By Cody Barbuto
Abstract
With the rise in the National Basketball Association (NBA) salary cap, there are the potential for substantial change in the NBA labor market. This study tests for this change by looking at the change in allocation of salary cap dollars across each member of every NBA roster. Data was collected from the years 2010-2017, with salary, position, and performance statistics for each player. To measure how the structure of teams has changed this study used a Gini coefficient to measure the variability of player salaries throughout the top twelve highest paid players on each team. A Gini of zero means every player is paid the same on a team, and a Gini of one means that all of a team’s salary goes to one player. After calculating the Gini coefficients, a linear regression model was formed to look at the role of roster construction on team wins. Wins was the dependent variable and the independent variables consisted of percentage of cap spent by each team, the Gini for the top 12 players on each roster, and the difference between the Gini of the top 5 and top 3 on each roster. The findings show that the NBA is still centered on the accumulation of a “big three” players, but more successful teams have now re-allocated their spending across their top five players on the roster.
Models
- Two multiple linear regressions were tested
- The first is the “Main Model” which includes data from the 2010-2015 NBA seasons
- The goal of the model is to predict team wins
- These Seasons were grouped together because of similarity in results from each season
- The second model tested is a regression on the 2017 season
- The goal is to predict team wins based off of several of the same variables used in the Main Model
| Variables | Estimate | Standard Error | T Value | P( >| t | ) |
|---|---|---|---|---|
| Intercept | 5.6072 | 7.7914 | .720 | .472940 |
| Central | 5.0388 | 3.6395 | 1.384 | .168432 |
| Northwest | 5.1907 | 3.8484 | 1.349 | .179595 |
| Pacific | 5.0673 | 3.2204 | 1.574 | .117874 |
| Southeast | 3.5436 | 3.8382 | .923 . | 357472 |
| Southwest | 14.1567 | 3.5856 | 3.945 | .000126**** |
| Market_Size Medium | -3.8750 | 2.8795 | -1.346 | .180587 |
| Market_Size Small | .8955 | 3.1056 | .288 | .773506 |
| Gini_top_twelve | -24.3767 | 16.6310 | -1.466 | .144978 |
| per_cap | 32.0543 | 6.0735 | 5.278 | 4.91e-7 **** |
| five_three_diff | 53.7109 | 22.7006 | 2.366 | .019359** |
R-Squared: .3336
F Stat: 6.958 DF: 10,136
Main Model Results
- No significance of financial structure of teams positively or negatively impacting wins
- The percentage of the salary cap a team spent was significant and positive as has almost always been the case in the NBA
- Gini of the top twelve variable is negative but not significant
- Five three difference variable is significant and positive
| Variables | Estimate | Standard Error | T Value | P( >| t | ) |
|---|---|---|---|---|
| Intercept | -42.5864 | 12.9112 | -3.298 | .003423** |
| RDWSDP | 2.3021 | .5710 | 4.032 | .000602* |
| Market_Size_Medium | -3.9242 | 3.2004 | -1.226 | .233718 |
| Market_Size_Small | -.2371 | 3.5699 | -.066 | .947667 |
| Per_cap | 44.5526 | 14.6155 | 3.048 | .006108 |
| Gini_top_twelve | 116.6344 | 36.1792 | .3.224 | .004072** |
| Twelve_nine_diff | -37.6687 | 71.3801 | -.528 | .603226 |
| Nine_five_diff | -65.0215 | 45.8821 | -1.417 | .171107 |
| Five_three_diff | -117.7903 | 34.7448 | -3.390 | .002761** |
R-Squared: .7654
F Stat: 8.562 DF: 8,21 P-value: .00003853
2017 Regression Results
- Significance of how teams are financially structured
- Percentage of the cap a team spent is once again significant and positive
- Gini of the top twelve is now significant and positive
- Five three difference variable is now significant and negative (Reversal from 2010-2015 findings, means ”super teams” are now becoming deeper
Conclusions
- The way teams structure themselves financially is becoming significant
- Small market teams generally didn’t have large Gini coefficient values like Large market teams
- Small market teams fared well in the regular season but struggled in the postseason
- As seen by the graphs above successful large and medium sized market teams had large Gini coefficient values while small market teams had smaller Gini coefficient values (Small market teams attract less all star caliber players)
- Continue research for future years, but 2018 is on track to have similar results as 2017
References
Bullard, Kurt. “The Distribution of NBA Title Odds Would be World’s Most Unequal Economy” The Official Blog of the Harvard Sports Analysis Collective, 26 Oct. 2016
Zimbalist, Andrew S. “Competitive Balance In Sports Leagues An Introduction.” Journal of Sports Economics, May 2002. Sage Publications.
Contact
Cody Barbuto
Syracuse University
cabarbut@syr.edu
315-436-2646