EXPLORATION OF INEFFICIENCIES IN THE NBA MARKET

______

A THESIS

Presented to

The Faculty of the Department of Economics and Business

Colorado College

In Partial Fulfillment of the Requirements for the Degree

Bachelor of Arts

By

Aidan Smith-Eppsteiner

1

ON MY HONOR, I HAVE NEITHER GIVEN NOR RECEIVED UNAUTHORIZED

AID ON THIS THESIS

Aidan Smith-Eppsteiner

Signature

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TABLE OF CONTENTS

ABSTRACT i

ACKNOWLEDGEMENTS ii

I. INTRODUCTION

II. LITERATURE REVIEW

2.1 History of NBA Draft and Collective Bargaining Agreement

2.2 Human Capital and Option Value Models

2.3 Draft Picks and Market Efficiency

2.4 Escalation of Commitment

III. THEORY

IV. DATA AND METHODOLOGY

V. RESULTS

VI. CONCLUSION

3 ABSTRACT

Statistics and performance metrics of the NBA afford analysis of draft position value. This paper compares college performance metrics to NBA productivity over a three year period. This paper also compares draft position to NBA productivity over a three year period. It considers how predictive draft position is of professional play, and whether the Collective Bargaining Agreement (CBA) prescribed salaries for the first round draft picks is efficiently distributed. The results did show a correlation between draft pick order and NBA productivity (less productivity as draft number increased), but it was not as strong as the correlation between college performance and NBA productivity. And, the decrease in CBA salary (from draft position 1 to 30) was steeper than the decrease in NBA productivity (from draft position 1 to 30) on average. As found in other sports leagues, namely the NFL, there is a tendency to overvalue the earliest draft picks of the first round. Results suggest that the draft picks in the middle and end of the first round are a better bargain than the early picks. This study suggests that the current CBA pay structure basing rookies’ pay on draft position might be improved and more efficient using other metrics.

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I. INTRODUCTION

The National Association (NBA), to many Americans and foreigners alike, is one of the most exciting sports to watch, in large part due to the exceptional athletes that comprise the league. It is is one of the fastest growing professional sports leagues in the world over the last 5 years, with great growth potential in the near future. From an economic perspective, the NBA functions as a basic labor market, where the players provide their talents and their compensation is based on their prior NBA performance.1 This study however concentrates on the collegiate draft market; the value of the players selected in relation to their pay as dictated by the NBA Collective Bargaining Agreement2 (CBA). Economic principles of market efficiency, overconfidence and escalation of commitment are reviewed in this labor market, as well as the models of human capital and option value for early entrants to the NBA.

Although strategies regarding building teams may differ among the 30 NBA teams, all decision makers realize the importance of the draft and adding the best value player to their teams, especially in consideration of the strict salary cap. Every team faces a salary cap for the total team of fifteen players as set by the CBA. Currently that cap is set at $99 million3, and if a team exceeds this cap, it faces a substantial luxury tax. The annual draft affords teams the opportunity to amass talent at a relatively discounted price. The draft consists of two rounds of thirty picks per round, and barring trades, each team should get 2 picks during the draft.

Specifics of the NBA draft process will be discussed in section 2.

1 http://www.cbafaq.com/salarycap.htm 2 https://www.nba.com/media/CBA101.pdf 3 http://www.nba.com/article/2017/07/01/nba-salary-cap-set-2017-18-season-99093-million

5 With approximately one third of the 2017 season complete, the disparity of pay and value can easily be seen by just looking at two players on the same team. , the second player selected in the draft and being paid approximately $6.85 million, is averaging 33 minutes,

9.1 PPG (Points per game), 7.2 assists, is making 33.2 % of his shots,. His teammate, Kyle

Kuzma, selected 27th, by , but traded to the , will be paid $1.51 million. Kuzma averages 30.4 minutes, 16.3 points, 1.5 assists, is making 49.1% of his shots. As the season has gone on, the two players values have started to converge, while Kuzma has gained overall value and has played far more games than Ball. Their values to the Lakers are similar enough, but their pay for two equally inexperienced players is vastly different. To date, Kuzma is the much better value.

