players gives players an incentive to re-sign with their own team since
they will make more, on average, if they stay than if they go to another team in
free agency.
NBA players represented by sports agents do not receive a larger salary,
on average, than players who are represented in some other way. However,
some individual representatives, on average, earn their clients larger salaries
than other representatives since some representatives are better negotiators than
others. Highly regarded representatives are able to capitalize off free agency by
successfully using a variety of tactics, such as overstating the demand for their
client, to take advantage of the presence of asymmetric information. They can
83
also make risk-adverse teams more willing to accept their demands because their
reputation of representing a certain type of player can reduce a team’s fear of
signing a bad player.
Players represented by lesser regarded representatives either value the
services their representative provides or still believe that an agent representation
is their only viable option. Clients of SFX, Octagon, or any other large
conglomerate may appreciate value added services more than the higher salary
they would receive, on average, if they had a powerful representative. Clients of
agents with a small cliental may value the friendship they have with their agent
more than having an aloof, but powerful representative. On the other hand, a
player could scrap their friendship and save themselves a lot of money in annual
fees by switching from their representative to an attorney like Lon Babby. They
are statistically as competent as most sports agent, but charges their clients a lot
less than them. However, most players shy away from hiring an attorney like
Babby because they are uncertain about an attorney’s ability to negotiate or
willingness to go the extra mile for their client. Marginal players want someone
who will put in extra effort because they usually have difficulty finding a spot on
a team each season. Players entering the league want a skill salesman because
their draft position often hinges on their representative’s ability to persuade
teams that their client is better than another player. Risk-adverse players will
gravitate towards a known entity until they are sure that an attorney can do as
competent job.
84
It may take time for players to realize that attorneys are as competent as
the typical sports agent since they are still relatively new to the sports industry.
It took two decades after the first prominent sports agent in the NBA before a
majority of players even considered having their own personal representative
negotiating their contracts. However, once that happened nearly every player
sought a representative to help negotiate their contracts. Until then it does not
matter how a player is represented, but who represents a player.
Fun with the Residuals:
I will apply Model 3 to individual players in playing in the league at the
start of the 2005-2006 Season. The performance statistics shown in the tables are
from the 2004-2005 Season and the salaries from the 2005-2006 season. Since the
coefficients came from a log-liner model I will undertake a series of
transformations to get the left hand side and right hand side so I can calculate
estimated values (in dollars) and residuals (in dollars). The following steps
depict how the transform from log-linear coefficients to dollars will be made.
(1). LN(Salaryi/1,000,000) = f(xi) + e
(2). ELN(Salaryi/1,000,000) = Ef(xi) + e
(3). Salaryi/1,000,000 = Ef(xi) + e
(4). 1,000,000 x (Salaryi/1,000,000) = 1,000,000 x Ef(xi) + e
(5). Salaryi = 1,000,000 x Ef(xi) + e
85
Where f(xi)=the model output value for player “i” and e = amount a player is
underpaid or overpaid by
In this section I answer these four questions: “Who are the most
underpaid players”, “Who are the most overpaid players?” “How much should
some of the up and coming players receive when they become free agents?”
“How much should some of the best free agents in the 2006 off-season receive?”
Which Players Should Ask For a Raise: The Best Values in the NBA
Rank
1
2
3
4
5
6
7
8
9
10
Name
Kevin Garnett
Shaquille O'Neal
Dirk Nowitzki
Jermaine O'Neal
Tracy McGrady
Tim Duncan
Amare Stoudamire****
Ron Artest
Allen Iverson
Elton Brand
Salary
$18,000,000
$20,000,000
$13,843,157
$16,440,000
$15,694,250
$15,845,156
$2,589,022
$6,694,737
$15,356,250
$12,630,095
Estimated Salary
$39,324,593
$40,806,124
$27,261,565
$28,201,349
$27,134,154
$26,885,507
$11,245,734
$14,251,651
$22,793,402
$19,275,618
Underpaid By…
$21,324,593
$20,806,124
$13,418,408
$11,761,349
$11,439,904
$11,040,351
$8,656,712
$7,556,914
$7,437,152
$6,645,523
Interestingly, many of the league’s premier players are some of its most
underpaid players. The large discrepancy between these players’ estimated
salaries and their actual salaries illustrate the large impact that the maximum
salary restrictions in the 1999 CBA had. If no maximum salary restrictions
existed it would be likely that many of these players would have earned salaries
close to their estimated salaries.
****
Phoenix Suns’ center Amare Stoudamire would likely have a higher projected salary if he was not still
playing under his rookie contract.
86
Many agents, in retrospect, were justified in vehemently resisting
proposed maximum salary restrictions during the 1998 NBA Lockout. These
results show that maximum salary restrictions has taken away income from
agents, as they feared, since they cannot negotiate mega deals like they had in the
past. If an agent had negotiated Shaquille O’Neal a contract where he would
earn his estimated salary in a season he would charge O’Neal a $1,634,404.96 fee
for that season. Instead the agent will likely receive $800,000 from O’Neal
(assuming the agent charged a 4% annual fee). It is safe to assume if the league
ever offered to remove maximum salary restrictions in a future CBA they would
have the agents’ support.
Best Values in the NBA, Accounting for the Maximum Salary Restrictions:
Rank
1
2
3
4
5
6
7
8
9
10
Name
Amare Stoudamire
Ron Artest
LeBron James
Marcus Camby
Dwyane Wade
Yao Ming
Ben Wallace
Tayshaun Prince
Primoz Brezec
Mike Bibby
Salary
$2,589,022
$6,694,737
$4,621,800
$8,157,143
$3,031,920
$5,594,906
$6,000,000
$1,763,115
$2,400,000
$11,500,000
Estimated Salary
$11,245,734
$14,251,651
$10,525,154
$13,741,409
$8,133,038
$9,684,486
$10,040,559
$5,212,945
$5,740,038
$14,712,269
Underpaid By…
$8,656,712
$7,556,914
$5,903,354
$5,584,266
$5,101,118
$4,089,580
$4,040,559
$3,449,830
$3,340,038
$3,212,269
This category includes players who do not make a maximum salary in the
2005-2006 Season. The logic behind this grouping is that a player cannot be
overpaid in a real sense if they already make as much as they are allowed to
make. The table shows that many of the most underpaid players are productive,
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young players who are still playing under their rookie contract. If the rookie
scale was never implemented in the 1995 CBA then it is likely that many of these
players would currently make their estimated salaries as part of a $100 million+
multiyear, rookie contract.
Players Who Should Be Most Happy with their Contract:
The Most Grossly Overpaid
Rank
Salary
Estimated Salary
Overpaid By…
Anfernee Hardaway
$15,750,000
$2,453,182
$13,296,818
2
Eddie Jones
$14,560,000
$4,813,280
$9,746,720
3
Antonio Davis
$13,000,000
$3,827,508
$9,172,492
4
Grant Hill
$15,694,250
$6,977,857
$8,716,393
1
Name
5
Keith Van Horn
$15,694,250
$7,255,885
$8,438,365
6
Tim Thomas
$13,975,000
$6,084,275
$7,890,725
7
Michael Redd
$12,000,000
$4,783,985
$7,216,015
8
Joe Johnson
$12,000,000
$5,577,431
$6,422,569
9
Jalen Rose
$15,694,250
$9,512,063
$6,182,187
10
Bobby Simmons
$8,103,448
$2,916,879
$5,186,569
Most observers of the NBA would agree that these ten players are among
the most overpaid in basketball. The teams that signed these players were
convinced that they would be highly productive players for several years to
come. However, most of these teams overestimated these players’ abilities,
failed to anticipate injury problems, or simply overpaid to keep them or acquire
them. Players like Anfernee Hardaway and Grant Hill experienced several
injuries that caused their skills to significantly deteriorate. Tim Thomas, Jalen
Rose, Eddie Jones, and Keith Van Horn were players who never lived up to great
expectations. Finally, players such as Michael Redd, Joe Johnson, and Bobby
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Simmons were offered too much money in free agency by teams (Milwaukee
Bucks for Simmons and Redd) that were desperate for talent.
These results are a good illustration of the negative consequences of
guaranteed contracts on teams. If these overpaid players’ teams were able to
directly tie pay to current performance, they would save money, since these
players are not very productive. However, since NBA contracts are guaranteed,
players are paid for potential, not production.
Players with Big Contract-Extensions to Sign in the Future: Rising Stars
Player
PPG
RPG
APG
Salary
Estimated Salary Extension
LeBron James
27.2
7.4
7.2
$4,621,800
$13,920,576
Dwyane Wade
24.1
5.2
6.8
$3,031,920
$10,756,762
Carmelo Anthony
20.8
5.7
2.6
$3,713,640
$5,238,256
Chris Bosh
16.8
8.9
1.9
$3,348,000
$5,972,258
Players who made this category are already elite players, but are still
playing under their respective rookie contracts. All four of these players will
enter the free agent market during the summer of 2007, and it is likely that these
players’ respective teams will do all they can to retain them. The safest method
these teams can use is to sign their players to large contract-extensions. The
estimated salaries on the far right of the table reflect the first year salary teams
should offer to their young players to entice them to not enter free agency††††.
††††
I adjusted each player’s nonperformance characteristics to reflect that they signed a contract-extension
in 2007. I assumed that league salaries did not change from the level of 2005-2006 and assumed that they
did their performance statistics were the same.
89
I doubt that Wade, Anthony, and Bosh will earn salaries that small in the
first year of their new contract-extensions. Each player is likely to receive a
maximum contract, which will give them a first year salaries above $12 million
(Coon, 2005).
