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 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 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 after signing Josh Smith.

Smith signed a four-year, $54 million deal but struggled on his new team, to the 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 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) (, Approximate Value, Versatility, Points per

2 , Ratio and Rating) to measure a player’s per 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. Do they then become more valuable players to their teams? Berri (1999) attempts to find a different measure of value by estimating how many wins a player adds to his team per minute over an average replacement. Controversially perhaps, he finds that Dennis Rodman was nearly as valuable or even more valuable than during the Chicago Bulls 1998-

99 championship run due to his 15.0 rebounds per game average compared to a league average of 8.8 per game for his position. This brings to light the idea that perhaps some statistical measures are overvalued and that more advanced measurements are required, a deficiency addressed by the current study.

A common media storyline claims that players in the final year of their current contract will play harder than in other years. The data Stiroh (2007) finds support this conception, and the study goes even further with the concept of shirking that Winkler (2013) investigated in his thesis. Using data from 349 players

5 who signed multiyear contracts from 1988 to 2002, Stiroh investigates the effect a player’s performance in his contract year has on the value and length of his next contract. The author finds that players not only play better than usual in their contract year; they play worse than usual in the first year of a long-term deal. He explains this finding by pointing out that the incentive for a good performance is much greater prior to signing a new contract than at the beginning of a contract.

Probably the most overlooked determinant of player salaries is the Collective

Bargaining Agreement (CBA) that governs all transactions. The CBA outlines the salary cap beyond which teams cannot sign new players, the many exceptions to that rule, the minimum and maximum salaries, and the limits placed on how teams can trade players and vary their salaries, among other things. For instance, a ten- year veteran has a higher minimum salary than an undrafted rookie, as well as a higher maximum salary. The CBA also provides incentives for teams to use in retaining players, including a higher maximum salary for players that re-sign with their current team compared to signing with a new team.

As shown, previous literature provides a basis for the current study.

However, a solid understanding of the theoretical underpinnings of the study is integral to evaluating and deriving conclusions from it.

Theory

Of the economic theories related to the current study, first and foremost is market efficiency. Every team can access almost all relevant information about players, so in a perfect world market inefficiencies would vanish. Everyone has access to all the traditional box score stats and the conventional advanced stats,

6 such as player efficiency and true shooting percentage, so in theory every team could come to the same conclusion about how much every player is worth.

Historically, however, some teams tremendously over value players while others undervalue them. Not every team places the same value on the same skills however; for example the currently prize three-point shooting above everything else, while the Philadelphia 76ers are searching for the youngest talent with the highest potential they can find. The more attractive teams to free agents are the big market and good weather, and to level the playing field for all involved, each team has a limited amount of salary it can spend to sign new players. Thus a roster becomes a capital allocation problem: how can you generate the most production and talent from a limited resource? If the market is efficient, then teams are forced to exploit other opportunities, notably the cost controlled draft. If the market is inefficient, then smart teams can take advantage.

This study also falls under the theory of labor economics, which deals with the relationship between workers and employers, especially wage and salary contracts. Salaries in the NBA are a mix between pay-for-performance and salary earned due to time spent in the league. Every drafted player is paid according to when he is drafted, where the top picks earn significantly more than the rest

(approximately $5 million for a number one pick compared to $500,000 for the last pick). Once that contract expires, players then can negotiate based on their talent and production in a pay-for-performance manner. However their ceiling is limited by the time they have spent in the league. The scale is set at 25% of the salary cap for 0 to 6 years in the league, 30% for 7 to 9, and 35% for 10+. Exceptions exist, but

7 players only rarely qualify for them. The minimum salary also increases for every year a player spends in the league, however despite the differences in minimum salaries for different experience levels, each contract counts the same amount for salary cap purposes. This prevents teams from prioritizing youth simply because young players on minimum deals are cheaper. If the NBA is a pay-for-performance league, then supply and demand for certain skills and production will set the wages, while if it isn’t, age will likely be the most significant factor.

The theory of industrial organization also should be considered since various aspects are governed by the CBA. The NBA can be viewed as both a monopoly and as a field of almost perfect competition. It is a monopoly in that no one can simply create a team and join the league, but at the same time the league strives to give every team the same advantage through the CBA, which governs every action and scenario. Critical to this study is the salary cap enforced by the CBA, which is set at approximately $58.7 million for the 2014-15 season. However, this cap is set to rise an undetermined, but significant amount, possibly as high as $90 million within only a few years. Since maximum salaries are also tied to the cap, they will jump as well.

