Brain vs Bron; a study on what athletes learn in college and how it affects their NBA salary

By: Jason Tanz

Section I: Introduction

        July, less than a month after the college year ends many college athletes and prospects prepare for the beginning of the future. The NBA draft is a young athlete, and prospects best chance for achieving their dream of playing in the National basketball association, NBA. while being drafted to an NBA team gives the player a roster spot and a rookie contract, there is no guarantee for success.

Some scouts attempt to determine who will be the next Michael Jordan, Lebron James or Wilt Chamberlain, through advanced analytics, it may be that a prospects education is the best identifier of their ability to lead adapt and perform on the highest level. This is seen by the fact that currently in the 2018-2019 NBA season of the top 10 highest paid players this year, six went to two years of college, three were “One and Done” players, meaning they went to the minimum required number of years college to be eligible for the NBA draft, and only one the reigning NBA champion Stephen curry went to three years of college. Which raises the question, do more years of college for athletes increase their NBA salary.

Many disregard college education as a factor for success, looking more at PER or player efficiency rating, race, and draft position as the factors involving player's second contract earnings. Mary F. Howard-hamilton and Sherry K. Watt, authors of New directions For Student Services looks at what student-athletes might be learning in college and deduce that they are taught to cope with stress, work with other races and differentiating ideas, that allows them to mature emotionally and physically more in college than they would  the NBA, creating team players and leaders that sets them up for more success in their professional career.

While College may be the reason players are successful, many team executives look at a players analytical stats and attempt to associate that with their value to the team. This started in the 1988-89 season with John Hollinger, who created the PER formula and "sums up all a player's positive accomplishments, subtracts the negative accomplishments, and returns a per-minute rating of a player's performance". While PER does create a scale of players compared to the league average of a PER of 15, is not perfect at predicting players value. As the analytic stats based on players per minute efficiency and does not account for fatigue of players, rewarding hard work for one minute a game the same as hard work over an entire 48-minute game. In addition, it was important to account for a Players race as there is a stigma around minorities in the NBA as the league is around 75% black, as well as the negative association with lower drafted players. In the data, we incorporated PER, race and draft position f the 2008-2012 draft class, to control for player performance and demographics, as well as its effects on the increase of an NBA players second contract.

        In section II we will define the variables used in the regression model and how they were created, as well as historical research on such variables by a peer-reviewed author in section III. Then in section IV will be the regression model, results, statistical significance and figures depicting the data. Next section V will look into the causation between NBA salary and education as well as the significance of the control's variables. Then section VI will conclude the paper and discuss further research as well as policy suggestions for the league going forward. Lastly, in section VII will be the appendix with the exact data tables used in research as well as several figures depicting the data and results and finally the sources used in research.

II. Important Variables and Data collection

        When it comes to NBA players college education when related to their second contract salary there are several variables to control for and look at. First, when it comes to the post-high school education of NBA players we are gathering data from the college and admissions and draft date of the player to determine how many years of education the player had from zero to four years of education, zero representing players that did not attend college but spent one year removed from high school training for the NBA outside of collegiate universities. We then will be looking at the 2008-2012 draft class and associate each player drafted with their years of college education from the value of zero to four, since there are no postgraduate players who have been drafted to the NBA.

To calculate the salary of each player we will be looking at the second contract of NBA players, as to control for those who have been in the league longer or those playing for multiple teams as those who stay on the same team for 5 years are eligible for a “supermax deal” worth more than those traded or signed in free agency. Looking at the second contract a player receives allows us to control for selection bias as we can then associate players that did not receive a second contract with a salary of zero for each draft class, while just looking at a present salary from ESPN.com does not incorporate players not resigned with a zero salary. Looking at players second salary also accounts for older players who have “past their prime”, such as Carmelo Anthony who used to be a superstar but currently can't break into a teams rotation.