The 2006 Free Agent Class‡‡‡‡
Player Name
PPG
RPG
APG
BPG
SPG
Salary Re-Sign
Predrag Stojakovic
20.1
4.3
2.1
0.2
1.2
$6,450,012
Ben Wallace
9.7
12.2
1.7
2.4
1.4
$10,542,357
The weak 2006 free agent class is led by Indiana Pacers’ forward Peja
Stojakovic. Stojakovic was acquired by the Pacers in a midseason trade with the
Sacramento Kings. Before the Stojakovic trade the largest question plaguing
Kings fans was how much the Kings should pay Stojakovic. It is difficult to
estimate Stojakovic’s market value subjectively because he is not good enough to
warrant a maximum contract, but was an elite player in the past. To make things
more complicated, Stojakovic is one of the best shooters in the world, yet he does
little else to help his team. The perception among experts is that many teams will
offer Stojakovic a large contract when he becomes a free agent because great
shooters are hard to come by.
The model estimates that Stojakovic’s market value at the start of the 20052006 Season was $9,541,732. However, since this is his contract year he will need
to play better this year or perhaps risk losing market value as general managers
‡‡‡‡
I adjusted Stojakovic and Wallace’s nonperformance statistics to reflect the start of the 2006-2007
season
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discount his performance. If Stojakovic performed as well as he did in the 20042005 Season he should be offered no more than $6,450,012 by the Pacers in the
first year of a new, long-term contract.
Detroit Pistons’s center Ben Wallace will not command as much attention
as Stojakovic will because he is expected to re-sign with the Pistons. Wallace is a
below average offensive, player, but is arguably the best defender in the league.
The problem for the Pistons’s is that Wallace is 31 years old and may soon be on
the downside of his career. If Wallace performed as well as he did in the 20042005 Season he should be offered no more than $10,542,357 by the Pistons in the
first year of a new, short-term contract.
Team Investment: Wins and Revenue
A lot of effort was spent earlier in answering why players, on average,
earned multimillion dollar salaries. Now it is time to focus those who pay the
players, the owners. Why are team owners willing to pay players the millions
that they and their representatives demand? I will run two models to see if
owners who increase the amount they spend on payroll receive a return on their
investment, which I define as an increase in wins or in revenue.
I have compiled the data for these two models from a variety of sources.
Team revenue data is from Fort’s website, team performance statistics is from
Steele, and home attendance data is from Munsey and Suppes
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Model 1: Payroll and Wins:
Win % = β1+β2 Payroll + β3Home Attendance % + β4 Contract Year + β5 Small
Market + β6 Large Market + β7 OFG% + β8 DFG% + β9 3PT% +β10 FT% +β11 PPG
+ β12 ORPG + β13 DRPG + β14 APG + β15 BPG + β16 SPG + β17 TPG + β18 Season +
e
Where payroll is in millions of dollars
Dependant Variable: Win %
A useful measure of a team’s success is their overall winning percentage.
Owners typically seek to maximize their team’s winning percentage and I am
interested in whether a team’s payroll has an impact on their overall winning
percentage.
Independent Variables
Payroll:
Teams that spend money appear to win more often than frugal teams
since they usually have more talented players. Before the 2003-2004 Season,
Minnesota was an average team that had been eliminated from the first round of
the NBA Playoffs for the past seven consecutive seasons, with a team payroll
often in the bottom third of the league. In the summer of 2003, Minnesota
undertook a series of roster upgrades to appease unhappy, superstar Kevin
Garnett. The roster upgrades came at a very high cost, as the team payroll
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jumped from $59 million to $72 million, 4th in the league. In the 2003-2004
Season, Minnesota’s record improved by 7 games, and advanced all the way to
the Western Conference Finals.
Team
Minnesota
Δ Payroll
$12,836,427
2002-2003 Record
51-31
2003-2004 Record
58-24
Improvement
+7 Games
I expect teams that spend more on payroll to win more games since their
expenditures often goes towards new players that can help them win.
Home Attendance %:
NBA teams tend to perform better at home than they do on the road.
There are many theories on why a team plays better at home, and one main
explanation is that home crowd support gives the home team additional energy
and motivation to perform well. This energy argument seems to explain why
Sacramento Kings play well at home. Sacramento’s Arco Arena is viewed
around the NBA as the place where the home team has the greatest home court
advantage. Arco is sold out for every Kings game and the enthusiastic crowd is
amongst the loudest-if not the loudest in the league. Since the Kings became a
perennial playoff team (since the 1998-1999 Season) they have won nearly 79% of
their home games. Even when the Kings were not a very good team their games
were sold out and the team had an impressive home record. The 1994-1995
Sacramento Kings were not a very good team, yet they were in playoff
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contention up to the last game of the season because they won enough games at
home to compensate for their poor road record.
Team
Overall Record
Home Record
Road Record
Attendance %
Sacramento
39-43 (.476)
27-14 (.658)
12-29 (.292)
100%
One reason that Arco Arena almost always sells out for Kings games is
that it is a small arena compared to others around the league. Arco Arena seats
17,317 fans for each Kings game while the Palace at Auburn Hills (Detroit
Pistons) can seat over 22,000 people. Another reason for the sell outs is that the
Kings’s fan base is the most enthusiastic in the league. The more enthusiastic a
team’s fan base is the likelier they are going to attend their team’s home games
during the course of a season. Since all arenas have different seating capacities
for NBA games it is not appropriate to use total home attendance to gauge fan
enthusiasm. I will instead use the percentage of total home attendance during a
season as a gauge for team fan enthusiasm for that season. The lower the
percentage of total home attendance the less enthusiastic fans were during the
season and vice versa. The more enthusiastic fans were greater the advantage
the team should had when they played at home. Therefore, I expect that a higher
percentage of total home attendance will translate into more wins for a team.
Contract Year:
Since individual players have an incentive to perform better during their
contract year and typically respond to these incentives, teams with many players
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in their contract year may play better than they normally would. One instance
where a team exceeded expectations and had a lot of players in their contract
year was the 2004-2005 Seattle Supersonics. Seattle was not expected to make the
playoffs in a highly competitive Western Conference, yet they defied predictions
and were one of the most improved teams in the NBA. The main reasons behind
the Supersonics’ improvement were debatable since there was little personnel
turnover during the 2004 off-season. Optimistic followers of the team pointed to
the team finally responding to the coaching of Head Coach Nate McMilian and
the team’s improved chemistry. Cynics of Seattle’s improved play pointed to the
fact that Seattle had an unusually high number of players who were in their
contract year, including star guard Ray Allen.
Team
2003-2004 Record
2004-2005 Record
Improvement
# of Players in Contract Year
37-45
52-30
+15 Games
8 (75% of roster)
Seattle
I expect that teams with more players in their contract year the better the team
will perform.
Market Size:
At first glance it may seem ridiculous that the region a team plays in can
affect whether they win or lose a game. However, many cynical fans believe that
small market teams have a disadvantage over large market teams. The
underlining argument behind this cynicism is that the NBA emphasizes
marketing to such an extreme that they would even compromise the integrity of
95
the game to give its large market teams a better chance to succeed. The more
successful large market teams are the more exposure opportunities the league
would have since television ratings would be higher. The NBA could give large
market teams an advantage over small market teams by instructing their referees
to refrain from calling fouls on large market players or call “phantom” fouls on
small market players.
I use the same method to classify teams and markets as I did in the second
and third models to see if one particular market size team has a significant
advantage over a different market size team. Before I test to see if large market
teams have a competitive advantage over small market teams I will see if there is
any competitive difference between the two markets:§§§§
H0: β6-β5=0
H1: Not H0
I expect that neither large market teams nor small market teams have a
competitive advantage or disadvantage over medium market teams. I also
expect that large market teams do not have an advantage or disadvantage over
small market teams.
Performance Statistics:
§§§§
There is no reason to test if large market teams have a competitive advantage or disadvantage over
small market teams if there is no significant difference.
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Several team performance measures will be included in this model, which
may influence how successful a team is over a season. Variables included in this
model are: offensive field goal percentage, defensive field goal percentage, points
per game, rebounds per game, assists per game, steals per game, blocks per
game, and turnovers per game. I expect every measure except turnovers per
game and defensive field goal percentage to positively impact a team’s record
over a season.
Season:
I assign annual dummy variables to control for the effects of time on the
data. To avoid the dummy variable trap I do not include the 1998-1999 Season,
which is my reference season in the model.
Determinants of Winning Results: (Payroll in Dollars)
Dependant Variable: Win Percentage
Variable Names
Constant
Estimated Coefficient
Standard Error
T-Ratio df =180
0.9047*
0.4661
1.941
1.191E-09*
6.234E-10
1.91
Home Attendance %
0.3134***
0.0629
4.982
Contract Year
0.0063*
0.0038
1.659
Small Market Team
0.0064
0.0165
0.3877
Large Market Team
-0.0204
0.0169
-1.209
Offensive Field Goal % (OFG%)
1.6459**
0.8143
2.021
Defensive Field Goal % (DFG%)
-5.0167***
0.5463
-9.183
Three Point % (3PT%)
-0.1066
0.4133
-0.2578
Free Throw % (FT%)
0.2432**
0.0978
2.488
Points Per Game (PPG)
0.0039
0.0029
1.332
Team Payroll
*****
There is no reason to test if large market teams have a competitive advantage or disadvantage over
small market teams if there is no significant difference.
97
Offensive Rebounds Per Game (ORPG)
-0.0018
0.0075
-0.2473
Defensive Rebounds Per Game (DRPG)
0.0124*
0.0070
1.774
Assists Per Game (APG)
-0.0027
0.0047
-0.5777
Blocks Per Game (BPG)
0.0059
0.0087
0.682
Steals Per Game (SPG)
0.0150*
0.0089
1.697
Turnovers Per Game (TPG)
-0.0125**
0.0050
-2.481
This Year is 1999
-0.0131
0.0306
-0.4287
This Year is 2000
-0.0378
0.0314
-1.201
This Year is 2001
-0.0383
0.0316
-1.214
This Year is 2002
-0.0538*
0.0323
-1.666
This Year is 2003
-0.0583*
0.0317
-1.841
This Year is 2004
-0.0492
0.0330
-1.493
.6716 (.6315)
0.0897
R-Square (Adj)/Standard Error
F-Statistic
16.733
*Significance at 10%
**Significance at 5%
***Significance at 1%
Only a few statistics explain how a team’s performance can affect their
chances of winning. Offensive field goal percentage has a positive affect on a
team’s winning percentage while defensive field goal percentage has a very
negative affect on a team’s winning percentage. The sheer magnitude of the
defense field goal percentage coefficient suggests that good teams usually play
defense and can rely on defense more than offensive. Each hundredth of a
percentage point less in defense field goal percentage results, on average, in a
team winning nearly four more gains per season! Defensive rebounding and
steals per game are also important variables that affect a teams winning
percentage. This is plausible because those two statistics relate to a team’s ability
to play defense. Finally, every additional turnover a team averages per game
negatively affects their winning percentage.