Though first introduced in baseball, analytics have made their way into the NBA, and are now an integral part of the draft selection process. The intuition of college scouts has yielded more and more to performance metrics, so decision makers can more confidently and directly compare potential draftees. This paper considers various collegiate performance metrics and

NBA performance metrics relative to a players draft position. Research supports that the greatest predictor of a pick’s NBA value is their collegiate efficiency rating.4 Efficiency rating will be described in section 2.3.

For purposes of this study, only first round draft picks, places 1- 30 were considered.

Data was collected for 3 consecutive drafts starting with the 2012/2013 season, and includes college performance metrics and 3 years of NBA performance metrics for each of the draftees.

This paper compares college performance to NBA performance, and compares draft positions

(and corresponding CBA directed salaries) to NBA performance to inform the value of draft

4Stephenson, Jordana C. “The Transfer of Talent: How to Predict Success in the NBA.”Colorado College, 2012, pp. 1–40.

6 positions. The current CBA is effective through the 2021 season, and will be renegotiated between the NBA and the NBA Player Association (NBPA). A new CBA could include a rookie pay scale with a more efficient distribution.

II. LITERATURE REVIEW

The purpose of this section is to review and consider previous research and literature relative to the NBA draft and rookie compensation. Relevant data, including the efficiency formula developed by the NBA, methodology and data will be contained in section IV, and the results and conclusion will follow in sections V & VI respectively.

There is a wide variety of literature and information on evaluating NBA draft prospects and how to predict their NBA performance, based on their collegiate performance and other relevant data. However, there is a deficit of literature as to whether the CBA efficiently compensates NBA draftees. The CBA provides the pay scale for rookies based on draft position: the earlier a player is drafted, the more he is paid. The only amount subject to negotiation is that the rookie can sign for as little as 80% or as much as 120% of the scale pay. The CBA pay scale range between first and last draft picks is significant. For example, within the first round of 2017 draft picks, a two year salary of $15,366,120 went to #1 pick , compared to the

#30 pick, Utah’s (traded to Lakers), garnering a $3,049,680 salary for two years. 5

Draft position 1 earns 5 times draft position 30.

An NBA team’s draft position is determined by its win/loss record from the preceding regular season. The team with the worst record receives the first pick and the draft order is inverse to each team’s win/loss record with small variations among the bottom 12 teams to

5forbes.com/sites/jasonbelzer/2017/06/23/2017-nba-draft-1st-round-rookie-salary-projections/

7 determine the specific order in which they pick.6 The NBA draft lottery also determines the specific order of the first 8 picks by a weighted average selection.

The field is comparatively large from which each team selects its first round draftee. The

NBA is a relatively small league and has far fewer players than are in Division I NCAA collegiate basketball. Only 1.4% of Division I players are drafted each year.7 Only 60 players are drafted each year, and in most recent years, approximately only 40 of those players will ever get to play in an NBA game. In a draft as limited as the NBA’s, teams devote considerable resources to evaluate these collegiate and foreign prospects.

2.1 History of NBA Draft and Collective Bargaining Agreement

To understand the relevant literature it should be viewed historically, as the NBA draft market has changed over the years along with NBA/CBA requirements. In 1971 the Supreme

Court ruled in favor of Spencer Haywood, and removed the NBA requirement of players waiting to enter the NBA draft until their college class graduated. In 1975, soon after Moses Malone chose to go the NBA’s smaller rival, ABA in 1974, the NBA dropped its criteria of “hardship” for players seeking early entrance. Currently the NBA requires players be one-year removed from high school graduation and be at least 19 years-old.

Salary caps were introduced in the 1983 CBA, and this “led to some inequities in rookie salaries.”8 Following dissatisfaction from the veteran players at the possibility of untried rookies being paid more than them, the rookie pay scale was introduced in the 1995 CBA, and the number of players seeking early entrance into the NBA quickly grew. With limited and lower salary prescribed for the first 3 years of their CBA, players wanted to start early to avoid further

6 https://www.sbnation.com/nba/2017/5/16/15641686/nba-draft-lottery-2017-rules-how-works 7 https://thepowerrank.com/2013/03/29/nature-vs-nurture-the-odds-of-playing-college-basketball/ 8 Groothius, Hill and Perri

8 delay of more lucrative salaries. With common use of early entrance in the NBA, the influencing labor market forces of human capital and option value models need consideration.

