Pay and Performance in Major League Sports

Purpose Statement 

This study examines the correlation between the pay and performance in Major League Sports. The Pearson correlation coefficient was used to assess the relationship linking the dependent and independent variables (Bujang & Baharum, 2016). The dependent variable player performance is determined by independent variables such as salary, number of games played, hours practiced, and age. The most important independent variable in this relationship is the salary of the player because it is a center of motivation on the pitch, thus, resulting to top performance. The salary of the Major League is influenced by the performance of the player (Tao et al., 2016). For the player to earn more amount of money, they will have to exhibit increased performance. 

Definition of Variables 

Dependent Variable 

A dependent variable is a variable that is of primary interest to a study. The dependent variable in this case is the performance of the player. It is the total input of a layer towards a team and game. Performance shows the number of points a player scores per minute of playing time. The level of measurement for this variable is ratio, since it shows the number of points scored per minute of playing time. 

Primary Independent Variable 

The salary of the player is the primary independent variable. The independent variable directly impacts the dependent variable in his case is the player salary. It represents the total amount of gross earning of a player. The level of measurement for this variable is ratio and it is measured in dollars. The amount of salary or pay is gleaned by assessing the paper performance and contribution towards the team. The player performance is affected by the amount of salary earned (Hill et al., 2017). According to Rosner & Shropshire (2011), there is a positive association between the player performance and the amount of income. It suggests that an increase in the amount of player salary will result in increased play performance. 

Three Independent Variables 

Independent variable, games played- this is the total number of games player by the played in the league. The level of measurement for this variable is ratio and it is measured by the total number of games of a player. The number of played games is assessed by the number of times a basket player appears on a pitch or participates in a league game. If the player participates in all team games in a regular season, he or she is supported to play total of 82 games, both home and away (International Basketball Federation, 2020). 

Independent variable, hours practiced- it shows the period of time a player takes to train in the field. Training is an integral part in the good performance of a player. This is an interval variable that is measured by the number of hours taken by a player in training. The number of hours a player takes to train is directly associated with performance (Sabesan et al. 2018). This means that the more hours a player takes in training improve his or her play performance. 

Independent variable, age- it describes the number of years that a player has lived. The level of measurement for age is ratio and it is measured in years. It is not automatic that the age of the player will impact on performance. There are young and older players who perform highly, while others perform poorly on the pitch. According to Celik & Ince-Yenilmez (2017), the prime aged have a high performance and salary or pay. Hence, age has a remarkable impact of pay and performance of the player. 

Data Description 

The study will use the secondary data from 30 professional basketball players. It will be collected from catalogues and websites that comprises of both the MLS Players Salary Guide, and MLS Players Association. The data collected will be administrative, this suggest that it will be extracted from previous studies, websites and the Major league database regarding the players. A questionnaire will be employed in assessing the demographic information of the player, such as age and gender, monthly income, number of games played and hours practiced. Simple random sampling technique was employed to gather data from the players. All the players had an equal chance of being selected for the study (Sharma, 2017). A sample of 30 players was selected at random for the league. The players sampled should be registered basketball professional players by Major League Sports. This data is collected from 1 st January 2021 to 1 st January 2022. This is a period of one year that represent a whole season. The study is limited to a small sample size. 

Results and Interpretation 

Multiple Regression 

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.778701
R Square	0.606375
Adjusted R Square	0.560956
Standard Error	2.81845
Observations	30
ANOVA
	df	SS	MS	F	Significance F
Regression	3	318.1648	106.0549	13.35089	1.81E-05
Residual	26	206.5352	7.94366
Total	29	524.7
	Coefficients	Standard Error	t Stat	P-value
Intercept	64.64606	3.2644	19.80335	3.31E-17
Salary	0.199645	0.316721	0.63035	0.033967
Age	-0.02157	0.177552	-0.12147	0.904251
Hours	0.005855	0.001961	2.985853	0.006091

The adjusted R 2 is 0.560956, it implies that 56.0956 percent of the variations of the player performance is explained by the model. 

The regression equation is given as; 

Performance = 64.64606+ 0.199645 Salary – 0.022157 Age + 0.005855 hours 

The coefficient of player salary is 0.199645, suggesting that salary has a significantly positive correlation between with performance ( p < 0.05). An increase in the player income will rai se the performance level. The hours of practice have a significantly positive correlation with performance ( p < 0.05). An increase in the player hours of practice will rise the performance. However, age had no significant effect. Therefore, the player pay/salary and the hours of play significantly improve their performance on the pitch. 

References 

Bujang, M. A., & Baharum, N. (2016). Sample size guideline for correlation analysis.  World ,  3 (1). 

Celik, O. B., & Ince-Yenilmez, M. (2017). Salary differences under the salary cap in Major League Soccer. International Journal of Sports Science & Coaching, 12 (5), 623-634. 

Hill, A. D., Aime, F., & Ridge, J. W. (2017). The performance implications of resource and pay dispersion: the case of Major League Baseball.  Strategic Management Journal ,  38 (9), 1935-1947. 

International Basketball Federation (2020). Statistics. http://www.fiba.basketball/ 

MLS Players Salary Guide | MLS Players Association. (2019). https://mlsplayers.org/resources/salary-guid. 

Rosner, S., & Shropshire, K. (Eds.). (2011). The business of sports. Jones & Bartlett Publishers. 

Sabesan, V. J., Prey, B., Smith, R., Lombardo, D. J., Borroto, W. J., & Whaley, J. D. (2018). Concussion rates and effects on player performance in Major League Baseball players.  Open Access Journal of Sports Medicine ,  9 , 253. 

Sharma, G. (2017). Pros and cons of different sampling techniques.  International Journal of Applied Research ,  3 (7), 749-752. 

Tao, Y. L., Chuang, H. L., & Lin, E. S. (2016). Compensation and performance in Major League Baseball: Evidence from salary dispersion and team performance.  International Review of Economics & Finance ,  43 , 151-159.