These results show that there are some factors that affect a team’s winning
percentage outside of on the court performance. The percentage of fans who
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attend home games can affect a team’s winning percentage. A team that draws
1% (.01) more fans increases their winning percentage, on average, by .003134,
which is approximately a fourth of a game. This means a team that sells out
every home game would yield 10 wins per season (.003134*41) ceteris paribus.
There is modest evidence that payroll is a marginal factor influencing a team’s
winning percentage. Every one million dollars spent on payroll increases a
team’s winning percentage by approximately .0012 (1.191E-09x1,000,000), which
is slightly less than tenth of a game.
The signs of the significant coefficients were no surprise, but the
insignificance of one was surprising. The number of players a team has playing
in their contract year does not significantly affect their winning percentage.
Large Market vs. Small Market Results
T-Statistic df=180
P-Value
1.607
.1097
The test was run simultaneously with the win model in Shazam. The test
results fail to reject the null hypothesis at 10% level. Large market teams do not
have a competitive advantage or disadvantage over small market teams. In
addition the win model results show that medium market teams do not have a
competitive advantage or disadvantage over large and small markets.
Model 2: Payroll and Revenue:
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Total Revenue = β1 + β2 Payroll + β3 Win % + β4 Home Attendance % + β5 Small
Market + β6 Large Market + β7 Number of Major Sports Teams + β8 Season + e
Where payroll and total revenue are in millions of dollars
Dependant Variable: Total Revenue
Total revenue aggregates all revenues that a team receives from national
and local sources. A team uses its revenues to pay off its operating expenses and
whatever is leftover is an operating profit. Revenue data from the 1998-1999 to
2003-2004 seasons are from Fort while data for the 2004-2005 Season is from
Forbes’s annual study of NBA team valuations.
Independent Variables:
Payroll:
Although each dollar spent on payroll is an operating expense, it may also
be an investment in a team’s overall success. Each dollar spent for payroll goes
towards compensating talent that may help a team win and perhaps draw in
additional revenue to pay off other operating expenses. The Orlando Magic
drastically increased their payroll during the 2001 off-season. The Magic signed
rising star Tracy McGrady and sign-and-traded for superstar Grant Hill, who
was one of the most popular players in the league. The acquisition of McGrady
and Hill made the Magic a formidable team and also made them appealing to
100
fans. However, an early season-ending injury to Hill dashed Orlando’s
championship aspirations, yet team revenue increased by 8%.
Team
Orlando
Δ Payroll
$11,827,641
Δ Revenue
8%
2000-2001 Record
41-41
2001-2002 Record
43-39
I expect that a team owner receives a positive return on every dollar invested in
their team.
Win %:
Successful teams are easier to market to fans than unsuccessful teams.
Good teams can market their players while bad teams have to be creative in their
marketing tactics. A bad team typically has to promote the other team’s star
player or offer discounts to fans to get them to attend games or buy merchandise.
The 2002-2003 Orlando Magic were a competitive, small market team that ranked
21st in total revenue. The next season the Magic were the worst team in
basketball, and they dropped to 24th in total revenue. Despite the lower cost of
going a game for Magic fans, which was measured by teammarketing.com’s
NBA Fans Cost Index, attendance dropped. To make matters worse, total
revenue dropped by approximately $2,000,000 from the season before.
Team
Total Revenue
Revenue Rank
Overall Record
Attendance %
Orlando 02-03
$80,000,000
21st
42-40 (.512)
85.7%
Orlando 03-04
$78,000,000
24th
21-61 (.259)
83.3%
I expect that a team’s overall winning percentage to have a positive impact on
total revenue.
101
Home Attendance %
A sizable portion of a NBA team’s revenue comes from ticket sales, the
sale of concessions, and merchandise sales. The more enthusiastic a fan base is
the likelier they are to go to games and buy team merchandise. A good example
to illustrate the importance of an enthusiastic fan base to team revenue is to
compare the fan bases of Los Angeles Clippers and the Los Angeles Lakers. In
addition to being in the same market, the Lakers and Clippers also play in the
same arena, the Staples Center. The major distinction between the Lakers and
Clippers is their popularity. The people of Los Angeles are far more enthusiastic
about the Lakers than they are about the Clippers. In the 2004-2005 Season the
Clippers were better than the Lakers, yet were dwarfed by them in terms of
revenue. The Lakers earned a lot more revenue than the Clippers because they
had more fans attend their games and buy team merchandise.
Teams
Overall Record
Total Revenue
Home Attendance %
LA Clippers
37-45
$83,000,000
.891
LA Lakers
34-48
$156,000,000
.986
Like in the win model, I will use the percentage of total home attendance
during a season as a proxy for team fan enthusiasm for that season. I expect
there to be a strong, positive relationship between the level of team enthusiasm
and a team’s total revenue.
Market Size:
102
Large market teams have potentially larger fan bases than small market
teams since they have more people in their local market who they can appeal to.
In addition, large market teams may have larger fan followings across the
country than small market teams. Two teams that have a lot of people who they
can appeal to are the New York Knicks and the Los Angeles Lakers. Despite the
New York Knicks’s poor performance in recent years the team led the league in
total revenue in the 2004-2005 Season. The second ranked team in total revenue
during that season was the Los Angeles Lakers, who faltered after trading away
their superstar center Shaquille O’Neal the past off-season.
Teams
Overall Record
Total Revenue
NBA mkt. rank
New York
33-49
$181,000,000
1
LA Lakers
34-48
$156,000,000
2
In stark contrast, the 2005 NBA Champion San Antonio Spurs played in one of
the smallest markets in the NBA. Despite the team’s success their revenue
dwarfs the revenue generated by the Lakers and Knicks.
Teams
Overall Record
Total Revenue
NBA mkt. rank
San Antonio
59-23
$121,000,000
21
I use the same method to classify teams and markets as I did in the salary and in
the win models. I expect that teams that play in larger markets have an
advantage in generating revenue and teams that play in smaller markets have a
disadvantage compared to those who play in medium markets.
Number of Major Sports Teams in Market:
103
The number of major sports teams in a particular market may affect the
amount of revenue a team generates. The more major sports teams in a
particular market the more options sports fans’ have to not spend their
disposable income on their local NBA team(s). Sacramento and Indianapolis are
both small markets with NBA teams that had similar records in the 2004-2005
Season. The major difference between the two markets is that Indianapolis has a
second major, professional sports team, the NFL’s Indianapolis Colts. As a
result, sports fans in Indianapolis have the option to distribute their disposable
income between the Colts and Pacers while sports fans in Sacramento only have
the Kings. This may explain why Sacramento generated $11 million more than
Indiana in revenue.
Team
# of Teams in Mkt
Record
Total Revenue
Mkt. Size/Type
Sacramento
1
50-32
$119,000,000
1,796,857 (Small)
Indianapolis
2
44-38
$108,000,000
1,525,104 (Small)
Sports teams that I consider major sports teams are teams from Major League
Baseball, the NFL, the National Hockey League, or the NBA.
Season
I assign annual dummy variables to control for the effects of time on the
data. To avoid the dummy variable trap I do not include the 1998-1999 Season,
which is my reference season in the model.
Determinants of Annual Revenue Results (Revenue in Millions):
104
Dependant Variable: Natural Log Total Revenue
Variable Name
Constant
Estimated Coefficient
Standard Error
T-Ratio df=190
2.7750***
0.1014
27.37
0.3290***
0.0951
3.459
5.537E-09***
1.1360E-09
4.873
Home Attendance %
0.6938***
0.1251
5.545
Small Market Team
-0.1204***
0.0306
-3.93
Large Market Team
0.1528***
0.0302
5.065
Number of Teams in Market
0.0004***
0.0001
4.229
This Year is 1999
0.5517***
0.0516
10.7
This Year is 2000
0.5874***
0.0555
10.58
This Year is 2001
0.6447***
0.0561
11.48
This Year is 2002
0.6397***
0.0586
10.91
This Year is 2003
0.7181***
0.0582
12.34
0.7606***
0.0578
13.15
.8152 (.8035)
0.17172
Win %
Team Payroll
This Year is 2004
R-Square (Adj)/Standard Error
F-Statistic
* Significance at 10%
69.843
** Significance at 5%
*** Significance at 1%
On the court success has a major impact on team revenue. One win will
increase a team’s revenues, on average, by approximately 0.4% {(1/82)*.3290}.
The general increases in the annual variables indicate that team revenues are
increasing, on average, around the league. Although the payroll variable is
statistically significant, its effect on team revenue is negligible. With a rate of
return so small, 5.537 x 10-9 %, team owners should not expect to make a lot of
money because they simply spend a lot of money.
Lessons from the Two Team Models:
The way a team plays can affect their ability to win over a course of a
season and the amount of revenue they generate. A team can win more if they
rely on playing good defense more than they rely on playing good offense. A
105
team that takes care of the ball each game-keep turnovers per game low-also will
win more often. The better a team rebounds on the defensive end the more they
will win over the course of the season. The more a team wins the more revenue
they will generate on average.