Currently there is a 2 year guaranteed period, with 2 additional one year option periods, wherein a player can be limited to the rookie prescribed salary.

2.2 Early Entry - Human Capital and Option Value Models

Why does early entry occur in the NBA? The NBA’s emphasis on and concentration of young players is unique to their league and less common in other major league sports. In baseball, almost all top prospects are selected directly out of high school, but there is an extensive “farm league” process that was created to foster their skills and prepare them for the next level of play. MLB players may spend as many as 8 years in their team’s farm league system, being developed and groomed for the next level of play. Such a learning curve and time allowance does not exist in the NBA. Players selected with one of the first 15 picks are often expected to have an immediate positive impact on their teams. Most top American high school basketball players intend to play a single collegiate season and then move into the NBA. For such early entrants with only 1 year of college training, teams often view their first year in the

NBA as the training period and are willing to risk them underperforming in their first season or two in order to reap a greater return in the future.

The NBA does have an equivalent to the minor leagues in Major League Baseball, which is now called the NBA G-League* (formerly known as the D-League), where players who cannot make the final 15-man active roster are relegated to train and become more polished performers before they become part of the active roster of the NBA team that drafted them. The league has recently implemented two-way contracts that are non-guaranteed for their time in the

9 NBA and stipulates that the player will play in both the G-league and in the NBA for the team that owns their rights.

Gary Becker, a renowned economist, first argued that employers will pay for general training and that the human capital model usually involves two periods: a first period in which the cost of training these employees (future NBA players) will exceed the marginal revenue product (MRP) (what the players will yield in terms of wins for their team), and a second period, where the wage paid to the employee will be less than their expected MRP. With a restricted rookie salary for up to 4 years, teams exercising the 3rd and 4th year options expect to reap great value from players during this period.

The human capital model provides that as skill sets vary between players, the entry time will likewise vary: the more skilled or talented players will require less experience than less talented players. Potential draftees who have not shown early talent will likely need to stay in college longer to provide signals for teams to consider.

There is a secondary theory that applies to the draft market called the early entry - option value, which posits that risky workers are preferred to safe ones at a given wage, because the risky workers have an upside option value. Firms are willing to hire risky workers if they can dismiss those risky workers who do not live up to their expectations at minimal to no extra cost.

In the NBA, this explains the rationale for using higher draft picks on younger, riskier prospects for the potential of a greater reward.

2.3 Draft Picks and Market Efficiency

The most known and referenced literature on the issue of market value of draft picks was written in 2005 by Massey and Thaler on the NFL: “The Loser’s Curse: Overconfidence vs.

10 Market Efficiency in the National Football League Draft.” This study analyzed the NFL annual draft, including psychological factors, and determined that teams tended to overvalue the “right to choose” in the draft, and that top draft picks are valued in excess of market efficiencies. The study also wisely considered trades as part of their economic analysis, as trades in aggregate, reveal market value of draft picks.9 Massey and Thaler compared “market values to the surplus value (to the team) of the players chosen with the draft picks. “[They defined] surplus value as the player’s performance value - estimated from the labor market for NFL veteran - less compensation.”10

The Massey and Thaler study is useful to explain uncertainty and future (premium) value of draft picks to teams. They highlight the example of the 2004 Draft and the coveted first pick for Eli Manning. The San Diego Chargers had the rights to the first pick, and it was known by all close to the NFL that they intended on drafting Eli Manning, but there was one problem:

Manning had no intentions of playing for the Chargers, whether they drafted him or not. It was also known by those covering the league that the New York Giants also desperately wanted Eli

Manning and had him as their number one quarterback over Philip Rivers and Ben

Roethlisberger. The Chargers still drafted Eli Manning and traded with the Giants, who drafted

Philip Rivers with the 4th pick and sent their third-round (65th) pick in 2004 and their first- and fifth-round picks in 2005 in order for the Chargers to make the trade. The Giants sacrificed a lot in order to get the QB they coveted and as we know, no selection is a sure thing.