The result will be an arguably new, highly regulated market with a greater amount of information available to everyone, and only a few teams will have natural advantages to play.

Lastly, for the sake of the fans, this investigation should be viewed under the lens of competitive balance. Under true competitive balance, no business has an advantage over any other business. When applied to sports, competitive balance includes the idea that every team has an equal chance at winning the championship.

8 Competitive balances drives profits by attracting and keeping fans; few desire to watch a game with a single certain outcome. The National Football League has achieved this to the point that more than one defending Super Bowl champion failed to qualify for the playoffs the next season. The NBA, however, has perhaps the lowest level of competitive balance out of the four major sports leagues in the U.S., with only eight different teams winning the championship in the past thirty years.

For comparison, the NFL has had eight different champions in the past decade alone, one explanation for why the Super Bowl is more popular than the NBA Finals.

Conversely, few teams in the NFL try to lose games, while for the past two seasons, according to Taylor (2014), the Philadelphia 76ers’ management has made it their mission to lose as many games as possible in an effort to game the system and receive multiple high draft picks. This leads to a loss of fans, attendance, and revenue for the team. A healthy league with sustainable revenue requires a good level of competitive balance.

The economic theories outlined here provide a sound base for the current study. However, concrete evidence in the form of data is required to draw any conclusions going forward. The next section presents the data and model used to provide such evidence.

Data and Methodology

This study uses the contract year (the year before a new contract is signed) per game data for each active player who is not on rookie salary, in addition to information such as age and height. Rookie salaries are determined by their draft slot, and thus cannot be evaluated the same way. Due to the relatively short length

9 of contracts, every active player has signed a new contract within the past six years.

A few players have been omitted because they had no experience in the NBA prior to signing their current contract, and three others (Danny Granger, Dwyane Wade, and

Kobe Bryant) are evaluated on the contract prior to their current one due to injuries.

All data are retrieved from Basketball-Reference.com, a website with a reputation for accurate data and lots of it, although the disclaimer on the site states that minor inaccuracies with official totals may exist. This includes the approximate values of contracts, as those specifics are not always officially released. Josh Smith signed for $54 million over four years with annual raises, but the year-to-year values were not published. However, any maximum or minimum contract is known since those are defined by the CBA, and many contracts are simple (e.g., Mike Scott signing for $10 million over three years). The majority of contracts are fairly simple, and all follow the same rules, so estimating them is straightforward.

This study uses a regression analysis to determine what independent variables significantly affect the expected yearly salary of a new contract, the dependent variable of the regression model. The independent variables considered are listed with their expected sign in the appendix. Due to high multicollinearity many variables are dropped, with the reduced model and the expected signs listed below

Reduced model:

Annual Salary = β0 + β1G + β2GS + β33FG + β4TR+ β5AST + β6ST + β7BLK - β8PF +

β9PPG + β103FG% + β11TS% + β12PER + β13WS + β14WS/48 + β15HT – β16AGE

10 Table 1 – Independent Variables

Variable (definitions provided in text) Abbreviation Expected Sign Games Played G + Games started GS + 3-pointers Made per Game 3FG + Total Rebounds per Game TR + Assists per Game AST + Steals per Game ST + Blocks per Game BLK + Personal Fouls per Game PF - Points per Game PPG + 3-point Field Goal Percentage 3FG% + True Shooting Percentage TS% + PER + Win Shares WS + Win Shares per 48 minutes WS/48 + Height HT + Age AGE -

The basic box score stats will be primarily positive. Points, rebounds, assists, steals, and blocks are all measures of positive production, and players who do those well should make more money. Turnovers and personal fouls are measures of undesirable production and should have a negative impact on expected salary.

Conclusions can easily be drawn from these volume stats, though they can be artificially inflated in certain game situations.

The +/- relationship of the volume measurement of shots made and attempted is harder to predict. Made field goals (field goals are all two and three point shots taken in play, as opposed to free throws, which are taken during a play stoppage) should have a positive impact, since the point of the game is to put the ball in the basket. Field goals attempts are a bit more nebulous. On one hand, coaches try to design plays that result in shots for the most talented players, so

11 more attempts should mean that the coach believes the player is talented, but on the other hand, attempts can lead to misses, and if a player is missing a lot he will not get paid as much.