A team’s ability to generate revenue is influenced by many factors not
related to its performance on the court. For instance, the percentage of fans that
attend their team’s home games (high fan enthusiasm) is a very important driver
of their winning percentage and revenue. Market size does not matter in terms
of winning games (there is completive balance in the NBA), but does make a
difference in generating revenue. Medium market teams generate more
revenues, on average, than small market teams, but less, on average, than large
market teams. Finally, changing payroll does little to directly change a team’s
winning percentage or draw in revenue.
Since team owners receive negligible, direct returns from spending money
on players why do they bother to spend money for their team beyond what they
are required to spend by the CBA? The answer may be that though teams gain
little directly from spending money on players they benefit indirectly. Like with
any typical business, NBA teams that have talented personnel will likely be
successful. However, it costs a lot of money to retain and acquire talented
personnel. Business owners are compelled to the spend money it takes to
acquire and retain talented personnel because they are likelier to make the
business succeed. Do NBA team owners have a similar financial incentive to
106
spend money on players who have the potential to improve their chances of
winning?
The results of the revenue model imply that team owners do have a
financial incentive to win since on the court success is the strongest, positive
determinant of team revenue. This result leads us to ask why winning positively
affects team revenue. The most plausible explanation of why winning increases
team revenue is that winning increases fan enthusiasm. The more enthusiastic a
team’s fan base is the easier it is for the team to persuade their fans to attend
their home games and buy their merchandise. I run a new model to see if on the
court success has any affect on fan enthusiasm. The model is as follows:
Home Attendance % = β1+ β2 Team Winning % + β3 Season + e
This simple test of significance model measures fan enthusiasm with
home attendance percentage its dependent variable and a team’s on the court
success with team winning percentage as its sole independent variable. I also
include annual dummy variables to account for possible yearly differences in the
data. Though this is a liner model, due to the use of percentages for the
dependent and independent variables the interpretation of these coefficients will
synonymous with the interpretation of log-linear coefficients.
Variable Name
Constant
Win %
Estimated Coefficient
0.7009***
0.3359***
Standard Error
0.0299
0.0471
T-Ratio df=196
23.4200
7.1300
107
This Year is 1999
This Year is 2000
This Year is 2001
This Year is 2002
This Year is 2003
This Year is 2004
R-Square (Adj)
Standard Error
F-Statistic
* Significance at 10% level
-0.0116
-0.0065
0.0038
0.0099
0.0100
0.0235
0.2138 (.1857)
0.0994
7.6140
** Significance at 5% level
0.0261
0.0261
0.0261
0.0261
0.0261
0.0259
-0.4433
-0.2505
0.1453
0.3804
0.3839
0.9093
*** Significance at 1% level
These results show that a team’s on the court success is a significant factor
in influencing fan enthusiasm. An additional win, on average, increases home
attendance by 0.33% over the course of a season. This 0.33% increase in home
attendance during the season will in turn increase team revenues. Along with
the increase of team revenues from gate receipts we should also expect a surge in
team merchandise sales. These results in conjunction with the results of the win
model imply there is a positive feedback [loop] between on the court success and
fan enthusiasm. The more on the court success a team has the more enthusiastic
fans will be and the more fan enthusiasm there is the greater a team’s home court
advantage will be (and vice versa). Therefore, a team owner who wants to
increase their revenues and team winning percentage should pay for players
who will improve their chances to win, induce fans to attend home games, and
buy team merchandise.
In reality, most team owners are willing to spend money so they can get
players who will help their team win. A team’s acquisition of players who help
them win often does increase their fan’s enthusiasm along with their revenues.
For example, early in the 2004 off-season Lakers’ center Shaquille O’Neal became
108
available on the trade market after he demanded to be traded. If a team wanted
to trade for O’Neal they would need to make a serious financial commitment.
Not only would a team have to pay O’Neal’s $27,696,430 salary in the 2004-2005
Season, they would also have to commit to giving him a long-term contractextension. Since O’Neal’s salary was so large the Lakers had to take at least three
players back in a trade to comply with the NBA’s trading rules for teams over the
salary cap. A team that had to trade this many players to the Lakers would also
have to spend money in the free agency to replace the players they traded to get
O’Neal. Many teams were willing to accept the financial responsibilities that
came with acquiring O’Neal since he was the most dominant player in the league
at the time. One team that had a serious interest in acquiring O’Neal was the
Miami Heat. The Heat was an up and coming team that won 42 games during
the 2003-2004 Season. Miami saw acquiring O’Neal as a quick way to transform
themselves into a championship contender and a way to excite their fan base.
The Heat won the “Shaq Sweepstakes” and traded three players and a future
first round draft pick to the Lakers for O’Neal. The Heat were not finished
retooling their roster since they later signed several free agents to complement
O’Neal and fill in roster spots that were vacated from the big trade.
The success the Heat experienced in the 2004-2005 Season was well worth
the nearly $14 million more spent in payroll. The revamped roster energized the
Miami fan base that the American Airlines’ Arena (where Miami plays) was
filled overcapacity for every Heat home game. This huge surge in attendance
109
helped fuel the 28% rise in team revenues and the 17 games improvement over
the past season.
Season
Payroll
Record
Home Attendance %
Revenue
2003-2004
$45,529,862
42-40 (.512)
76.6%
$93,000,000
2004-2005
$59,495,338
59-23 (.720)
101.4%
$119,000,000
Why do all teams not try to “buy a championship” if it helps them
increase revenues? There are two reasons that explain why teams do not always
spend money on players who may help them win. The first reason is that
spending money on players does not always work. All of the teams in the league
are engaged in a zero sum game, so when one team succeeds another team fails.
Players who can alter a team’s fortune, like Steve Nash, are very hard to come by
and are the top wage earners in NBA’s winner-take-all-market. The second
reason teams do not always spend money on players who may help them win is
that teams face the luxury tax. The financial penalties from the luxury tax may
more than offset any gains in revenue that teams derive from the marginal
success they experience from adding a player to their team.
Key Changes in the 2005 CBA: A Preliminary Assessment
Age Limit:
After great controversy the league and NBPA agreed to an age limit,
which would prevent a high school player after their senior year to immediately
enter the NBA Draft. For every LeBron James or Amare Stoudamire whom
became superstars there was also a Leon Smith, Korleone Young or Kwame
110
Brown whom failed. American players now must be one year removed from
high school, and like international players, be at least 19 years of age to be
eligible for the NBA Draft.
Rookie Contract Length Altered:
The length that a rookie contract can be guaranteed for has been reduced
from three years down to two years. However, the maximum length of a rookie
contract remains at four years since teams now have the option to pick up the
third and, if they want, the fourth year of a player’s contract. If neither option is
picked up the player will automatically become an unrestricted free agent. If
both options are picked up then after the fourth year the player can become a
restricted free agent if they are given a qualifying offer.
New Roster Limits:
Teams are required to carry 13 players, 12 on the active roster and 1 player
on the inactive list. Players on the inactive list can consist of injured and non
injured players and can come off the list after serving at least a game.
New BRI:
The share of basketball related income that the league uses to calculate the
salary cap increases under the new CBA. The percentage share of BRI the league
used in the first year of the new CBA increased by nearly 1.5%. As a result, the
111
salary cap jumped from $43,870,000 in the 2004-2005 Season to $49,500,000 in the
2005-2006 Season. The table below shows that the 2005-2006 Season may be the
beginning of substantially larger salary caps in the future since the BRI share
used to calculate the salary cap will rise to 51% the following season.
Season
2005-2006
2006-2007
2007-2008
2008-2009
2010-2011
Projected BRI to Determine Salary Cap
49.5%
51%
51%
51%
51%
Reduction in Maximum Salary Increases & Years:
The longest contract-length that a team can offer when it attempts to resign their own player has been reduced from seven years to six years. In
addition, the longest contract-length a team can offer when it attempts to sign a
free agent from another team has been reduced from six years to five years.
These changes were made to shorten the commitments owners have to players
who may underachieve and/or sustain serious injuries after they sign their
contracts.
The maximum pay raises teams can give to players in new contracts has
also been reduced. For Bird and Early Bird contracts pay raises have been
reduced to 10.5% per season from 12.5%. For all other contracts pay raises have
been reduced to 8% from 10%.
Significant Changes to Restricted Free Agency Rules:
112
The original team now has the opportunity to extend an eligible player a
maximum qualifying offer. This special type of qualifying offer is essentially a
maximum contract offer with a maximum contract length of 6 years and 10.5%
pay raises per season (Coon, 2005). It can only be applied to players coming off
the fourth year of their rookie contract who are eligible for the Bird exception
(Coon, 2005). This allows a team to demonstrate to their young star before any
other offers can be made by other teams that they are committed to pay him the
money he wants. A maximum qualifying offer also forces other teams to
increase the minimum length of their offer from two years to three years.
The new CBA makes it far easier for teams to keep their restricted free
agents than the last CBA. In the last CBA teams could lure a restricted free agent
away from their original team by offering more money than what the original
team could offer. The new CBA places restrictions on other teams, so they can no
longer offer more than what the original team can. Other teams can now only
offer a first or second year restricted free agent up to the league average salary in
the first year (Coon, 2005). This allows the original team, even if they are over
the salary cap, the opportunity to utilize their midlevel exception, which salary is
the same as the league average salary, to re-sign the player††††† (Coon, 2005).
The new CBA further constrains a team from signing away another team’s
restricted free agent. A team can only offer a restricted free agent player an 8%
†††††
The original team may also use an Early Bird exception to match the offer if the player has been on the
team for the past two consecutive seasons (See Coon)
113
pay raise in the 2nd year and 6.9% pay raise each season after the 3rd season. In
the 3rd season a team may give the player more than a 8% pay raise, but if this
happens the team will subject to another restriction. A team that offers a player
anything beyond an 8% pay raise in the third year must be able to fit the
contract’s average salary into their salary cap for each year in the contract (Coon,
2005). This restriction demands a team at the time they offer the contract to have
the salary cap space in advance to cover the average salary of the contract during
each year of the contract (Coon, 2005). For example a team that offers a restricted
free agent a 5 year $38 million dollar contract must already have available at least
$7.6 million in salary cap space not only in the upcoming season, but also in the
next four seasons. A team that gives a player an 8% or less pay raise in the 3rd
season is not subject to this restriction.