When choosing players to associate in our data set we chose players from the 2008 draft class to the 2012 draft class, giving us 300 individual samples as there are 60 players drafted each year. This allows us to look at players who were drafted after the one and done rule was implemented, not allowing high school players to go straight to the NBA draft as to not skew the results. In addition, the cut off for players was the 2012 draft class as it allows the players to fulfill their two-year rookie contracts and have the chance to then sign a second contract, if we look at more recent draft classes it would skew the results based on the players immaturity in the league and the increasing salary cap. Lastly, when acquiring data we did not want to look too far back as the NBA team salary cap has increased over the last few years, which allow newer players to sign larger and larger contracts. Taking the players drafted in 2008-2012 means that they would sign their second contract in 2010-2014 where the NBA team salary cap remained around at $58 million, allowing second contracts signed to stay relatively constant, as the current NBA Salary cap is $99 million and set to increase for the 2020 season, as seen In Figure 1.

III. What college does for Athletes

        The NBA is focused on improving the talent in the league, so that they can market it more and increase the leagues value, as the driving force behind American Business such as the NBA in to generate wealth. In Order to accomplish this, the NBA enacted the “One and Done Rule” which changed draft eligibility of players, forcing them to be one year removed from high school, giving them an extra year to develop. This is looking at by H. Stawin his article The NBA Gets a College Education: An Antitrust and the Labor Analysis of the NBA's Minimum Age Limit 56 Case W. Res. L. Rev. 825 (2005-2006). Where he used a simulated business case to show that people responsible for a losing course of action will invest further than those not responsible for prior losses and returning to college giving athletes the chance to solve their problems, work on what caused them to fail and learn from it.  (Barry M. Staw and Ha Hoang). However, while more years college does help prospects with issues on and off the court it does hinder their ability show their worth.

While college athletes are taught skills they need to be successful they are hindered for staying in college as it reduces there draft stock and position in sed draft. This is looked at by Daniel A. Applegate where he determined based on people's commitment to the position, as they will reward players more based on draft order and can use their draft position as a significant predictor of minutes played over the first five years of an NBA layers career, shown in

Based on the data in Table 1. We see that those drafted lower have less opportunity to prove themselves on the court and therefore struggle to show their worth in the NBA and receive a second contract, which may associate why more second round picks are out of the league by the end of their rookie deals compared to first-round picks. However, others such as Mary E. Howard-Hamilton and Sherry K. Watt relate it to player performance and that those with education have the ability to show their value in the limited chance they receive.

In the book Student Services for Athletes by Mary E. Howard-Hamilton and Sherry K. Watt, they look at how colleges are designed to help produce the best individual possible through psychological and cognitive development. They argue that due to the fact that college athletes are exposed to a multiple of diverse situations and interactions with a more diverse group of peers set them up for success as they are more likely to be Intellectually, Mentally and Physically fit, which helps demonstrate leadership qualities and reinforces the idea of their increased value. The theory on why College Students learn more than those in a professional setting is that they have the whole team on coaches and trainers working to develop their star while NBA teams focus more on the monetary value players have and how they can increase their wealth rather than helping the “student of the game”.

IV. Data set

In Order to control for statistically significant variables, in the data set we had to associate a racial dummy variable to different players in the NBA to control for Racial bias, in the NBA for our regression analysis. We associated a 0 to black players, a 1 to white players, 2 to Hispanic players and 3 to Asian players; when running our regression analysis in order to determine the significance race has on an NBA players salary. Since the NBA is around 75% black, 21% white, 3% Hispanic and less than 1% Asian, it would allow us to see if it is race versus college education that increases NBA players salary.

        It is also important to record the draft position of each player and use it as a dummy variable in my regression and run and R squared analysis to determine if it is a significant variable. This would allow users to see if a higher placement in the draft is what drives NBA players salary. In  Barry M. Staw and Ha Hoang’s article, Sunk Costs in the NBA: Why Draft Order Affects Playing Time and Survival in Professional Basketball, they look at the effect draft position has on salary, and how those drafted higher are seen to be more likely to succeed and are given more leeway, for their mistakes as they are seen to still have potential, due to the stigma of being a top pick or even a number one overall pick. While it is known that draft position affects the player's salary we wish to see if this trend continues into the player's second contract, thus attempting to control for such selection bias.  