Three point-field goals tend to be a niche stat/skill for players, as many specialize in only doing that. J.J. Redick is one such player, averaging almost the same number of three pointers made per game as two point shots made. However, while there is a bit of a premium to be paid for good three-point specialists, very few of them are stars or get paid star salaries. This suggests that, while more three- pointer success leads to a higher salary, diminishing returns or a ceiling is present.

The shooting percentages are quality measures of a player’s efficiency at scoring, and unlike the volume stats, they are often somewhat deceiving. Centers traditionally have the highest field goal percentages because they shoot close shots, while point guards often have to force long shots with a high degree of difficulty and often have the lowest field goal percentage of any player. However this does not mean that centers are better shooters than everyone else. Still, efficiency is always good, and the effect on salary should be positive.

Effective field goal percentage and true shooting percentage are attempts to measure just how good a shooter a player is by weighting three-point makes as 1.5 makes and, in the case of true shooting percentage, also including free throws in the calculation. Thus Anthony Tolliver can shoot an inefficient 42% from the field, while his effective field goal and true shooting percentages are a very good 59% and

60%, respectively. This suggests that Tolliver smartly chooses his shots for maximum effect.

12 Games, games started and minutes per game are decent proxies for how highly a coach values a player. NBA coaches try to win, and if one player gives them a better chance at winning than a different one, the first player will play more minutes and more games.

The advanced statistics of win shares, win shares per 48 minutes, and player efficiency rating (PER) are harder to evaluate, since they are calculated, not reported as are the other statistics, and not as easily available to fans. Win shares is an estimate of how many wins a player has added to his team over a season, while win shares per minute is an estimate of how many wins per 48 minutes, with the average player expected to provide .100 wins per 48 minutes. Since win shares is a volume statistic, players can easily inflate this by getting more minutes, and conversely, injured players will not produce as high a number. PER is calculated so that an average player is fifteen, while a bench player might be between ten and fifteen, a star between twenty and thirty and a historically good player at thirty and above. PER often has the opposite problem compared to win shares: it is relatively easy for a player to play two minutes a game for several games and end up with a

PER above twenty when it would normally be below fifteen. Given a large enough sample, PER will average out, and historically NBA teams do not give lots of money to players who have miniscule amounts of experience.

Height is a precious commodity in the NBA and is expected to be positively correlated with higher salaries, while age is more complicated. On one hand, youth is valued highly, as athleticism tends to decrease with age, but on the other hand, the

CBA grants higher maximum and minimum salaries to players who have been in the

13 league longer. Unfortunately for older players, youth tends to indicate higher athleticism, and many teams value athleticism above all else since it can’t be taught.

An athletic player may learn how to be a great shooter, but a great shooter will never be able to increase his athleticism noticeably. Also, teams will have more years to teach a young player than an older one, and this would lead teams to pay more for youth. The results of the analysis will be shown and explained in the following section.

Results and Analysis

This section will evaluate the results of the analysis by focusing on each variable and its significance or lack thereof. First however, an examination of the summary statistics of each variable and a correlation matrix (found in the appendix) are presented.

As seen, the players in the dataset are quite diverse in their talents and salaries, ranging from the minimum of $650,000 to Kobe Bryant’s $24,800,000. The mean salary is $6,358,496, which suggests that the majority of players earn less than that amount. Interestingly, the mean player efficiency rating is close to 15, which is what the league average is always adjusted to be per year, despite having data from across multiple seasons, and the win shares per 48 minutes is reasonably close to .100, which is the approximate league average.

Notably, more than a few variables have similar numbers, in particular the shooting percentage and rebounding variables are fairly closely connected. A correlation matrix (found in the appendix) shows multiple such relationships with high degrees of correlation between variables.