It is still possible for a team to lose their restricted free agent. A team can
still offer a restricted free agent a contract that the original team is not willing to
match. The original team can still lose a free agent if they do not have the
necessary salary cap exceptions to re-sign their player. The time the original
team has to decide whether they want to match another team’s offer has been
reduced from 15 days to 7 days (Coon, 2005). This decreases the amount of time
other teams have their salary cap space tied up for, which may encourage more
teams to give restricted free agents offers.
New Luxury Tax Rules:
114
Team owners have gained a lot of certainty in regards to the luxury tax.
First the tax will be implemented every season regardless of the share the BRI the
players receive. Second the luxury tax threshold will be set by the league at 61%
of the league’s projected BRI for the upcoming season (Coon, 2005). Anyone
above this threshold will be taxed dollar for dollar by the amount they exceed the
threshold. Most importantly, the league will announce to the teams the luxury
tax threshold before the season starts and tax them based on their payroll level at
the end day of the season (Coon, 2005). This gives teams the opportunity to trim
payroll during the season so they can avoid the tax (Coon, 2005). Teams that stay
underneath the tax threshold will be rewarded by being given a 1/30 share of the
luxury tax money paid by teams over the threshold (Coon, 2005).
One Time Amnesty Clause:
As soon as the new CBA went into effect teams were given until August
15, 2005 to waive one player and avoid paying the luxury tax on that player.
Teams that were below the luxury tax threshold had no incentive to waive a
player because they saved no money by waiving a player. Teams that waive a
player are still responsible for the guaranteed portion of a player’s contract.
Meanwhile, teams above the luxury tax threshold had an incentive to waive a
player because they could potentially save millions of luxury tax dollars. The
one caveat to this one time opportunity was that a team that waived a player
could not re-sign them.
115
Some teams took advantage of the one time opportunity to potentially
save luxury tax dollars. For example, the Dallas Mavericks and Los Angeles
Lakers released maximum contract players, Michael Finley and Brian Grant
respectively. The players that were waived were released so late in the offseason that they had to accept much lower salaries from new teams than they
received from their old team.
2005-2006 Results:
I ran the sports agent model and highly regarded representative models
for the 2005-2006 Season. Like with the previous models, I omitted first year
players and players who did not play in the past season. The players involved
were on NBA rosters as of November 1, 2005, which was the first day of the
season. The 2005-2006 player salary data is derived from Kramer.
Model 1: Sports Agents vs. Other Forms of Representation Results:
Dependent Variable: Natural Log Salary
Variable Name
Constant
Games Played (GP)
Points Per Game (PPG)
Rebounds Per Game (RPG)
Assists Per Game (APG)
Blocks Per Game (BPG)
Steals Per Game (SPG)
Team Win %
Championship
All-Star
Age
Age Squared
Estimated Coefficient
-8.2714***
0.0025
0.0543***
0.0461**
0.0525*
0.1246
0.0832
0.2438
0.1048
-0.0534**
0.3761***
-0.0075***
Standard Error
2.0750
0.0017
0.0091
0.0229
0.0296
0.0796
0.1072
0.2049
0.0969
0.0250
0.0960
0.0017
T-Ratio df=332
-3.9860
1.4160
5.9840
2.0130
1.7750
1.5660
0.7760
1.1900
1.0820
-2.1370
3.9170
-4.3350
116
Experience
Height
Race
Foreign
Forward
Center
1st Round Before 1995 CBA
1st Round After 1995 CBA
Contract-Extension
Resign Restricted Free Agent
Sign Restricted Free Agent
Sign Unrestricted Free Agent
Contract Year
Agents
Small Market Team
Large Market Team
R-Square (Adj)/Standard Error
F-Statistic
* Significance at 10%
0.1336***
0.0419**
-0.0840
-0.0389
-0.0870
-0.2076
N/A
-0.0746
0.2651**
0.2087*
0.1897
-0.1207
-0.2539***
-0.2900*
0.0364
0.0792
.7148 (.6925)
32.01
** Significance at 5%
0.0228
0.0166
0.0816
0.1105
0.0932
0.1388
N/A
0.1217
0.1172
0.1118
0.1156
0.0877
0.0715
0.1650
0.0726
0.0699
0.52894
5.8490
2.5310
-1.0300
-0.3520
-0.9335
-1.4960
N/A
-0.6124
2.2620
1.8670
1.6410
-1.3770
-3.5500
-1.7570
0.5011
1.1330
*** Significance at 1%
The major development in the 2005-2006 Season is that the agent
coefficient has become significantly negative. Players represented by sports
agents made 29% less, on average, than players not represented by sports agents.
This development may be the result of the amnesty clause because players who
were overpaid before were no longer overpaid and in some cases are now
underpaid.
Model 2: Highly Regarded vs. Ordinary Representatives Results:
Variable Name
Constant
Games Played (GP)
Points Per Game (PPG)
Rebounds Per Game (RPG)
Assists Per Game (APG)
Blocks Per Game (BPG)
Steals Per Game (SPG)
Team Win %
Championship
All-Star
Estimated Coefficient
-8.1240***
0.0021
0.0536***
0.0509**
0.0476
0.0979
0.1244
0.2808
0.0899
-0.0501**
Standard Error
2.0780
0.0018
0.0092
0.0230
0.0303
0.0805
0.1090
0.2063
0.0981
0.0252
T-Ratio df=325
-3.9090
1.1820
5.8270
2.2160
1.5730
1.2170
1.1410
1.3610
0.9165
-1.9910
117
Age
Age Squared
Experience
Height
Race
Foreign
Forward
Center
1st Round Before 1995 CBA
1st Round After 1995 CBA
Contract-Extension
Resign Restricted Free Agent
Sign Restricted Free Agent
Sign Unrestricted Free Agent
Contract Year
Mark Bartelstein
Aaron Goodwin
Bill Duffy
Arn Tellem
David Falk
Marc Fleisher
Lon Babby
Dan Fegan
Small Market Team
Large Market Team
R-Square (Adj)/Standard Error
F-Statistic
* Significance at 10%
0.3550***
-0.0071***
0.1335***
0.0396**
-0.0666
-0.0771
-0.0883
-0.1938
N/A
-0.0671
0.2465**
0.2086*
0.1932*
-0.1074
-0.2458***
0.0454
-0.2937
0.1507
0.1228
0.2928
0.3019*
0.3357**
-0.0114
0.0328
0.0585
.7225 (.6944)
25.648
**Significant at 5%
0.0973
0.0017
0.0233
0.0166
0.0819
0.1185
0.0938
0.1390
N/A
0.1219
0.1182
0.1121
0.1164
0.0893
0.0719
0.1075
0.2318
0.1332
0.1127
0.1819
0.1829
0.1667
0.1173
0.0734
0.0709
0.52733
3.6500
-4.0900
5.7410
2.3830
-0.8126
-0.6511
-0.9418
-1.3940
N/A
-0.5507
2.0840
1.8610
1.6590
-1.2040
-3.4200
0.4222
-1.2670
1.1310
1.0890
1.6100
1.6510
2.0140
-0.9676
0.4472
0.8258
*** Significant at 1%
The Babby coefficient has turned statistically significant; clients of Lon
Babby made 33.57% more, on average, than players represented by lesser
regarded representatives.
Has 2005-2006 Changed the Underlining Relationships?
I will rerun the agent model and the highly regarded representative model
with the inclusion of the 2005-2006 data to see if the additional data changes the
signs and significance of the coefficients from these two models.