9 Massey, Cade, and Richard H. Thaler. “The Loser's Curse: Overconfidence vs. Market Efficiency in the National Football League Draft.” SSRN Electronic Journal, 2 Apr. 2005, pp. 1–59., doi:10.2139/ssrn.697121. 10 Massey, Cade, and Richard H. Thaler. “The Loser's Curse: Overconfidence vs. Market Efficiency in the National Football League Draft.” SSRN Electronic Journal, 2 Apr. 2005, pp. 1–59., doi:10.2139/ssrn.697121.

11 There is a common finding in sports economics literature that the number of points scored per game by a player throughout college career is the most significant basketball statistic.11 Though less predictive, statistics such as FG% or 3FG%12 will often inform NBA teams’ of players’ quality, performance and skill. Upon further investigation through others’ regressions, it is clear that scoring alone is not the only factor that is valued in a player’s overall productivity, but was the only factor consistently found to be correlated with NBA player evaluation.13

A player’s draft number has a significant, positive effect on their “hazard” rate for being traded. There are many websites and databases that run analyses and create valuation indices of all the players in the NBA, but very few account for the value of holding the rights to a certain pick in the draft and what the relative value of each draft position might be based on several years’ worth of performance metrics of players drafted.14 Barzilai In “Assessing the Relative

Value of Draft Position in the NBA Draft” attempted to do just that. Similarly, in an undergraduate thesis, NBA performance metrics were used to analyze the relative value of draft position in the NBA.15

The primary literature on professional drafts and NBA free agency does not discuss the impact of foreign players on the NBA draft and valuation of players by teams. Akira Motomura, a professor at Stonehill College, recently published a paper on the relative economic value of drafting international players in the NBA. His research showed that NBA teams didn’t start

11 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3796832/, Game Indicators Determining Sports Performance in the NBA 12 Percentage= Total shots made/ total shots, 3FG%= 3-point shots made/ 3-point shots 13 Berri, Brook, and Schmidt (2007). “Does One Simply Need to Score to Score?”International Journal of Sport Finance, 2, n4; (November): 190-205. 14 http://www.82games.com/barzilai1.htm, Barzilai, 2007 15 Watave, Adhiraj. “Relative Value of Draft Position in the NBA.” UC Berkeley, University of California, Berkeley, 2016, pp. 1–21

12 drafting international players regularly until 1996. His question essentially addressed whether teams value international prospects accurately. International players earned salaries in the 1996-

97 season and 1997-98 NBA seasons that were roughly twice as high as they should have been after accounting for their performance statistics in the previous season. The influx of foreign players in the NBA draft limits the number of players, whose college efficiency ratings can be properly observed.

The NBA came up with an efficiency rating in 2014 called PIE, which stands for Player

Impact Estimator and by current standards of NBA “SabreMetrics” is considered the holy grail of performance metrics. According to Stephenson’s research in “The Transfer of Talent: How to

Predict Success in the NBA”, a one point increase in college efficiency will equal a .5400457 increase in NBA efficiency.16 This can be restated as every point of collegiate basketball efficiency results in an approxmate .54 unit incresase in NBA efficiency. The variables used in their regression were: NBA Efficiency Rating (REFF), which was the average efficiency rating of the first three years of a player’s NBA career, college efficiency rating, points, rebounds, assists and steals positively, field goal attempts, field goal percentage, etc. Stephenson’s model was a simple OLS model that attempted to show the relationship between a player’s NBA efficiency as the dependent variable and College Efficiency, with other College performance dummy variables such as Final4 and BigConference. Her

2.4 Escalation of Commitment

16 Stephenson, Jordana C. “The Transfer of Talent: How to Predict Success in the NBA.”Colorado College, 2012, pp. 1–40.

13 The final economic concepts to consider are escalation of commitment and sunk-cost effect:: In Staw and Hoang’s 1995 study,17 they were used to evaluate the NBA draft. Staw and

Hoang pointed out that although draft order was not a perfect indicator of performance, it was often a predictor of player’s court time, finding even with poor performance the highly drafted player often received more play time since large commitments had already been made. Camerer later re-examined Staw & Hoang’s data, for alternate explanations to escalation, but confirmed escalation, albeit weaker.18 Staw and Hoang explored the idea that draft order is an indication of the expected future performance or skill level of a given player. The same player’s performance during a season is then an additional signal of inherent quality.

