Determinants of english premier league soccer players wealth case study examples

Description of the study

Soccer players in English premier League are some of the wealthiest sportsmen in the world. However, the wealth of individual soccer players in the English premier League differs with some players having a higher net worth than others. An article by Sunday Time listed David Beckham as the wealthiest soccer player with a net worth of 249 million dollars. The list was based on identifiable wealth, property, land and other assets excluding bank account balances. It stated that the wealth of soccer players could be determined by their physical performance, annual income and education level. To this end, this study seeks to ascertain whether a soccer player’s value is determined by physical performance, annual income and education level.

Study questions

The questions for this study were;
Is physical performance a significant determinant of soccer players’ wealth?
Is annual income a significant determinant of soccer players’ wealth?
Is education level a significant determinant of soccer players’ wealth?
Hypotheses
Null hypotheses (H0):
Physical performance is not a significant determinant of soccer players’ wealth.
Annual income is not a significant determinant of soccer players’ wealth.
Education level is not a significant determinant of soccer players’ wealth.
The three null hypotheses can be represented as;
H0: b1 = b2 = b3 = 0
Alternate hypotheses (HA):
Physical performance is a significant determinant of soccer players’ wealth.
Annual income is a significant determinant of soccer players’ wealth.
Education level is a significant determinant of soccer players’ wealth.
The three null hypotheses can be represented as;
H0: b1 ≠ b2 ≠ b3 ≠ 0

I think annual income and education level are significant determinants of a player’s value. This is because soccer players’ annual income determines how much money they can invest. It is also logical to assume soccer players with higher education levels will make more sound investment decisions and hence create more wealth. I do not think physical performance will be a significant determinant.

Data type and source

This study will rely on secondary data to obtain the data required for this study. Data regarding to a player’s wealth, annual income, physical performance and education level will be obtained. Data relating to the year 2011 will be used because it is the most recent historical data that can be obtained.

The unit of observation of this study will be players. A random sample will be obtained from the population of English premier League players.

Definition and measurement of variables

The dependent variable will be a players’ wealth. A player’s wealth will be measured by the net worth of the player in US dollars. There will be three independent variables for this study; physical performance, incomes and education level. Physical performance will be measured by the number of matches a player played for the team in a season. Income will be measured by how much a player earned in dollars. Education level is a dummy variable. Data on education will be qualitative. Therefore, it will be coded to facilitate regression analysis. Data on education level was given numerical values; respondents with high school education will be assigned code 0 while those with college education will be assigned the code 1.

Model

Y = b0 + b1X1 + b2 X2 + b3 X3
Where;
Y is the dependent variable which represents a player’s wealth in US dollars
X1 is an independent variable representing the physical performance of a player
X2 is an independent variable representing the annual income of a player
X2 is a dummy variable for education level. The education level dummy variable will be zero for high school education and one for college education.
b1 , b2 , and b3 represent coefficients of the variables X1 , X2 , X3 respectively.

Data analysis

This study will rely on multiple linear regression analysis during data analysis. Multiple linear regression analysis will be used in evaluating the nature of the correlation between the dependent variable and the three independent variables. Multiple regression analysis will also be used to ascertain the explanatory strength of the model developed. T-tests for regression will then be performed to ascertain whether the coefficients of the three independent variables are statistically significant. A p-value of more than 0. 05 will imply that the null hypothesis should be rejected. Therefore, the variable will be statistically significant. On the other hand, a p-value of less than 0. 05 will imply that the null hypothesis should be accepted. Therefore, the variable will not be statistically significant..

Error term

Auto correlation is not likely to arise in the regression analysis because the data used was not time series data. Heteroskedasticity is likely to arise because the model will rely on historical data from secondary sources. White test will be used to test for Heteroskedasticity. Heteroskedasticity can be corrected by corrected by weighted least-square method.

Use of the model

The model will be able to explain some of the variations in the wealth of English Premier League soccer players. This study results will be of interest to soccer players, sports analysts and commentators, investment advisors of soccer players and government officials.

Works Cited

Beresford, Philip. ” Richest Footballers In England 2012 – UK Rich List.” The Sunday Times. London: The Sunday Times, 6 May 2012.
Gravetter, Frederick J and Lori-Ann B Forzano. Research Methods for the Behavioral Sciences. 4. London: Cengage Learning, 2011.