118
Model 1: Sports Agents vs. Other Forms of Representation Results:
Dependent Variable: Natural Log Salary
Variable Name
Constant
Games Played (GP)
Points Per Game (PPG)
Rebounds Per Game (RPG)
Assists Per Game (APG)
Blocks Per Game (BPG)
Steals Per Game (SPG)
Team Win %
Championship
All-Star
Age
Age Squared
Experience
Height
Race
Foreign
Forward
Center
1st Round Before 1995 CBA
1st Round After 1995 CBA
Contract-Extension
Resign Restricted Free Agent
Sign Restricted Free Agent
Sign Unrestricted Free Agent
Contract Year
Agents
Small Market Team
Large Market Team
This Year is 1996
This Year is 1997
This Year is 1998
This Year is 1999
This Year is 2000
This Year is 2001
This Year is 2002
This Year is 2003
This Year is 2004
This Year is 2005
R-Square (Adj)/Standard Error
F-Statistic
* Significance at 10% Level
Estimated Coefficient
-7.9823***
0.0020***
0.0522***
0.0527***
0.0750***
0.2209***
0.0073
0.3495***
0.0125
-0.0068
0.4008***
-0.0077***
0.1197***
0.0167***
0.0251
0.0115
0.012
0.0848*
0.1673***
-0.1541***
0.1441***
0.1361***
0.0983
-0.3300***
-0.3214***
0.0442
0.1415*
-0.0003**
0.0828
0.2200***
-0.0058
0.5107***
0.7212***
0.7694***
0.7854***
0.8199***
0.8502***
0.8970***
.6533 (.6498)
190.945
**Significance at 5% Level
Standard Error
0.6664
0.0006
0.0033
0.0079
0.01
0.0286
0.0369
0.0708
0.0364
0.0087
0.0356
0.0006
0.0085
0.0048
0.0299
0.0545
0.0344
0.0503
0.0464
0.0418
0.0412
0.0488
0.063
0.0278
0.0263
0.0466
0.0812
0.0002
0.0514
0.0515
0.0526
0.0526
0.0515
0.0522
0.0523
0.0514
0.0522
0.0522
0.64479
*** Significance at 1% level
Model 2: Highly Regarded vs. Ordinary Representatives Results:
T-Ratio df=3750
-11.98
3.217
15.63
6.671
7.534
7.715
0.1966
4.94
0.3425
-0.7738
11.27
-12.23
14.1
3.494
0.838
0.2109
0.3484
1.686
3.608
-3.687
3.497
2.79
1.562
-11.86
-12.21
0.9468
1.743
-2.129
1.611
4.273
-0.1099
9.709
14.01
14.73
15.02
15.94
16.28
17.19
119
Dependent Variable: Natural Log Salary
Variable Name
Constant
Games Played (GP)
Points Per Game (PPG)
Rebounds Per Game (RPG)
Assists Per Game (APG)
Blocks Per Game (BPG)
Steals Per Game (SPG)
Team Win %
Championship
All-Star
Age
Age Squared
Experience
Height
Race
Foreign
Forward
Center
1st Round Before 1995 CBA
1st Round After 1995 CBA
Contract-Extension
Resign Restricted Free Agent
Sign Restricted Free Agent
Sign Unrestricted Free Agent
Contract Year
Mark Bartelstein
Aaron Goodwin
Bill Duffy
Arn Tellem
David Falk
Marc Fleisher
Lon Babby
Dan Fegan
Small Market
Large Market
This Year is 1996
This Year is 1997
This Year is 1998
This Year is 1999
This Year is 2000
This Year is 2001
This Year is 2002
This Year is 2003
This Year is 2004
This Year is 2005
Estimated Coefficient
-1.1976*
0.0017***
0.0510***
0.0539***
0.0724***
0.2155***
0.0159
0.3572***
0.0067
-0.0061
0.4068***
-0.0077***
0.1152***
0.0160***
0.0433
0.0499
0.0141
0.0784
0.2113***
-0.1346***
0.1489***
0.1472***
0.1054*
-0.3240***
-0.3165***
0.0890*
0.1857***
0.1101*
0.1728***
0.1987***
0.0647
0.1137
0.1377**
0.0217
-0.0003**
0.0826
0.2179***
-0.0132
0.4977***
0.7122***
0.7549***
0.7660***
0.8379***
0.8360***
0.8896***
Standard Error
0.6659
0.0006
0.0034
0.0079
0.0100
0.0286
0.0369
0.0710
0.0366
0.0087
0.0355
0.0006
0.0085
0.0048
0.0299
0.0577
0.0347
0.0508
0.0537
0.0433
0.0413
0.0487
0.0627
0.0279
0.0263
0.0469
0.0818
0.0638
0.0393
0.0426
0.0983
0.0721
0.0580
0.0227
0.0001
0.0511
0.0513
0.0524
0.0526
0.0515
0.0522
0.0524
0.0526
0.0524
0.0523
T-Ratio df=3743
-1.799
2.714
15.18
6.833
7.227
7.532
0.432
5.033
0.1842
-0.6982
11.46
-12.27
13.5
3.337
1.446
0.8643
0.4078
1.544
3.931
-3.111
3.608
3.021
1.68
-11.62
-12.04
1.898
2.271
1.725
4.401
4.668
0.658
1.576
2.372
0.9548
-2.053
1.616
4.246
-0.2523
9.466
13.84
14.45
14.61
15.94
15.96
17
120
R-Square(Adj)/Standard Error
F-Statistic
* Significance at 10% Level
.6577 (.6537)
163.418
**Significance at 5% Level
0.6413
*** Significance at 1% level
The additional data caused two changes in the estimated coefficients. The
first change is that the Race variable became statistically insignificant, so black
players do not earn more in salary, on average, than white players. The second
change is that the Duffy variable is now statistically significant, so clients of Bill
Duffy make 11% more, on average, than players represented by lesser regarded
representatives.
Compare the CBA’s:
Each collective bargaining agreement can alter the salary environment in
which teams and representatives operate. New rules and restrictions can
increase or reduce the number of ways teams can sign a player and affect how
much a representative can demand for their client during contract negotiations. I
will rerun the agent and highly regarded representative models to see if there are
any significant differences in the explanatory variables between 1995, 1999, and
2005 CBA’s.
Model 1: Sports Agents vs. Other Forms of Representation Results:
Variables/Season
Constant
Games Played (GP)
Points Per Game (PPG)
1995 CBA
-7.4549***
(1.441)
0.0024**
(0.0012)
0.0539***
1999 CBA
-8.8468***
(0.8196)
0.0016*
(0.0008)
0.0484***
2005 CBA
-8.2714***
(2.075)
0.0025
(0.0017)
0.0543***
121
Rebounds Per Game (RPG)
Assists Per Game (APG)
Blocks Per Game (BPG)
Steals Per Game (SPG)
Team Win %
Championship
All-Star
Age
Age Squared
Experience
Height
Race
Foreign
Forward
Center
1st Round Before 1995 CBA
1st Round After 1995 CBA
Contract-Extension
Resign Restricted Free Agent
Sign Restricted Free Agent
Sign Unrestricted Free Agent
Contract Year
Agents
Small Market
(0.0062)
0.0477***
(0.0139)
0.0751***
(0.0183)
0.1930***
(0.0545)
-0.0155
(0.0707)
0.3740***
(0.1269)
0.0028
(0.0688)
0.0124
(0.0167)
0.3526***
(0.0749)
-0.0072***
(0.0013)
0.1172***
(0.0181)
0.0207*
(0.0108)
0.1343**
(0.0584)
0.3555**
(0.1609)
0.0061
(0.0690)
0.0823
(0.1068)
0.2020***
(0.0753)
-0.0292
(0.1045)
-0.1155
(0.0844)
0.0539
(0.0888)
0.1150
(0.1895)
-0.3344***
(0.0552)
-0.3392***
(0.0554)
0.0828
(0.0514)
0.1055**
(0.0044)
0.0599***
(0.0105)
0.0774***
(0.0128)
0.2281***
(0.0369)
0.0154
(0.0473)
0.3383***
(0.0950)
0.0153
(0.0476)
-0.0009
(0.0111)
0.4762***
(0.0449)
-0.0090***
(0.0008)
0.1329***
(0.0109)
0.0131**
(0.0057)
-0.0008
(0.0381)
-0.0278
(0.0683)
0.0068
(0.0437)
0.1146*
(0.0626)
-0.1112
(0.1102)
-0.0652
(0.0527)
0.2634***
(0.0527)
0.2106***
(0.0717)
0.1244
(0.0823)
-0.3440***
(0.0350)
-0.3205***
(0.0341)
0.0330
(0.0338)
0.0171
(0.0091)
0.0461**
(0.0229)
0.0525*
(0.0296)
0.1246
(0.0796)
0.0832
(0.1072)
0.2438
(0.2049)
0.1048
(0.0969)
-0.0534**
(0.0250)
0.3761***
(0.0960)
-0.0075***
(0.0017)
0.1336***
(0.0228)
0.0419**
(0.0166)
-0.0840
(0.0816)
-0.0389
(0.1105)
-0.0870
(0.0932)
-0.2076
(0.1388)
N/A
(N/A)
-0.0746
(0.1217)
0.2651**
(0.1172)
0.2087*
(0.1118)
0.1897
(0.1156)
-0.1207
(0.0877)
-0.2539***
(0.0715)
-0.2900*
(0.1650)
0.0364
122
Large Market
This Year is 1996
This Year is 1997
This Year is 1998
This Year is 1999
This Year is 2000
This Year is 2001
This Year is 2002
This Year is 2003
This Year is 2004
R-Square (Adj)
Standard Error
F-Statistics
* Significance at 10%
(0.0499)
-0.0004**
(0.0002)
0.0622
(0.0505)
0.1948***
(0.0519)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
.6429 (.6320)
0.62262
58.803
**Significance at 5%
(0.0329)
-0.0003
(0.0003)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
0.4986***
(0.0537)
0.7194***
(0.0511)
0.7630***
(0.0520)
0.7776***
(0.0523)
0.8405***
(0.0529)
0.8385***
(0.0529)
.6431 (.6382)
0.66037
132.003
***Significance at 1%
(0.0726)
0.0792
(0.0699)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
.7148 (.6925)
0.52894
32.01
The only important change is the decline in the agent coefficient‡‡‡‡‡,
which implies that an average sport agent’s ability to influence their client’s
salaries has been diminishing over time. This decline in the average agent’s
influence is likely the result of the increasingly restrictive contract rules since the
1999 CBA.
Model 2: Highly Regarded vs. Ordinary Representatives Results:
Variables/Season
Constant
Games Played (GP)
‡‡‡‡‡
1995 CBA
-7.2577***
(1.436)
0.0021*
1999 CBA
-8.9207***
(0.8185)
0.0013
2005 CBA
-8.1240***
(2.0780)
0.0021
Although the agent coefficient is not statistically significant from zero in the 1995 and 1995 CBAs, I
ran a hypothesis test and found that the 1995 coefficient is greater than the 1999 coefficient..
