The Contract Year Determinants of an NBA Player's Salary
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THE CONTRACT YEAR DETERMINANTS OF AN NBA PLAYER’S SALARY A THESIS Presented to The Faculty of the Department of Economics and Business The Colorado College In Partial Fulfillment of the Requirements for the Degree Bachelor of Arts By Aaron Liss April 2015 THE CONTRACT YEAR DETERMINANTS OF AN NBA PLAYER’S SALARY Aaron Liss April 2015 Economics Abstract The purpose of this study is to identify the primary determinants of an NBA player’s salary from his contract season performance. While some factors are outside a player’s sway, such as height and age, others such as points per game are within their control, and this study examines which factors are significant. The study examines 272 players and data from the year before they signed their new contract. A regression analysis tests the relationship between salary as the dependent variable and a number of independent variables. The analysis reveals that NBA teams value players who score points and generate wins (as measured by win shares). While teams will never forego the human aspect and evaluation present in every transaction, analyzing the statistical side should help expose some market inefficiencies currently present in the NBA. KEYWORDS: (National Basketball Association, Player Salary Determinants, Performance) JEL CODES: (Z20, L1, J2) ON MY HONOR, I HAVE NEITHER GIVEN NOR RECEIVED UNAUTHORIZED AID ON THIS THESIS Signature TABLE OF CONTENTS ABSTRACT iii INTRODUCTION…………………………………………………………………………… 1 LITERATURE REVIEW………………………………………………………………….. 2 THEORY………………………………………………………………………………………. 6 DATA AND METHODOLOGY…………………………………………………………. 9 RESULTS AND ANALYSIS……………………………………………………………… 14 CONCLUSION……………………………………………………………………………….. 21 FUTURE RESEARCH……………………………………………………………………. 22 REFERENCES………………………………………………………………………………. 23 APPENDIX…………………………………………………………………………………… 25 LIST OF TABLES 1 Independent Variables ………………………………………………………………… 11 2 Summary Statistics ……………………………………………………………………... 15 3 Regression Results ……………………………………………………………………… 17 4 Independent Variables (original model) ………………………………………. 25 5 Correlation Matrix ………………………………………………………………………. 26 Introduction The National Basketball Association (NBA) ranks among the most popular and most profitable sports leagues in the U.S., as evidenced by the recent sale of the Los Angeles Clippers for approximately two billion dollars. With a new television rights deal much higher than projections, a lot more money will flow into the NBA, raising the salary cap and likely inciting a spending spree from general managers as they try to snag the best talent for their teams with the additional funding capacity. However this frenzy has many teams worried that they will overpay for talent or get less production out of their new players than they expected. In anticipation of this development, the current study investigates what factors influence how much a player gets paid. Do general managers pay for factors other than production, or do players earn their compensation as measured by their statistical performance? Every year it seems, a team goes out and signs a free agent to a contract with immediate regrets. Last year, that was the Detroit Pistons after signing Josh Smith. Smith signed a four-year, $54 million deal but struggled on his new team, to the point that the Pistons cut him from the team a quarter of the way into the second year of the deal. The Los Angeles Lakers signed Kobe Bryant to a two-year, $48.5 million dollar deal at an age where most NBA players have retired or taken reduced money and roles to continue in the league. Contrast Bryant with Dirk Nowitzki and Tim Duncan, who stand out as players who have deliberately taken less money than they could have and still produced at a high level. Nowitzki and Duncan are the exception rather than the rule however, as most other players seek to maximize 1 what they earn. Conversely, teams try to maximize production per dollar spent, so there is some give and take in negotiations, as can be expected. This thesis looks at the production of each NBA player at the time of his most recent, non-rookie salary. This comparison will allow discernment about whether factors that are mostly under a player’s control, such as basic statistics, have a significant influence on expected salary, or whether other factors, such as height, age, potential, etc., demonstrate a larger influence. In other words, do players earn their salary, or do teams decide how much a player is worth to them due to variables other than economic-oriented production? This section has provided some brief background and context for the investigation. The next section provides a literature review to give a better sense of the related economic questions that have been asked and answered previously, as well as to provide a springboard for the theoretical base for the research. The third section explains the economic theories that pertain. The fourth section outlines the data used and how it will be