This study uses quantile regressions to evaluate salaries of NBA players in the 2001–2002 season separately for big men and guards. Quantile regression allows the measurement of the return to player attributes for guards and big men at different salary (skill) levels along the distribution of NBA salaries.

І. Introduction

The salaries of National Basketball Association (NBA) players constitute the primary component of costs of NBA franchise operation. Despite this, there is little theoretical or empirical groundings for the salary determining process of players. This analysis estimate players’ salaries, separating players into two groups: big men (forwards and centers in the League) and guards. Additionally, the study utilizes quantile regression procedures to estimate salaries of big men and guards. Quantile regression allows the measurement of the effect of performance attributes on players’ salaries at different points along the salary distribution. Indeed, the returns to attributes may also differ greatly by player position for low-salary, low-skill, bench-warmers on a team relative to high-salary, high-skill superstars.

II. Data and Results

Individual performance statistics of the 409 NBA players listed in the end of the 2001–2002 season rosters of the 29 NBA franchises were taken from the official website of the NBA. Performance statistics are utilized for games played during the regular season, excluding the preseason and playoffs.

Table 1 presents OLS estimation of earnings of NBA players in the 2001–2002 season. The coefficient on exp in column 1 reveals that each year of experience significantly increases earnings of NBA players by 36.8 percent. Findings of large returns to experience are expected given that the individual player salary cap is based on years of experience in the league (Staudohar, 1999 ). An increase of 1 assist per game significantly increases earnings by 7.4 percent. Additionally, an increase in average block shots of 1 per game results in significant earnings increase of 16.3 percent whereas, an increase in the average number of points scored per game by 1 point significantly increases salaries of NBA players by 4.4 percent. Earnings of blacks differ insignificantly from their white counterparts. These findings are consistent with recent evidence of no employer earnings discrimination of NBA players (Kahn, 2000). The findings for the coefficient on big-men reveal the NBA premium for centers and forwards from their size that is not a result of observed performance statistics. Big men significantly earn 18 percent more than guards, after accounting for other performance attributes.

Tables 2 and 3 present the results of the quantile regressions for guards and big men, respectively. The coefficient on exp is large and significant for guards at each skill level; however, the coefficients are much larger for players at intermediate skill levels relative to low-skill players and superstars at the tails of the distribution. Indeed, the returns to each year of NBA experience is 52, 51, and 53 percent for guards at the 25 th, 50 th, and 75 th quantiles, respectively — but, only 34 percent for low-skill players at the 15 th quantile and superstars at the 90 th quantile. This finding indicates greater returns to human capital accumulation for intermediate level, role players (occurring in the middle of the earnings distribution) relative to low-skill players and superstars. This could reflect team owners’ willingness to reward guards who are role players for doing the subtle things necessary to win that are not accounted for in performance statistics — for instance, diving on the floor to save a ball from going out of bounds.

Low-skill guards (15 th quantile) are unique in being significantly rewarded for steals (a 1 percent increase in steals per game increases their earnings by 62 percent) and discounted for rebounds (a 1 percent increase in rebounds per game decreases earnings by 13 percent). The negative returns to rebounding for low-skill guards may appear odd at first glance; however, in the transition from offense to defense, one of the primary responsibilities of guards is to be the first players back on defense. Thus, guards prevent the other team from scoring fast-break points. If guards remain on their offensive end of the court for rebounds, the opposing team is likely to score easy fast-break points. Moreover, low-skill guards’ decreased returns to rebounding may indirectly capture the negative effects for rebounding and not getting back on defense when the opposing team fast-breaks. Furthermore, the negative effect diminishes as skill level increases — possibly indicating that higher skilled players are more athletic or have superior decision-making abilities, which allows them to discern the opportune times to remain on the offensive end for rebounds versus getting back on defense. Scoring points is significantly rewarding for guards at all levels of the earnings distribution.

Table 3 presents the regression results for the quantile earnings estimates for big men. Similar to guards, the returns to each season of NBA experience for big men is greater for players at intermediate skill levels relative to low-skill players and superstars at the tails of the distribution. Additionally, blocking shots is a very lucrative attribute for intermediate and high-skill big men, but not for their lower skill counterparts—medium- and high-skill big men receive a significant premium (between 15.6 and 25.7 percent for a 1 percent increase in blocks per game) but the returns for low-skill big men are smaller in magnitude and insignificant. In contrast, low-skill big men receive the highest returns to rebounding (12.6 percent) — with the returns to rebounding declining in magnitude and significance as skill level increases (a significant increase of 10.2 and 7.4 percent increase at the 25 th and 50 th quantiles, respectively, and insignificantly increasing at higher skill levels). Low-skill big men (in the 15 th quantile) are unique as the only big men who are significantly rewarded for assists or penalized for increased average steals. A 1 percent increase in average assists increases their earnings by 16.6 percent. However, a 1 percent increase in average steals reduces their earnings by 10.4 percent. A possible explanation for low-skill big men’s salary discount for steals is that in a half-court defense, big men are often positioned near the basket — constituting the last line of defense in preventing easy lay-ups for the opposing team. If a big man goes for a steal (rather than staying in between the basket and the offensive player) the big man may be unsuccessful in stealing the ball — leaving the offensive player near the basket for an uncontested lay-up. Moreover, the low-skill, big-men discount for steals may reflect the negative effects of going for steals, but failing to steal the ball. Also, the negative effects diminish as skill level increases, possibly indicating that higher skilled big men are more athletic or have better decision-making abilities which allow them to discern the opportune times to go for steals versus remaining in defensive position (between the offensive player and the basket).