123
Points Per Game (PPG)
Rebounds Per Game (RPG)
Assists Per Game (APG)
Blocks Per Game (BPG)
Steals Per Game (SPG)
Team Win %
Championship
All-Star
Age
Age Squared
Experience
Height (inches)
Race
Foreign
Forward
Center
1st Round Before 1995 CBA
1st Round After 1995 CBA
Contract-Extension
Resign Restricted Free Agent
Sign Restricted Free Agent
Sign Unrestricted Free Agent
Contract Year
Mark Bartelstein
(0.0012)
0.0518***
(0.0062)
0.0460***
(0.0139)
0.0772***
(0.0184)
0.1730***
(0.0550)
-0.0087
(0.0710)
0.4002***
(0.1267)
-0.0062
(0.0689)
0.0136
(0.0167)
0.3518***
(0.0745)
-0.0071***
(0.0013)
0.1173***
(0.0181)
0.0182*
(0.0108)
0.1596***
(0.0585)
0.4101**
(0.1706)
0.0392
(0.0688)
0.1326
(0.1068)
0.2109***
(0.0748)
-0.0366
(0.1045)
-0.1398*
(0.0839)
0.0704
(0.0884)
0.1606
(0.1875)
-0.3357***
(0.0547)
-0.3396***
(0.0547)
0.0414
(0.0008)
0.0483***
(0.0044)
0.0601***
(0.0106)
0.0707***
(0.0130)
0.2319***
(0.0369)
0.0234
(0.0472)
0.3309***
(0.0946)
0.0142
(0.0477)
-0.0018
(0.0111)
0.4786***
(0.0450)
-0.0089***
(0.0008)
0.1277***
(0.0110)
0.0127**
(0.0057)
0.0156
(0.0383)
0.0298
(0.0723)
0.0005
(0.0443)
0.0907
(0.0637)
-0.1289
(0.1089)
-0.0827
(0.0527)
0.2615***
(0.0526)
0.2177***
(0.0715)
0.1268
(0.0824)
-0.3424***
(0.0350)
-0.3205***
(0.0339)
0.0825
(0.0018)
0.0536***
(0.0092)
0.0509**
(0.0230)
0.0476
(0.0303)
0.0979
(0.0805)
0.1244
(0.1090)
0.2808
(0.2063)
0.0899
(0.0981)
-0.0501**
(0.0252)
0.3550***
(0.0973)
-0.0071***
(0.0017)
0.1335***
(0.0233)
0.0396**
(0.0166)
-0.0666
(0.0819)
-0.0771
(0.1185)
-0.0883
(0.0938)
-0.1938
(0.1390)
N/A
(N/A)
-0.0671
(0.1219)
0.2465**
(0.1182)
0.2086*
(0.1121)
0.1932*
(0.1164)
-0.1074
(0.0893)
-0.2458***
(0.0719)
0.0454
124
Aaron Goodwin
Bill Duffy
Arn Tellem
David Falk
Marc Fleisher
Lon Babby
Dan Fegan
Small Market Team
Large Market Team
This Year is 1996
This Year is 1997
This Year is 1998
This Year is 1999
This Year is 2000
This Year is 2001
This Year is 2002
This Year is 2003
This Year is 2004
R-Squared (Adj)
Standard Error
F-Statistic
*Significance at 10%
(0.1176)
0.3946
(0.2016)
-0.4606**
(0.1928)
0.1899**
(0.0833)
0.2125***
(0.0769)
0.2416
(0.2444)
0.1387
(0.2606)
0.5001**
(0.2147)
0.0287
(0.0439)
-0.0004**
(0.0002)
0.0547
(0.0501)
0.1870***
(0.0518)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
.6513 (.638)
0.61786
48.724
**Significance at 5%
(0.0573)
0.2373**
(0.0983)
0.1884**
(0.0775)
0.1730***
(0.0483)
0.1679***
(0.0534)
-0.0356
(0.1267)
0.0789
(0.0836)
0.1525**
(0.0687)
0.0284
(0.0294)
-0.0003
(0.0003)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
0.4901***
(0.0534)
0.7121***
(0.0509)
0.7502***
(0.0518)
0.7600***
(0.0524)
0.8256***
(0.0529)
0.8240***
(0.0530)
.6477 (.6419)
0.657
110.821
***Significance at 1%
(0.1075)
-0.2937
(0.2318)
0.1507
(0.1332)
0.1228
(0.1127)
0.2928
(0.1819)
0.3019*
(0.1829)
0.3357**
(0.1667)
-0.0114
(0.1173)
0.0328
(0.0734)
0.0585
(0.0709)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
N/A
(N/A)
.7225 (.6944)
0.5273
25.648
While the average sports agent’s influence is declining some highly
regarded representatives are not suffering the same fate. Some of these
125
representatives’ affect on their respective clients’ salaries have either fluctuated
or increased with each CBA.
Limitations of the Model
The variables I used to measure offensive productivity in the salary model
may not be the best variables. The offensive statistics I used measure a player’s
final output, which does not explain how productive players are in certain
situations or how efficient they may or may not be. Certain players are very
productive in short spurts and not so productive when they play for extended
minutes. For example, Chicago Bull’s guard Ben Gordon is a prolific scorer off
the bench, but struggles as a starting player. Some players are terrible when they
play in limited minutes, but play well when they are given extended minutes.
Sacramento Kings’s forward Kenny Thomas is an extreme example of a player
who plays horribly in limited minutes off the bench but plays okay when he is
given extended minutes as a starter. A player who has good performance
statistics on paper may not really be a good player, but an inefficient player. A
player is inefficient when he takes a lot of shots to get his points or turnover the
ball a lot as he tries to build up his assist totals. A player’s performance statistics
may be inflated or depressed due to the tempo his team plays. For example,
offensive statistics for Phoenix Suns players were inflated in the 2004-2005
Season since the Suns played at a frenetic pace, which gave them many more
offensive possessions than anyone else. The limitations of the traditional
126
performance statistics (points per game, rebounds per game, etc) has motivated
some to develop new performance measures that can better describe a player’s
play. For instance, the NBA’s official website now reports in each player profile
a statistic that purports to quantify a player’s efficiency.
Another problem with the salary model is that most of the performance
variables measured offensive production. As a consequence, a player’s defensive
skill was not fully considered in the model because it is extremely difficult to
quantify a player’s defensive ability. Blocked shots and steals per game are the
most commonly used measures to quantify a player’s defensive ability, but one
can argue that these two statistics may overrate a player’s defensive ability. A
player who steals a lot of passes during a game may gamble a lot on defense,
which is a sign of a poor defender. A player who blocks a lot of shots may not
necessarily be a good defender. They may be able to block a lot of shots due to
their height, but when they are not blocking shots could be a defensive liability.
A shot blocker may lack the lateral quickness necessary to play one on one
defense§§§§§, which can result in the offensive player getting an easy path to the
basket. The model suggests that players are paid mostly for their offensive
production, which may or may not be the case in reality.
The salary model does not account for anything that occurs during the
postseason. This is a problem because some players can make a lot of money
§§§§§
On defense players not only run forwards and backwards but also shuffle their feet side to side or
laterally. Lateral quickness enables a defender to stay with his man as they move diagonally towards the
basket.
127
based on how they perform in the playoffs. For example, Kings’ guard Mike
Bibby played like an average point guard during the 2001-2002 regular season,
but played like a superstar during the 2002 NBA playoffs.
2001-2002 Season
Regular Season
Post Season
Points/Game
13.7
20.3
Rebounds/Game
2.8
3.8
Assists/Game
5.0
5.0
Bibby excelled in the 2002 postseason as he took over games and hit numerous
clutch shots. His play improved each game as he carried his team to the brink of
an NBA Finals appearance. However, the Kings lost in overtime of Game 7 of
the Western Conference Finals to the Lakers. Fortunately for Bibby his amazing
postseason play came at the end of his contract year. Bibby’s regular season
performance statistics and non-performance characteristics indicated he should
only had been offered $4,000,808 in the first year of his new contract. However,
Bibby’s postseason play allowed his agent David Falk to convince the Kings that
his client could play like that consistently; especially when the game was on the
line. According to the salary model, the Kings overpaid for Bibby since they
gave him $8.5 million in the first year of a new 7 year $80 million contract.
One must exercise extreme caution when using the salary model to
compare players. Although a player is estimated to earn more money than
another player, it does not mean that the player with the higher estimated salary
is the better player. The model cannot account for differences in a player’s talent
or differences in a player’s skill. The model also tends to award older players
higher salaries than younger players since the age and experience coefficients are
128
extremely positive and the 1st round draft pick post 1995 coefficient is extremely
negative. For example, the salary model says that 14 year veteran Stacey
Augmon should earn a higher salary than second year player Luol Deng in the
2005-2006 Season.
Season
2004-2005
Player
Stacey Augmon
Points/Game
3.5
Rebounds/Game
1.8
Assists/Game
0.7
Estimated Salary
$1,927,590
2004-2005
Luol Deng
11.3
5.3
2.2
$1,886,992
Although the model is doing its job, any NBA general manager will tell you they
would rather have Luol Deng on their team than Stacey Augmon. Deng will
likely be a superstar in a few years while Augmon will be out of the NBA.
Conclusions:
NBA player representatives are able to demand multimillion dollar
contracts for their clients because the NBA collective bargaining agreement
guarantees players a majority of league revenue through a revenue-sharing
program. Teams must spend money a minimum amount of money on players,
but they often spend beyond the salary cap, a method of revenue distribution
that aligns the interests of players and owners. Players and owners are satisfied
with this arrangement because it has brought a large degree of certainty to both
sides.
The NBA is a winner-take-all-market with a wide disparity between the
highest paid and lowest paid players. Players differentiate themselves through
their on the court performance and with their non-performance characteristics.
129
Quality players command a large portion of their respective team’s payroll since
they can make a major difference in determining whether a team contends for a
championship or does not make the playoffs. Teams do not pay lesser players
nearly as much as they pay quality players because their supply is larger.
Regardless of a player’s quality, the league shares enough revenue with its
players so that, if it was distributed equally, each player would make a
multimillion dollar salary.
The way a player is represented in contract negotiations does not affect
how much they are paid. However, the individual representative does affect
whether a player makes more, equal, or less than their peers. Players
represented by highly regarded representatives make, on average, more than
players represented by a lesser regarded representative.
With the presence of asymmetric information in free agency NBA teams
look for indicators to determine if a player will play well on their team. Players
attempt to send interested teams a signal when they hire or switch to highly
regarded reputations. Since representatives with good reputations tend to
negotiate good contracts, players are attracted to them. However, player
representatives have an incentive to limit the number and type of clients they
represent, and this limitation serves as a signal for teams that sends them some
information about the quality of the player they are interested in.
Team owners do not benefit directly from investing money in their
players in the sense that they do not create additional wins or generate
130
additional revenue. However, an owner’s additional investment can allow the
team to keep good players and/or acquire others, which may increase the
chances of success. The more a team wins, the more fan enthusiasm they
generate, and the result is higher team revenues.
The average sports agent’s influence over their clients’ salaries has
declined with each CBA, as new salary rules restrict the ability of agents to
negotiate large contracts for their clients.. Although sports agents have a lesser
impact on their client’s salaries than they had in the past, they will always
remain part of the NBA labor market.