14 Table 2 – Summary Statistics

Variable Mean Standard Min Max Deviation Salary $6,358,496 $5,348,496 $650,000 $24,800,000 Age 27.0 3.6 22 37 Games Played 63.4 18.0 5 83 Games Started 34.9 31.0 0 82 Minutes Per Game 24.9 8.4 4.9 39.7 Field Goals per game 4.1 2.1 .6 10 Field Goals Attempted per 8.8 4.4 1.2 21.5 Game 2-pointers per Game 3.5 4.1 .2 8.6 2-pointers Attempted per 6.6 3.9 .4 17.4 Game 3-pointers per Game .8 .75 0 3.5 3-pointers Attempted per 2.2 1.9 0 7.7 Game Free Throws per Game 1.9 1.5 0 7.6 Attempted per 2.5 1.8 0 9.5 Game Offensive Rebounds per 1.1 .9 0 4.3 Game Defensive Rebounds per 3.3 1.8 .4 9.2 Game Total Rebounds per Game 4.4 2.5 .7 13.3 Assists per Game 2.4 2.0 0 10.7 Steals per Game .8 .4 0 2.4 Blocks per Game .5 .48 0 3 Turnovers per Game 1.5 .8 .1 4 Personal Fouls per Game 2.0 .67 .3 3.8 Points per Game 10.9 5.8 1.3 27.7 Field Goal Percentage .464 .061 .304 .736 2-point Field Goal Percentage .489 .061 .214 .736 3-point Field Goal Percentage .301 .156 0 1 Free Throw Percentage .749 .108 .344 .94 Effective Field Goal .509 .054 .336 .736 Percentage True Shooting Percentage .545 .05 .379 .73 Player Efficiency Rating 15.3 4.2 3.4 30.4 Win Shares 4.1 3.1 -.4 15.9 Win Shares per 48 minutes .112 .069 -.058 .8 Height 79 3.6 69 86

15 Since a number of variables exhibit high degrees of correlation, several were eliminated before running regressions. The exact variables omitted due to high collinearity (defined as a degree of correlation greater than .65 between two explanatory variables) are as follows: minutes per game, field goals made, field goals attempted, two-pointers made, two-pointers attempted, three-pointers attempted, free throws made, free throws attempted, offensive rebounds, defensive rebounds, turnovers, field goal percentage, two-point field goal percentage, free throw percentage, and effective field goal percentage. Along with multicollinearity, heteroskedasticity was detected using the Breusch-Pagan test, and the final model is adjusted to compensate. The final regression equation used is:

Annual Salary = β0 + β1G + β2GS + β33FG + β4TR+ β5AST + β6ST + β7BLK - β8PF +

β9PPG + β103FG% + β11TS% + β12PER + β13WS + β14WS/48 + β15HT – β16AGE

The coefficients and t-statistics are shown on the following page. The model has a very high R-squared and adjusted R-squared value at 0.7712 and 0.7549 respectively, indicating that the model can explain approximately three quarters of the variance in salary.

Predictably, points per game is the most significant variable. For a variable with greater range than most, points per game has a large coefficient, with each point per game adding $622,158.30 to the expected yearly salary. Games are won by having more points than the opponent, so it makes sense that players that score are considered the most valuable. Also unsurprisingly, win shares, the estimated number of wins added by a player, is the second most significant variable. Each win

16 Table 3 – Regression Results Variable Coefficient T-Statistic Games Played -57,122.97 -4.36*** Games Started 1,998.77 0.20 3-pointers Made per Game -1,571,449 -4.16*** Total rebounds per Game 75,018.21 0.47 Assists per Game 426,539.5 2.88** Steals per Game 277,118.1 0.43 Blocks per Game 1,136,167 1.94* Personal Fouls per Game -318,734.2 -0.76 Points per Game 622,158.3 8.07*** 3-point Field Goal Percentage -352,286.7 -0.24 True Shooting Percentage 10,502,560 1.80* Player Efficiency Rating -282,306.2 -2.46** Win Shares 776,385.2 5.65*** Win Shares per 48 minutes -4,472,021 -1.26 Height 136,276 1.63 Age -77,071.5 -1.52 R-squared 0.7712 Adjusted R-squared 0.7549

*** Significant at .01 ** Significant at .05 * Significant at .10 a player adds is worth $776,385.20 to their team. This suggests that the NBA is a results-driven league.