In economics the sunk cost effect is considered relative to firms acting irrationally to keep an investment, or retain an employee, longer than they should based on an analysis of that investment’s or employee’s additional marginal value compared to their marginal cost. Staw and

Hoang tested whether the amount teams spent for players in the NBA influenced how much playing time players got and how long they stayed with NBA franchises, with results that confirmed their hypothesis.

III. THEORY

Market efficiency is the most important economic theory in the NBA draft market.

Evaluating the current rookie salary structure in the NBA to improve and maximize market efficiency is beneficial to the teams and league. Every NBA team has access to the relevant

17 Staw, Barry M., and Ha Hoang. “Sunk Costs in the NBA: Why Draft Order Affects Playing Time and Survival in Professional Basketball.” Administrative Science Quarterly, vol. 40, no. 3, 1995, pp. 474–494., doi:10.2307/2393794.

18 Camerer, Colin F., and Roberto A. Weber. “The Econometrics and Behavioral Economics of Escalation of Commitment: a Re-Examination of Staw and Hoang NBA Data.” Journal of Economic Behavior & Organization, vol. 39, no. 1, 1999, pp. 59–82., doi:10.1016/s0167-2681(99)00026-8.

14 statistical information on prospective draftees. In this era, no NBA team has a material advantage over its competitors with regards to information about potential draftees performances. Although there will be some differences in the qualities most valued in a player, points scored will always be highly valued. This paper considers the predictive value of college performance metrics to advanced NBA metrics, and draft positions/corresponding salaries to advanced NBA metrics, to analyze the value of draft positions and consider the efficiency of rookie pay.

Professional sports leagues use different means to limit the cost burden of training on the teams, and where the NBA used to rely on college for training, currently training takes place on

NBA teams under limited rookie salaries. As discussed in section 2.2 above, the economic principles of human capital model and option value inform the early entrance in the current draft.

In “Labour Markets in Professional Sports” Rosen and Sanderson noted that the

“important elements of supply and demand are starkly observable in professional sports.”

However, some CBA requirements such as salary caps, and reverse-order draft impact this labor market, by redistributing money from talented players to owners and rewarding loss. The implication of reverse-order drafts is considered as a negative externality from excess incentives to win. 19

With strict salary caps, a roster is a limited resource, and NBA teams aim to generate the most production and talent from this limited resource. With an efficient market, teams will need to balance their roster of highly paid seasoned stars with relatively low cost draftees.

19 Rosen, Sherwin, and Allen Sanderson. “Labor Markets in Professional Sports.” 2000, doi:10.3386/w7573.

15 IV. DATA & METHODOLOGY

This study compares a) the NBA rookie salary scale for players drafted in the 2012/13,

2013/14 and 2014/15 drafts to their NBA performance, and b) collegiate performance to NBA performance, to measure the efficiency of the NBA rookie labor market.The data generated as part of this exercise provides the observation of whether NBA draft order or collegiate performance, as the determinant of compensation, would yield a more efficient NBA rookie contract labor market. This study compares two different models: the first uses the NBA efficiency rating as a function of a player’s draft position, tenure in the league, number of years in college vs a regression using a player’s college efficiency, as well as other character factors that might affect performance. Measuring efficiency in the NBA is not particularly straightforward because there is not one measurement for NBA performance that is uniformly accepted as the best or most reliable.

Data was collected from NBA and college statistics on the players drafted in the first round from the 2012-2014 NBA drafts and their performance during the first three years of their

NBA careers, 2012-13 through the the 2016-17 season for all three draft classes. Basketball- reference.com was the source for the college and professional statistics used herein. The data considered includes: points, rebounds, assists, turnovers, minutes played, in a given season.

These years of data were selected because they apply to the most recent group of NBA players who have played a minimum of three years in the NBA under a rookie contract. A similar efficiency statistic was calculated for both college and professional performance data, but unlike the statistics found for pro performance, for which there were three years of data, only one year of collegiate data was used in the analysis of each draftees’ collegiate performance. For the time period analyzed, top players play collegiate basketball for one year; the

16 minimum required to be draft eligible. There are exceptions to this scenario, but not many. All information on the rookie-scale salaries for the 2012-14 rookie draft classes was obtained from cbafaq.com and basketball-reference.com. The NBA’s CBA specifies how much rookies will be paid through the first four years of his career.

