Athletic scholarship has focused on determining the factors that contribute to differences in compensation among NBA players. While performance has often been attributed to salary differences, the impact that the position held by the player has received less analysis. This research will evaluate the wage discrepancies between players in the National Basketball Association. Specifically, the wage differences between offensive players, called “strikers” and defensive players, called “backers” will be evaluated through statistical analysis. Though both positions contribute to the success of a team in complementary ways, the importance of offensive players to generating fan enthusiasm and attaining point advantages against competing teams supports the hypothesis that players of this position would possess the highest earning potential. However, statistical analysis reveals a nuanced evaluation of this claim. As the statistical analysis demonstrated, defensive players received salaries that were higher on average and more consistent than their offensive counterpart. Yet, top earning offensive players received salaries that were higher than their defensive counterpart. These results suggest that offensive players are primarily rewarded monetarily for high performance and highlight the necessity of conducting further research to assess the relationship between performance indicators and offensive player salaries.
Analyzing professional athlete salaries requires targeting specific data points, graphing and comparative study. Athletics, as an economic paradigm, depends heavily upon the type of sporting under examination. The value of data points will carry particular distinction based on the target sporting genre. Therefore, researchers should expect to conclude information relative to particular genres and understand data may carry significant differences in value when compared to other sports.
Targeting professional basketball for examination requires an understanding of the sport’s context, player roles, productivity and geographic location. Prior to conducting an in-depth analysis of basketball, researchers should consider the National Basketball Association (NBA) as the premiere sporting league for the genre in the world. The NBA possesses the top talent and pays the highest wages to the best players (much like every sport pays top-dollar to the best ) among the various professional associations throughout the world. The Euroleague continues to be the second most popular basketball venue, and increasingly attracts top talent without current NBA contracts. Before conducting comparative study between the data points in each league respectively, researchers should target specific areas for analysis in both areas.
In order to discover how player wages correlate with productivity, researchers must examine several particular data points. The first of which being the annual salary each player receives; while the top talent in the NBA can generate millions annually, many players amass a six figure salary as second-tier player or member of another professional league’s roster. Another important data point is the specific player positions. For the sake of examination, reducing the five position player roles into two distinct categories aides in conducting research. Therefore, dividing predominately offensive players into one category (or “strikers”) and putting defensive counterparts (or “backs”). Once roles are categorized, it becomes significant to explore player productivity. The following data points relate to productivity and are used for comparative study: points, rebounds and assists per game, as well as geographic location (playing for the NBA in the United States or in the Euroleague). Concluding research found the higher the productivity among players for the American league, the higher the annual salary especially for the offensively productive players or “strikers.”
After applying the appropriate T-test, several factors can be extracted from the data. Primarily, the NBA values more productive strikers with higher salaries and Euroleague salaries appear to be more consistent between both strikers and backs. Strikers in both leagues possess higher production numbers than backs and are paid millions of dollars more in annual wages. Individual productivity data points (points, rebounds, assists) appear to be less of a factor than position type and playing for the American league roster.
The data for this test was obtained by evaluating the salaries of 49 defensive NBA players and 71 offensive NBA players. The players were randomly selected for this study. The tables for this analysis considered six variables: 1) wage (in million dollars), 2) position, 3) points (average points), 4) assist, and 5) whether the player was an American or foreigner. Yet, the focus of this research was on two primary variables: wages and the position held by the players. The research conducted a statistical analysis of the data by obtaining descriptive statistics describing the mean salaries of backer and striker players. Further, a t-test was conducted to confirm the reliability of the differences present in the statistic results.
The results of the T-test reveal that strikers receive generally higher salaries in the NBA. Table 1 presents the case processing summary of the analyzed dataset. The case processing summary describes the number of cases that were successfully assigned to the sample and can be referenced to determine whether the data was appropriately captured (Davis, 2013). The data obtained from the table describes the size and validity of the sample. As Table 1 illustrates, 49 cases were assigned to the back sample and 71 cases were assigned to the striker sample. No cases were excluded from either sample group, confirming that no errors were present when the sample was determined.
The second set of results describes the descriptive statistics obtained from the dataset. Table 2 presents the descriptives table, which presents descriptive statistics obtained from an analysis of all selected variables for the analysis (Antonius, 20013). Table 2 provides a significant understanding of basic data trends regarding wages paid to strikers and back players. As the median figures for both populations determines, average pay is variable between the groups. Overall, back end players enjoy a higher median salary than strikers. Further, strikers have the opportunity to earn a significantly higher salary than their backer counterparts. The upper bound for backers is higher than the upper bound for strikers while, yet the lower bound is also lower for backers. Thus back players have the opportunity to earn wages at the lowest end of the spectrum and at the highest end of the spectrum. These highlighted means are also illustrated in Figure 1. A cursory evaluation of the descriptive table would suggest that backers have the highest earning potential in the league, enjoying both a higher median salary and a higher upper salary range.
The summary of percentiles separates the dataset into values of 100 equal parts (Antonius, 2003). Table 3 provides a summary of the weighted average and Tukey’s Hinges values for calculating percentiles. Weighted averages divide the data results into seven percentiles (Walker & Shostak, 2010). In contrast, Tukey’s Hinges divides the data into three percentiles and was utilized to construct the boxplot illustrated in Figure 1. Both methods reveal that backs obtain higher wages than strikers on average, within the mid-range of the data. Yet, within the 75th percentile, both the weighted average and Turkey’s Hinges demonstrate that strikers receive higher earnings than backers. The data demonstrates that low- to mid-range backers earn higher wages, yet high-earning strikers earn more than high-earning backers.
The group statistics present the mean statistics for the sample groups (Davis, 2013). Table 4 presents the group statistics between backers and strikers. As the group statistics reveals, backers receive a higher mean wage than strikers, though the standard deviation between the wages is higher for strikers. The data confirms the findings that the range in wages is more diverse among strikers than backers.
Finally, Table 5 presents the results of the independent t-test. The t-test is utilized to confirm that a reliable difference exists between the mean data of sample groups (Walker & Almond, 2010). The t-value for the data is .021; thus with a p-value of .05, the difference between the wages earned by the two groups is statistically significant. The t-test confirms the validity of the descriptive statistics obtained to describe the wages of strikers and backers.
With a respective population of 49 and 71, the samples of backers and strikers was large enough to obtain reliable statistics on the wages earned by each group of NBA players. The analysis reveals two interesting trends. First, on average, defensive players receive higher wages than offensive players. The wage differences were especially pronounced for lower and middle earning players in both groups. However, offensive players in the upper percentiles earned higher wages than their defensive counterparts. This figure suggests that top offensive players are rewarded with higher salaries while defensive players receive consistent salaries regardless of performance.
The primary limitation of this research is that the exact reasons for the wage discrepancies between low and high earning offensive players cannot be determined from the statistical analysis. While it can be determined that offensive players in the top percentiles earn more than defensive players of similar standing, it is not known what factors contribute to higher pay. This presents the opportunity for further research that investigates the attributes that contribute to higher pay for some offensive players. This research sets a foundation for understanding the factors that contribute to wage discrepancies between different types of players in the NBA.
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Davis, C. (2013). SPSS for applied sciences: Basic statistical testing. Melbourne: CSIRO Publishing.
Walker, J., & Almond, P. (2010). Interpreting statistical findings: A guide for health professionals and students. Berkshire, UK: Open University Press.
Walker, G. A., & Shostak, J. (2010). Common statistical methods for clinical research with SAS examples (3rd ed.). Cary, NC: SAS Institute.