IV. Conclusion

We utilize quantile regressions to examine salaries of NBA players — allowing for a different distribution of earnings for big men and guards. Our findings provide further insights into the salary structure of NBA players — as players with a range of skills and playing different positions receive significantly different salary rewards and penalties for performance attributes.


  1. Dupree, David. “2001–02 NBA Salaries” USA Today, (November, 15, 2001).
  2. Hamilton, Barton H. “Racial Discrimination and Professional Basketball Salaries in the 1990s.” Applied Economics 29 (March 1997): 287–96.
  3. Kahn, Lawrence M. “The Sports Business as a Labor Market Laboratory.” Journal of Economic Perspectives 14 (Summer 2000): 75–94.
  4. Koenker, Roger and Gilbert Bassett. “Regression Quantiles.” Econometrica 46 (January 1978): 33-50.
  5. Nance, Roscoe. “10-Year Vets Bide Time on Market.” USA Today, (July 31, 2003).
  6. Staudohar, Paul. “Labor Relations in Basketball: The Lockout of 1998–99.” Monthly Labor Review 122 (April 1999): 3–9.

 Table 1

Salaries of NBA Players in 2001–2002 Season

Variables Coefficient t-statistic
Constant -1.282*** (-6.74)
Exp 0.313 *** (10.93)
Exp 2 -0.014** (-7.83)
Assists 0.071** (2.43)
Blocks 0.151 *** (3.63)
Steals 0.052 (0.94)
Rebounds 0.062 *** (2.68)
Pts/Game 0.043 *** (4.09 )
Free throw 0.244 (1.11)
Black -0.010 (-0.12)
Big-man Sample size 0.188*409 (1.88)
R ^ 2Adj- R^ 2 .602.593

Note: * (**, ***) denotes significance at the .10 (.05, .01) level.


Table 2

Salaries of Guards in the NBA in the 2001–2002 Season

Variable 15 th 25 th 50 th 75 th 90 th
Constant -1.698***(-5.25) -1.499***(-3.48) -1.334***(-3.80) -1.502***(-3.18) -1.032***(-2.99)
Exp .29409***(4.76) .4221***(6.51) .4157***(6.90) .4306***(4.87) .2933***(5.07)
Exp 2 -.0151***(-4.24) -.0220***(-5.98) -.0190***(-5.16) -0.0191***(-3.50) -.0117***(-3.75)
Assists .0442(0.99) .0196(0.36) .0409(0.92) .0539(0.81) .0433(0.90)
Blocks .1016(1.22) .1201(1.41) .1553**(2.20) .1894**(2.01) -.0310(-0.46)
Steals .4859**(2.31) .3570(1.54) -.0096(-0.04) .0132(0.04) .0972(0.44)
Rebounds -.1458*(-1.69) -.0530(-0.57) -.0267(-0.31) .0229(0.18) .0613(0.79)
Points/game .05452***(2.71) .0549***(2.60) .0745***(3.51) .0407**(2.17) .0468**(2.33)
Free throw .2081(0.71) -.0577(-0.13) .1919(0.47) .9472*(1.78) .3756(0.92)
Black -.1205(-0.61) -.1637(-0.63) -.1927(-0.93) -.0164(-0.06) .1667(0.78)

Note: t-statistics are in parenthesis, * (**, ***) denotes significance at the .10 (.05, .01) level.

Table 3

Salaries of Big Men in the NBA in the 2001–2002 Season

Variable 15 th 25 th 50 th 75 th 90 th
Constant -1.682***(-5.80) -1.756***(-6.60) -1.149***(-3.76) -.1874(-0.75) .4952*(1.79)
Exp .2360***(4.54) .2947***(6.01) .3255***(6.59) .3027***(6.87) .2440***(5.66)
Exp 2 -.0090***(-2.77) -.0133***(-4.36) -.01479(-4.61) -.0129***(-4.48) -.0089***(-3.45)
Assists .1534*(1.78) .1143(1.38) .0011(0.01) .0736(1.01) .0417(0.42)
Blocks .1221(1.30) .1161(1.38) .2289***(2.75) .1453**(2.03) .2267***(3.44)
Steals -.1094*(-1.83) -.0637(-1.01) .0456(1.28) .0447(1.48) .0137(0.42)
Rebounds .1186***(2.99) .0979**(2.51) .0720*(1.94) .0242(0.87) .0024(0.09)
Points/game .0453**(2.09) .0407*(1.83) .0490**(2.29) .0294*(1.86) .0281*(1.75)
Free throw .0093(0.02) .3001(0.79) .3276(0.85) -.0313(-0.10) .2718(0.75)
Black -.14579(-0.92) .0602(0.43) -.0425(-0.33) .1155(1.02) -.0967(-0.82)

Note: t-statistics are in parenthesis, * (**, ***) denotes significance at the .10 (.05, .01) level.



See Koenker and Bassett (1978) for a review of this procedure.

During the 2001–2002 NBA season there were 29 teams. Since then, one team has changed locations, the Charlotte Hornets moved to New Orleans and, another, the Charlotte Bobcats, was added in the 2004–2005 season.

Data for this analysis were obtained from http://www.nba.com, last viewed April 30, 2004.

The marginal impact of a characteristic on the salaries of the group in question is found by taking the exponential of the estimated coefficient minus one and multiplying by 100.

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