analyzed, describing the variables, regression models, and expected results. In the fifth section, analysis and results detail the findings, and this paper concludes with the implications of those results, the weaknesses of the study, and recommendations for future studies. Literature Review Labor contracts in the NBA differ from the norm, and deciding how much money to offer a player constitutes a complex decision. A thesis by Brodman (2009) attempts to address this complexity. He uses a regression analysis of the six indices created by Hollinger (2003) (Efficiency, Approximate Value, Versatility, Points per 2 Field Goal, Turnover Ratio and Rebound Rating) to measure a player’s per minute production and ability (along with other measurements such as race, position, age, team winning percentage, and team payroll) to explain a player’s expected salary. Save for the rookies, Brodman evaluates players from the 2006-07 season using their current salary and career statistics. He uses separate variables for career stats and contract year stats to determine whether career performance or the most recent season play is more significant. Brodman finds that points per field goal attempt represents the most significant variable, but the more valuable the player is to his team in his contract year, the less he is paid. The second finding is contrary to the expected result. Perhaps more advanced statistics could help explain his finding, or maybe something else is in play. Staw and Hoang (1995) offer an interesting idea that a sunk-cost effect could be in play for many teams. The more resources (high draft pick) and money poured into a player, the more opportunities he will have to succeed. This in turn would likely lead to a higher expected salary compared to later draft picks or minimum salary players. The authors examine the careers of players drafted from 1980 through 1986 and who had played at least two years in the NBA and explain minutes played as a function of statistical production per minute. They also group the box score stats into three categories, scoring, toughness and quickness, and find that scoring is the greatest predictor of playing time but that draft position is also significant (though it possibly decreases over time). A study by Groothuis, Hill, and Perri (2009) suggests that many teams are trying to find superstar talent and failing. They suggest that there are many more 3 false positives (players who have the potential to be a star but never become one) than there are actual stars, and this can lead teams to overpay players. The draft exemplifies this, where teams can signal that a player has star potential by picking him early on, but many of the best players in the league aren’t the first pick in their draft. The model finds that higher drafts picks tend to be better players as measured by the efficiency formula (an old model intended to put a single number on production), but the model has an R-squared value of between 16 and 17%. This implies that the general managers for teams are not very accurate when evaluating talent. In a separate and less formal setting, Bill Simmons, an ESPN writer for thirteen years and named one of the most influential people in online sports in 2007 by the Sports Business Journal, corroborates this by going through nineteen years worth of NBA drafts and ranking the players based on their actual careers. While his rankings and the exact order can be debated, he finds that the draft position is only a limited predictor of success, that in reality the best talents are fairly randomly drafted (his rankings from the 2011 draft are as follows: 15, 1, 11, 38, 22, 16, 5, 9, 60, 13, 30, 24). While teams have a suboptimal record at evaluating talent, teammates and other external circumstances play a role in a player’s success as well. A paper by Idson and Kahane (2004) shows that a player’s teammates can have a large effect on how that player produces. They regress salary against the points, assists, rebounds, steals, blocks and teammates’ productivity (as proxied by the coach’s years coaching and career winning percentage). The authors find that while the model is mostly insignificant for an individual, the effects on the teams are significant at the 5% 4 level. No one better exemplifies the idea that circumstances matter than Michael Redd. Redd was stuck behind his all-star teammate Ray Allen for two and half years in Milwaukee at the start of his career. The first year after Allen left and Redd became a starter marked the first of six straight seasons where Redd averaged over 20 points per game, ending only after he suffered a devastating knee injury. Before he became a starter, Redd signed a contract paying him about $3 million a year. After he became a starter and an all-star, he signed a new contract paying him on average about $15 million a year. Thus teammates and circumstances matter. Despite the somewhat extreme nature of Michael Redd’s breakout campaign as a back up to an all-star in one year, many players put up bigger and better numbers when given more playing time.