This study used these 90 players, The first 30 players selected in each of three draft years,

2012, 2013 and 2014, to compare their professional performance (NBA PER), against the relative pay of these 90 rookies. When referred to as NBA PER, this is not an exact player efficiency rating but rather a very close proxy and a more broad efficiency rating used before

PER was widely accepted as the reference point stat for efficiency. My efficiency rating was calculated as: (PTS + REBS + ASTS + STLS + BLKS) - [(FGA-FG) + (FTA-FT) -

TURNOVERS]

Figure 1 is a graph that presents rookie pay for the first 30 draft positions plotted against their NBA PER.

Figure 1: NBA Efficiency vs Pick

*Series1= Salary Pick 1-30, **Series2=NBA PER by Pick

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The beta-value on the explanatory variable, NBAEff, is .385 in the draft order model. The p-stat for this beta was 0.001, which shows significance at the highest level.

This study also compared the collegiate PER, or efficiency of first thirty players drafted in the 2012-2014 drafts to their NBA PER (2012-13 - 2016-17 seasons ). The results of this comparison are displayed in Figure 2 below.

Figure 2 - College PER v.NBA PER

The beta coefficient of the College PER to NBA PER is -.573. This was computed as a negative number because of the inverse correlation to declining draft order (every decrease in draft

18 position/ increase in pick # is negatively correlated to NBA performance by -.573). This result is similar to Stephenson’s .548 Correlation Coefficient.

V. RESULTS

The data and computations of this study demonstrate that college efficiency rating is more predictive of NBA performance than draft order. Although the two results cannot be compared directly because they are measuring different values, the R^2 value of the college efficiency model (colleff) as compared to NBA PER is significantly higher than that of the draft order model.

This study considers only NBA performance to determine the value of a rookie. It does not weigh other factors that NBA teams may consider when deciding which eligible collegiate player to draft. Such factors could include, name recognition or idiosyncratic team needs. A team could benefit most from idiosyncratic performance factors, that are different from those valued in the most widely accepted collegiate efficiency matrix. Such unique team needs could cause a player with lower collegiate efficiency to have greater value to a team than a different collegiate player whose higher college efficiency would not create equal team performance improvement.

The results from the two regressions were mostly in line with expectations of the relationships between the dependent and key independent variables.The analyses showed NBA efficiency to be strongly inversely correlated with NBA draft pick, with a correlation coefficient of -0.359 and a P-value of 0, indicating that controlling for all other factors, this has a very strong impact on the outcome of NBA Efficiency. As expected, it shows that player’s draft position have a very strong impact on their Professional Efficiency’s over the first three years of

19 their careers. In a second version of this model, minutes played was included as another control

variable and it made pick no longer significant. While pick became insignificant, minutes played

was now significant, but that was due to endogeneity between those two variables. Here are

those results:

NBA Efficiency vs Pick

NBA_Efficiency Coefficient Robust Standard t-statistic p-statistic(

Error significant at

5%level) pick -0.358 0.0821 -4.37 0.00 final-four -1.32 1.805 -0.73 0.467 coll_title 10.64 3.14 3.39 0.001 bigConf 1.02 1.496 0.68 0.499

International Dropped Dropped Dropped Dropped

(collinearity) (collinearity) (collinearity) (collinearity)

SchoolCode 0.026 0.569 0.45 0.655

CollYears_Dummy -0.356 1.61 -0.22 0.8

20 NBAEff Coefficient Standard Confidence P-Stat Error Interval

College_Eff 0.679 0.194 (.289, 1.07) 0.001

Final_Four 0.052 0.063 (-6.13, 1.87) 0.411

Coll_Title

Big_Conf 3.21 1.68 (-.156, 6.60) 0.061

International dropped dropped dropped dropped

CollYrs_Du 4.83 1.66 (3.23, 6.49) mmy

In the second regression, College Efficiency was more strongly correlated with NBA

Efficiency, with a correlation coefficient of .679 and a p-value of .001, with the same other exogenous control variables as the first regression. This result shows that while pick was an important determinant of these draftees’ NBA Efficiencies, College Efficiency was a stronger overall indicator of the professional performance of players. The R-squared value of the model that regresses NBA Efficiency against College Efficiency is 0.7572 as opposed to the much lower

R-squared value of the model that compares NBA Efficiency against Pick variable is 0.5754.

