You want to study factors that decide the transfer value of soccer players. The valuation of soccer players is determined by several factors, such as player characteristics and performance data. Because being equipped with talented players is essential for professional soccer team’s successful performance, professional clubs in top soccer leagues attempt to maintain a competitive squad by purchasing and selling players. Prior to a transaction, management and experts work collectively to explore both qualitative and quantitative variables to compute a fair price to be paid to (or received from) another team in exchange for a particular player. Due to the subjective nature and countless variables that are taken into account when valuing a player, the transfer value can be quite arbitrary and did not necessarily represent a player’s true market value.
 
The Excel file “Extra Credit_Transfer Value of Soccer Player” contains data for 77 transfers of notable offensive soccer players (i.e. midfielders and forwards) that moved to another team between 2009 and 2015. Let’s understand factors that are related to a players’ value and also build a functioning model that could predict a player’s market value.
 
Here are the explanation of the variables:
GBP: the player’s transfer price in British pounds
Y-5: Appearance over total number of league games (utilization ratio) five years prior to transfer
Y-4: Appearance over total number of league games four years prior to transfer
Y-3: Appearance over total number of league games three years prior to transfer
Y-2: Appearance over total number of league games two years prior to transfer
Y-1: Appearance over total number of league games one year prior to transfer
WA: weighted average of utilization ratio in league games over the past five years (seasons)
Goals: total number of goals scored in league games over the past five years
App: total number of appearance in league games over the past five years
G/A: ratio of goals to appearance in league games over the past five years
Age: player’s age at the time of transfer
Height: height of player
Pos: player’s position—OFF for offensive position, such as striker and wingers, MID for offensive midfielder
Foot: dominant foot—L for left, R for right, B for both
CR: Club ranking of the team that is selling the player in categories: 1=top eight in league ranking over the past five years; 2=below top eight in league ranking over the past five years
NR: FIFA ranking of the player’s national team in categories: 1=almost always enters round 16 in the FIFA World Cup, and has a high probability of reaching the semi-finals; 2=often enters round 16, but has a low probability of reaching the semi-finals; 3=often does not enter round 16, and has a very low chance of reaching the semi-finals.
 
 
 
 
 
 
 
Questions:

With all the variables provided in the Excel, intuitively, discuss which ones you think are important determinants for soccer players’ transfer value. Please note that not all variables are necessarily important in this study.

[Type your answers below]
 
 

For some categorical variables, if you think they are important determinants for players’ transfer value, create dummy variables for them. Please note that you will only need to do this for the categorical variables you believe are important.

[Type your answers below]
 
 

For the variables that you think are important from question 1 and converted to dummy variables in question 2, make the correlation table and identify whether there’s any multicollinearity problem.

[Type your answers below]
 
 

Run regression to determine factors that are related to players’ transfer value (as measured by the variable “GBP”). Copy and paste your regression result to the word and briefly discuss the following:

The significance of each independent variable. For the significant independent variables, discuss whether the signs are as expected and also describe how a one unit change in each significant independent variable will affect your dependent variable.
Briefly discuss R-square, adjusted R-square, F-value, and p-value for the ANOVA table.
If your regression in (1) does not make sense, fix it by adding or dropping variables.

[First copy & paste your regression result from excel (screenshot), then type your answers below]
 
 

Pick a player listed, use the regression model from question 4 to predict this player’s transfer value. Compare the predicted value to the actual transfer value.

[Type your answers below]
 
 

Discuss the limitations of your analysis and how it can be improved.

[Type your answers below]
 

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