Lastly, It is important to record a player PER, player efficiency rating. This would allow us to determine if the salary is based on the performance of the player, based on analytics experts. With all of these controls and determining their statistical significance would allow us to tell what factors affect NBA players salaries, such as increased college education for players increases their performance, and knowledge of the game which results in a high second contract.

The data collected for the study was compiled in Table 2. And shown in a scatter plot of College education and wage shown in Figure 2. Such data was gathered from the 2008-2012 NBA draft class where we recorded draft position 1-60, wage of the players first year of their second salary, then generated ln(wage) to account for diminishing returns, PER to account for performance, and a players race of one to four, in the attempt to account for all statistically significant variables that affect a player's second contract.

V. Regression Model

        Based on the data compiled from the five NBA draft classes from 2008-2012, we ran a basic regression model where are dependent variable is a player second NBA contract salary, in the variable ln(wage), with independent variable being years of college education in the formula:

Equation 2. Regression Result

Ln(Wage) = .094(Education) - 1.188(Race) + .571(PER) - .145(Draft Position) + 8.949

Where each coefficient relates to how the correlated variable effects a players wage, positive associating an increase in salary and a negative coefficient reducing their second salary earrings.

VI. Data Analysis

        Based on the results of the regression it becomes apparent that NBA player salaries are based on several key factors. Looking at Table 3. We see that the main statistical factors affecting salary, in terms of ln(wage), related mainly to race. This is evident by the fact that the absolute value of the coefficient is the largest of those in the regression model.

                However, based on the standard error, t value and p-value none of the variables are statistically significant in a 95% confidence interval. Although based on the results, education does increase the average NBA players salary by .094 for each year of college. Although a Players salary is more impacted by their Race, Draft Position and PER. based on the distribution of players wage based on education depicted in Figure 2. We can see that education affects Players move from one year of college versus spending a year out of high school training. In addition, there is a larger distribution of salary when it comes to multiple years of college. While the top salary for players between different years of education remains around the same, there are more players with extremely low salaries the more years of a college education a player has. This could be explained by the fact that Talent effects a players contract since we see a larger impact an increased PER has on salary compared to College education, race seems to be the biggest impact of players salaries.

VII. Causation and Conclusions

        The United States economy and society is mainly capitalistic and therefore is centralized around the generation and creation of wealth. For NBA players success is seen in the same way, that better players receive higher contracts; as well as championships which tend to lead to an increased salary as well. While players based on our regression model are paid more based on Race. while going to at least one year of Colleg drastically increases a players earnings, going to more than one year of college does not increase there salary and slightly reduces it on average. While Mary E. Howard-Hamilton and Sherry K. Watt’s theory that college does help players mature and develop, there seems to be a cap on their ability and that pure talent in the form of PER allows for the best candidates for the job to rise to the top more significantly, and receive a higher wage. While PER is not the perfect way to test a players talents it is the most used analytics in the modern NBA and explains why players with the highest PER in the league have some of the highest salaries. In addition to talent, the race was the largest factor in effecting players salary, as the race dummy variable was set up based on the percent of players of that race in the NBA and being a minority gives you a larger dummy variable, from 1-4, and subsequently increased the negative effect on players salaries. This could be explained through racial bias from history. Teams are continually looking for the next Micheal Jordan, who was African American, and according Larsen, T., Price, J. & Wolfers, J. to in the article Racial Bias in the NBA: Implications in Betting Markets looks at the prejudice and racial bias in sports, how black players are favored to win in betting markets and may prove why race is the main factor affecting our results in the regression model. While it is race and PER are not correlated there is a stigma around minorities, and team executives may want to look into other cultures and races, such as Chinese players like Jeremy Lin who were never given the same opportunity, but shined none the less, to find the best talent for their team to help win championships and increase their wealth.

Appendix:

Table 1: Effects of Draft Position on Minutes Played for the Second through the Fifth Year Played

Source:

Figure 1. NBA Salary Cap per Team from 1984-2021

Source:

        Table 2. NBA Salary, Education and Controls for 2008-2012 Drafts