These results indicate that rookie-scale compensation based on college efficiency could yield a more efficient NBA rookie labor market than the present NBA rookie labor market, which is predicated on draft order.

VI. CONCLUSION

21 This study analyzed the relationship between draft order and NBA performance and compared this to the relationship between college performance and NBA performance, with an eye toward determining which is most predictive of NBA performance, and what might make

CBA pay most efficient.

Given the steep decline in CBA pay throughout the 1-30 draft positions of the first round

(example of 2017 pick #1 5 times the salary of pick #30), and the less steep decline in performance metrics for those positions, the CBA might be more efficient if the salary range was less steep, or incorporated some other metrics to determine salary in addition to draft position.

The results indicate that a rookie compensation scale based on college efficiency could yield a more efficient NBA rookie labor market than the present market predicated on draft order.

Alternatively, if draft order remains controlling, the salary decline between positions should be softened. The initial precipitous salary decline of positions 1-10 is steeper than the decline in performance for the same positions. For example, the average NBA PER of picks 4 and 5 (11.3 and 7.2 respectively) is similar to that of Pick 14 (9.9), which sits in the middle of the largest performance/salary gap in Figure 1. The average college PER for the example picks are also comparable, 15.7, 19.7 and 18.4, for picks 4,5, and 14 respectively. However, pick 4 garners twice the salary of pick 14. A notable comparison example is of two UNC players: John Henson, pick 14 in 2012 earned an salary of 1.5 million, and Cody Zeller, pick 4 in 2013, a salary of 3.2 million. With similar college PERs (Zeller 19.7 and Henson 19.4) and similar NBA PERs (Zeller averaging 10.8 and Henson 11.2) the salary difference highlights the inefficient market.

Given the current CBA rookie pay scale, the results also shed light on the relative value of the various draft positions 1-30. This information could inform draft selections, including trading strategy. In cases where the NBA performance corresponding to the draft position far

22 exceeds the rookie pay, there is a greater surplus benefit to the team. The results showed the greatest surplus from positions 9 through the 30, with a few exceptions. However, the earliest picks do show the highest NBA performance, and if the team has the money to spend, the early picks could provide the team the talent it needs.

23

Works Cited

http://www.espn.com/nba/story/_/id/17714126/american-basketball-players-

share-tales-really-play-china

Hendricks, Wallace E., et al. “Uncertainty, Hiring and Subsequent Performance: The

NFL Draft.” Journal of Labor Economics, vol. 21, no. 4, 11 Aug. 2001, pp. 1–30., doi:10.2139/ssrn.289265.

Groothuis, Peter A., et al. “Early Entry in the NBA Draft.” Journal of Sports Economics, vol. 8, no. 3, June 2007, pp. 223–243., doi:10.1177/1527002505281228.

Staw, Barry M., and Ha Hoang. “Sunk Costs in the NBA: Why Draft Order Affects

Playing Time and Survival in Professional Basketball.” Administrative Science Quarterly, vol.

40, no. 3, 1995, pp. 474–494., doi:10.2307/2393794.

Groothuis, Peter A., et al. “Early Entry in the NBA Draft.” Journal of Sports Economics, vol. 8, no. 3, 2007, pp. 223–243., doi:10.1177/1527002505281228.

Stephenson, Jordana C. “The Transfer of Talent: How to Predict Success in the NBA

.”Colorado College Economics Thesis, May 2012, pp. 1–43. CC Digital Library, digitalccbeta.coloradocollege.edu/pid/coccc:5931.

24 Berri, D. J., Brook, S. L., & Fenn, A. J. (2011). From college to the pros: predicting the NBA amateur player draft. Journal of Productivity Analysis, 35(1), 25-35.

Appendix

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Important Links:

https://www.sbnation.com/nba/2014/9/29/6152821/nba-rookie-contract-extension-

explainer http://www.82games.com/barzilai1.htm http://www.cbafaq.com/scale2011.htm http://www.espn.com/nba/columns/story?columnist=hollinger_john&id=2850240

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Appendix

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