Acknowledgements:
I like to thank Larry Coon for giving me his permission to use his work in
this study because I could not have accurately described the important features
of the 1999 and 2005 CBA’s without it. I also like to thank Dr. Phillip Martin for
his guidance and suggestions.
Bibliography
Agesa, Jacqueline., Agesa, Richard U., and Toshkova, Maria. NBA Salaries: Role
Players and Superstars. The Sport Journal. 8.2 (2005): 7 Oct. 2005
<http://www.thesportjournal.org/2005Journal/Vol8-No2/jacquelineagesa.asp>.
131
Badenhausen, Kurt. “The Business of Basketball”. Forbes.com 22 Dec. 2005. 28
Dec. 2005. <http://www.forbes.com/2005/12/21/basketballvaluations_05nba_land.html>.
“Basketball History”. 2005. history-of-basketball.com 8 Nov. 2005.
<http://www.history-of-basketball.com/history.htm>.
Beacham, Greg. “Recap”. 2005. Yahoo Sports. 1 Feb. 2006.
<http://sports.yahoo.com/nba/recap?gid=2005042023>.
Bender, Patricia. Patricia’s Various Basketball Stuff. 2005. 18 Nov. 2005.
<http://www.dfw.net/~patricia/>.
Bradley, Robert. “Labor Pains Nothing New to the NBA”. 2005. APBR Hoops.
27 Oct. 2005. <http://members.aol.com/apbrhoops/labor.html>.
Bruton, Mike. “Big-Name Agents Corrupt NBA Talks”. The Deseret News. 5
Jan. 1999: Sports. Newsbank. Shields Library., Davis, CA. 27 Nov. 2005
<http://infoweb.newsbank.com/>.
Coon, Larry. NBA Salary Cap/Collective Bargaining Agreement FAQ. 2005 4
Oct. 2005. <http://members.cox.net/lmcoon/salarycap99.htm>.
Coon, Larry. NBA Salary Cap/Collective Bargaining Agreement FAQ. 2005. 9
Nov. 2005. <http://members.cox.net/lmcoon/salarycap.htm>.
Cuban, Mark. “What Business is the NBA”. Online posting. 18 Nov. 2005.
<http://www.blogmaverick.com/entry/9658878343376412/>.
Dey, Matthew S. "Racial Differences in National Basketball Association Players'
Salaries: Another Look," The American Economist. 41:2 (1997): 84-90.
132
DuPree, David. Basketball Salaries Database. 2005. 28 Oct. 2005.
<http://asp.usatoday.com/sports/basketball/nba/salaries/default.aspx
>.
Eschker, Erick, Perez Stephen J., and Siegler, Mark V. The NBA & the Influx of
International Basketball Players. Applied Economics 36 (2004). 1009-1020.
Fort, Rodney. Rodney Fort’s Sports Economics Sports Business Data. 2005. 20
Oct. 2005.
<http://www.rodneyfort.com/PHSportsEcon/Common/OtherData/Dat
aDirectory.html>.
Frank, Robert H. and Cook, Phillip J. The Winner Take All Society. New York:
Free Press, 1995.
Friedman, David. “Basketball in America”. Hoopshype. 27 Oct. 2005.
<http://hoopshype.com/articles/america_friedman.htm>.
Garcia, Art. Buckner Draws Prime Suitors. Fort Worth Star-Telegram. 17 Jul.
2002: Sports Pg. 8. Newsbank. Shields Library., Davis, CA. 22 Nov. 2005.
<http://infoweb.newsbank.com/>.
Groothuis, Peter A. and Hill, J. The New NBA Collective Bargaining Agreement,
the Median Voter Model, and a Robin Hood Rent Redistribution. Journal
of Sports Economics. 2.2 (2001): 131-144.
“Hawks Sign Kevin Willis”. The Official Site of the Atlanta Hawks. 27 Jan. 2006.
<http://www.nba.com/hawks/news/Hawks_Sign_Kevin_Willis_092204
.html>.
133
Heisler, Mark. “Joke’s on NBA and Its Players in Lockout”. Los Angeles Times.
8 Aug. 1995: C3 Newsbank. Shields Library., 27 Nov. 2005. Davis, CA.
<http://infoweb.newsbank.com/>.
Jorgensen, Loren. Malone Hires Manley as Agent. Deseret News. 14 Jul. 1998:
D1. Newsbank. Shields Library., Davis, CA. 9 Nov. 2005.
<http://infoweb.newsbank.com/>.
Lee, Michael. “Hughes Says Wizards Made Him ‘Go Looking Around for
Offers’”. Washington Post. Aug. 5, 2005. 18 Oct 2005.
<http://www.washingtonpost.com/wpdyn/content/article/2005/08/04/AR2005080402171.html>.
Munsey, Paul and Cory Suppes. Ballparks by Munsey and Suppes. 2006. 10 Jan.
2006. <http://basketball.ballparks.com>.
“National Basketball Association: 2002-2003”. Team Marketing Report. 3 Jan.
2006. <http://www.teammarketing.com/fci.cfm?page=fci_nba_0203.cfm>.
‘NBA TV Contracts”. InsideHoops.com. 27 Nov. 2005.
<http://www.insidehoops.com/nba-tv-contracts.shtml.>.
NBPA FAQ. NBA Player’s Association. 2005. 11 Nov. 2005.
<http://www.nbpa.com/faq.php>.
NBPA History. NBA Player’s Association. 2005. 29 Oct. 2005.
<http://www.nbpa.com/history.php>.
134
NBPA Regulations Governing Player Agents. New York: National Basketball
Players Association, June 1991.
“New NBA CBA”. InsideHoops.com. 20 Oct. 2005.
<http://www.insidehoops.com/nba-collective-bargainingagreement.shtml.>.
Povtak, Tim. Agent Provocateurs: Are They Ruining the Game? The Orlando
Sentinel. Sept. 10, 1995: C16. Newsbank. Shields Library., Davis, CA. 10
Nov. 2005 <http://infoweb.newsbank.com/>.
Professional Basketball Transaction Archive. 2005. 23 Nov. 2005.
<http://www.n-c-systems.com/hoops/index.html>.
Rosen, Sherwin and Sanderson, Allen. Labour Markets in Professional Sports.
The Economic Journal. 111 (2001): 47-68.
Rosenbaum, Dan T. It Doesn’t Pay to be Young in the NBA. 2003. 20 Oct. 2005.
<www.uncg.edu/bae/people/rosenbaum/youngnba1.pdf>.
Rosenbaum, Dan T. and Stein, Andy. Re-Negotiating the NBA Collective
Bargaining Agreement. 2003. 27 Nov. 2005.
<http://www.uncg.edu/bae/people/rosenbaum/NBA/cba1.pdf>.
Rosner, Scott R. The History and Business of Contraction of Major League
Baseball. 2002. 24. Oct. 2005.
<gst.wharton.upenn.edu/srosner/Fireworks/Support%20files/MLB%20
Contraction%20Article%20-%2009.11.02.pdf>
135
Sandoval, Greg. For SFX, a Different Kind of Free Agent. Washington Post. 23
Oct. 2004 22 Oct. 2005. <http://www.washingtonpost.com/wpdyn/articles/A55479-2004Oct22.html>.
Shropshire, Kenneth L. and Davis, Timothy. The Business of Sports Agents.
Philadelphia: University of Pennsylvania Press, 2002.
Siegler, Kevin J. and Sackley, William H. NBA Player: Are they Paid for
Performance? Managerial Finance. 26.7. (2000): 46-51.
Spears, Marc J. Nuggets Notes: Kemp Plans to Check Out Chances to Rejoin
League. Denver Post. 24 Nov. 2005: Sports. Newsbank. Shields
Library., Davis, CA. 18 Jan 2005 <http://infoweb.newsbank.com/>.
The Sports Industry Overview. Street & Smith’s SportsBusiness Journal. 2006.
21 Feb. 2006.
<http://www.sportsbusinessjournal.com/index.cfm?fuseaction=page.fea
ture&featureId=43>.
Staudohar, Paul D. The Sports Industry and Collective Bargaining. Ithaca:
Cornell University, 1986.
Staudohar, Paul D. Salary Caps in Professional Team Sports. Compensation and
Working Conditions. Spring. (1998): 3-11..
Staudohar, Paul D. Labor Relations in Basketball: The Lockout of 1998-99.
Monthly Labor Review. 125.4. (1999): 3-9.
Steele, Doug. Doug’s NBA & MLB Statistical Home Page. 2005. 13 Oct. 2005.
<http://www.dougstats.com/>.
136
Stiroh, Kevin J. Playing for Keeps: Pay and Performance in the NBA. 2004. 12.
Oct. 2005.
<www.ny.frb.org/research/economists/stiroh/stiroh_contract_71504.pdf
>
US Census Bureau; Census 2000. Ranking Tables for Population of Metropolitan
Statistical Areas, Combined Statistical Areas, New England City and
Town Areas, and Combined New England City and Town Areas: 1990
and 2000. (PHC-T-29) (2003) 12. Dec. 2005.
<http://www.census.gov/population/www/cen2000/phc-t29.html>
Whitley, David. NBA Draft Robs Cradle of America. The Tampa Tribune. 27
Jun. 1996: Sports Newsbank. Shields Library., Davis, CA. 9 Nov 2005
<http://infoweb.newsbank.com/>.
Youngblood, Kent. Sprewell Sounds Off on Contract Without Extension, Trade
Request Possible. Star Tribune. 1 Nov. 2004: Sports 3C. Newsbank.
Shields Library., Davis, CA. 28 Oct. 2005
<http://infoweb.newsbank.com/>.
“The 20 Most Influential People: Sports Agents”. Street & Smith’s
SportsBusiness Journal 2005. 27 Nov. 2005.
<http://www.sportsbusinessjournal.com/index.cfm?fuseaction=page.fea
ture&featureId=1238>