Assists per game is significant at the 5% level, indicating that players who make plays for their teammates are valued. Players with high numbers handle the ball more than their teammates, and given that they will be making lots of decisions with the ball, talented ball handlers are valued highly. Turnovers are highly correlated with assists, and subsequently are dropped from the model, but that correlation suggests that ball handlers might be paid more regardless of their skill level. However, presumably any player who is the primary distributor for the team is likely to be talented to begin with, which would lead to higher salaries

17 anyway, so perhaps this stat is valued as an indicator of talent level more than as a measure of production.

Age and height were shown to be insignificant, but both had expected signs, as the coefficient is negative for age and positive for height. This is some indication that the theory behind those variables is correct.

True shooting percentage is only significant at the 10% level, indicating that while some teams may value efficiency in scoring, not all do. Alternatively, it could be that some of the best scorers are inefficient. Players such as Josh Smith, Rudy

Gay, Joe Johnson, and Deron Williams are among the best scorers and highest paid athletes, but they sport below-average true shooting percentages. Other players such as Larry Sanders and Roy Hibbert command large salaries due to factors other than scoring, mostly defensive skills, which could reduce the significance of the variable.

Neither of the two defensive measures is significant at the 5% level, although blocks are significant at the 10% level. Steals are somewhat random compared to the other statistics as they depend more on the situation at hand than talent. Most steals are the result of error by an opposing player, and they are harder to manufacture by a defender’s talent alone than blocks. They are also scheme dependent. The Miami Heat team that won back-to-back championships used a defensive scheme designed to force turnovers by the other team, while the Indiana

Pacers, a better defensive team by popular consensus, has been near the bottom of the league in steals over a similar stretch. So if steals depend more on the coach than the player, it makes sense that they are insignificant in the model. The low

18 significance of blocks may be due to the lack of league-leading shot blockers among the highest-paid players. Most of the highest-paid players are very good at scoring

(Kobe Bryant, , LaMarcus Aldridge) or passing (Chris Paul, Deron

Williams, Marc Gasol), but only two of the top twenty-five highest-paid players this year are defensive-focused shot blockers: and Roy Hibbert. So while blocks might add to expected salary, most of the players who are currently skilled at it are not paid as highly.

Total Rebounds is surprisingly insignificant. In theory, rebounding is a crucial part of winning games, as it gives your team the ball and a chance to score, however it is a much less glamorous skill than most others. No one ends up on the highlight reel for getting a critical rebound as they might for a cool dunk or a big . Perhaps teams do not value skilled rebounders, or possibly only players without any other skill specialize in rebounding, in which case they would not be making much money anyway.

Personal Fouls is insignificant, which is not unexpected. Fouls tend to be the result of guarding talented players, not skill or lack thereof from the defender. Thus, they would have no impact on what a player earns.

Several variables had results contrary to expectation, such as games played, three-pointers made, three-point percentage, player efficiency rating, and win shares per 48 minutes. Each of these requires further discussion.

Games played, player efficiency rating and three-pointers made are negatively related to salary. Each game reduces salary by $57,212.97. Durability is a positive characteristic and is expected to be rewarded as much. Possibly this

19 result is due to the nature of the dataset, since some players were injured the year before they signed their new deal, notably Eric Gordon earning almost $15,000,000 with only nine games played, and Derrick Rose receiving nearly $19,000,000 after

39 games.

Player efficiency rating is a single measure of how well a player performs, and in theory the better an athlete plays, the more he should be paid. A quick look at the data reveals an interesting phenomenon. Most of the highest PERs belong to players signing extensions to their rookie deals or older veterans taking a pay cut.

Due to the CBA, extensions to rookie deals are capped below what an older player could earn, meaning that the players are likely earning salaries below market value.

The older veterans such as Dirk Nowitzki and Tim Duncan, while producing high

PERs, are paid less since their skills are on the decline. This situation could be the result of a period of lesser talents entering the league, followed by a recent surge in highly skilled players.

The negative coefficient for three-pointers made is surprising because there is a definite market for players who shoot three-pointers very well. One explanation for this is that most of the top three-point shooters are specialists and are not paid very highly, while many players (usually big men, since they traditionally don’t shoot three-pointers), earn a lot of money while not taking any three-pointers at all.

Possibly for similar reasons, three-point percentage has a negative coefficient. However the variable is insignificant, so the evidence is inconclusive.

Win shares per 48 minutes is insignificant with an unexpected coefficient. This is

20 likely because win shares per 48 minutes is a quality measure, so many role players can have a high number there while actually playing and getting paid very little.

