The NBA Player Annual salary

Purpose of the Study

NBA began in 1946 as the Basketball Association of America with the first game attracting a crowd of about 8000 spectators. The first years of its initiation saw players earning an average of $5000 annually, making them settle for other jobs during the offseason (Robert Bradley, 2018). The player earnings from the NBA has exploded considerably to an estimated average of $5,012,892 in the 2016/2017 season (Ronny Gordon, 2017). The purpose of this study is to determine whether an athlete’s performance on the court influences the annual compensation. The Players’ Annual Salary (PAS) will be the dependent variable and will be determined by three independent variables, namely, Player Efficiency Rating (PER), Assist Percentage (AST), and Steal Percentage (STL). The Player Efficiency Rating is the primary independent variable since it cooperates with other factors, among them the dribbling rate, the offensive, and defensive abilities. These components generalize to the element of the general player’s efficiency. The general model in this study is expected to be

PAS= B0 + B1*PER + B2*AST + B3*STL

Definition of Variables

This paper has data on the 2016/217 NBA season. The Player Annual salary of the season is the dependent variable, and it is given in dollars ($) unit measure. This is the collective sum of benefits a player gains from his team throughout the season. This variable is going to be predicted by the independent variables. The degree effect of every independent variable to the Player Annual Salary is going to be realized in this study.

The important independent variable, Player Efficiency Rating is a measure of a player’s contribution per-minute. It is a rate measured using a statistical metric encompassing factors such as rebounds, attempts, and blocks. This is tipped to have a great positive impact on the annual pay of a basketball athlete. The Assist Percentage (AST) variable indicates the percentage calculated from the number of points scored by a team that the particular player assisted. This variable will affect the dependent variable positively since the assists determine the overall points that a team will gain. The Steal Percentage (STL) variable is the statistical measure of the number of times a player gets possession from the opponent, which is likely to cause harm to the opposite team. It seems to have a positive correlation to the dependent variable. This variable indicates how skillful a player is in regaining possession and may lead to an offensive attack resulting in quick baskets.

Data Description

This study was carried out using data of the 2016/2017 NBA season to find out the relationship between the Player Annual Salary and the independent variables, Player Efficiency Rate, Assist Percentage, and Steal Percentage. The data was sourced from public forums, www.basketball-reference.com, and www.espn.com/nba/salaries. The latter website provided data on Annual player earnings, of which a random sample of thirty names was selected. The observations of the independent variables were retrieved from the basketball-reference.com website. The original dataset had twenty-nine columns, but for purposes of this paper, three columns were extracted to be used as predictor variables. The values observed from the three variables were then manually merged with the selected random sample to form a data matrix of thirty observations per variable. The dataset is discrete per every randomly selected player. There are no major limitations of the data hence good for statistical analysis. Multiple regression analysis is to be carried to determine whether the predictor variables correlate with the dependent variable.

Presentation and Interpretation of Results

Table1.

Descriptive statistics of the sample data

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Statistic N      Mean          St. Dev.       Min        Max

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PAS       30       14,132.9    8,212.7       918.4      26,540.1

PER       30       17.4           4.9               10.8        27.6

AST       30       17.4           13.5             2.2          50.7

STL       30        1.5             0.8              0.3          3.1

Table1 above presents the summary statistics of the sampled data. It shows the mean of each of the variables, the standard deviation, and the minimum and maximum values. The standard deviation, which is a square of variance, shows how observed values are deviating from the calculated mean.

Table 2.            Estimate          Std. Error   t value   Pr (>|t|)    (Intercept) 6131.95    4840.77    -1.267   0.21648    PER          1143.00      302.85       3.774   0.00084 ***AST            25.90        128.67       0.201   0.84203    STL           -10.55        1855.05    -0.006   0.99551    —Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Multiple R-squared:  0.506,  Adjusted R-squared:  0.449 F-statistic: 8.876 on 3 and 26 DF,  p-value: 0.0003213

Table 2 above shows the summary of a multiple regression analysis results of the sampled data. The F-statistic was 8.876, meaning that at least one of the variables could be used to predict the dependent variable. From the p-values column, it is proven that only the Performance Efficiency Rate (PER) was statistically significant since its p-value is below 0.05 significance. Steal Percentage (STL) had a negative correlation with the dependent variable, which goes against our expected result. The model is found to be;

PAS= 6131.95 + 1143PER

Adjusted R is a value that is used in a model to adjust the number of terms. It solves a problem of multiple R squared of increasing when a new independent term is introduced in a model. It explains the percentage of variations of the dependent variable caused by predictor variables. From the table above, its value is 0.449.

Conclusion

This study sought to find the kind of relationship between the independent variables and the dependent one. From the above analysis, there is no statistical evidence that the Assist percentage and the Steal percentage affect the amount of pay a player receives annually. A player with a higher Performance Efficiency Rate is, however, likely to get more pay.

Appendix

Sample data

References

Ronny Gordon, (2017). Average NBA Salary, 2018. Retrieved from

https://gazettereview.com/2017/02/average-nba-salary-much-nba-players-make.html

Robert Bradley, (2018). Labor Pains Nothing New to the NBA.  Retrieved from

http://www.apbr.org/labor.html

Eastman, B. & Vincent, C. (2009). Determinants of pay in the nhl: A quantile regression approach. Journal of Sports Economics, 10 (3) 256-277.

basketball-reference.com. (2017) 2016-17 NBA Player Stats. Retrieved from

https://www.basketball-reference.com/leagues/NBA_2017_advanced.html

ESPN.com. (2017).  NBA Player Salaries – 2016-2017.  Retrieved from http://www.espn.com/nba/salaries/_/year/2017.html