Only six of the sixteen variables used in the regression turned out to be significant, however the model still explains about three quarters of the variance in salary with only a couple of unexpected results. A regressions with only the significant variables produces an R-squared of .7426, only slightly lower than the primary model; thus, a model with only six variables might be helpful in measuring possible inefficiencies in NBA contracts.

Conclusion

The purpose of this study was to identify the primary determinants of an

NBA player’s salary in an attempt to see how much control players can exert over their expected salary and how much is due to factors outside their control such as age. With the salary cap set to rise in the coming years, players can maximize their income if they know what teams look for in players, while the teams can identify overvalued or undervalued skills in an attempt to beat the market.

To address these issues, this study looked at every player’s performance the year before they signed their new contract as measured by the traditional stats and a few new advanced ones. Analysis revealed that teams most highly value statistics that contribute directly to wins. Points per game and win shares were easily the most significant variables, and assists were not far behind. This paints the picture of the NBA as a league driven by results. Wins matter, not the process. However, as teams are currently trending towards higher usage of advanced analytics in their player evaluations, it will be interesting to see if that focus changes.

21 This study has several limitations that should be considered for future research, the largest of which is the dataset. By nature, nearly every piece of data considered here is interconnected, producing layers of correlation between variables. Taller players tend to get more rebounds and blocks, have a higher field goal percentage, and have a lower free throw percentage, while nearly every one of the top players in the steals and assists categories is short, for example. Finding a few stats that measure multiple related statistical categories may help.

Another shortcoming is the lack of defensive statistics. Teams are starting to measure nearly everything possible on the offensive side, but lag considerably on measuring defensive capabilities. Some players such as Tyson Chandler and Tony

Allen have virtually no offensive skills but are among the most skilled defenders in the league and are paid as such. However, in the analysis done in this study, neither player does very much to earn his keep.

One final major limitation is difficulty in retrieving the data. Much of the advanced analytics are not available for public use, and what is available is difficult to find and gather in a useable form.

Future Research

In the future, more research should include the effects on career earnings, not just per season, investigating what skills translate best into career longevity.

Also, an analysis of each statistical measure would be very helpful for studies such as this: looking at volume of points per game versus efficiency and the effects on annual and career earnings, for example.

22 This study in particular could be refined by using more advanced data, such as plus/minus, synergy stats, offensive and defensive ratings, and more. An investigation into whether career stats or contract year stats are more influential should also be considered. The exact determinants of an athlete’s salary will likely never be fully unlocked due to the human nature involved in all such contracts, but this study is a good step in that direction.

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24 Appendix A – Original, Full Model

The original regression model will look like this:

Annual Salary = β0 + β1G + β2GS + β3M + β4FG + β5FGA + β62FG - β72FGA + β83FG +

β93FGA + β10FT + β11FTA + β12OR + β13DR + β14TR+ β15AST + β16ST + β17BLK -

β18TO - β19PF + β20PPG+ β21FG% + β222FG% + β233FG% + β24FT%+ β25EFG% + β-

26TS% + β27PER + β28WS + β29WS/48 + β30HT - β31AGE

Variable (definitions provided in text) Abbreviation Expected Sign Games Played G + Games started GS + Minutes per Game M + Field Goals Made per Game FG + Field Goals Attempted per Game FGA + 2-pointers Made per Game 2FG + 2-pointers Attempted per Game 2FGA - 3-pointers Made per Game 3FG + 3-pointers Attempted per Game 3FGA + Free Throws Made per Game FT + Free Throws Attempted per Game FTA + Offensive Rebounds per Game OR + Defensive Rebounds per Game DR + Total Rebounds per Game TR + Assists per Game AST + Steals per Game ST + Blocks per Game BLK + Turnovers per Game TO - Personal Fouls per Game PF - Points per Game PPG + Field Goal Percentage FG% + 2-point Field Goal Percentage 2FG% + 3-point Field Goal Percentage 3FG% + Free Throw Percentage FT% + Effective Field Goal Percentage EFG% + True Shooting Percentage TS% + Player Efficiency Rating PER + Win Shares WS + Win Shares per 48 minutes WS/48 + Height HT + Age AGE -

25 Appendix B – Correlation Matrix

26 Appendix B – continued

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