Advance Data - Pricing of Players in the Indian Premier League

Advance Data Analysis

ASSIGNMENT: PRICING OF PLAYERS IN THE INDIAN PREMIER LEAGUE

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   Submitted to -                                                                   Submitted by-

   Dr. Shailja Rego                                                                Vikas Garg

                                                                                               D023, Group 1

Objective

To recognise selling price of a cricket player in IPL depends upon which variables. Find out the relationship between selling price and these identified variables and review the effectiveness of the above relationship through multiple coefficient of determination.

Procedure

Multiple Regression Analysis (MRA) was used to attain the relationship and measure its effectiveness. The following steps were taken for this

Step1:

To modify the data and identify the dependent variable and the independent variables in the data.

Dependent variable: SQRT(S-B) where S = Sold price and Base Price = Base Price

Independent variables: Auction year, MTS, B25-35, BOW*SR-BL, AUSTRALIA, BAT, CAPTAINCY EXP, TEAM, BOW*T-WKTS, L25, BOW*ODI-SR-BL, INDIA, BAT*SIXERS T20, BAT*T-RUNS, BOW*WICKETS, BOW*ODI-WKTS, BAT*ODI-SR-B, BOW*ECO, BAT*HS T20, BAT*ODI-RUNS-S, BOW, BAT*AVE T20, BAT*SR -B T20, BOW*RUNS-C, BAT*RUN T20, BOW*AVE-BL

Step 2:

As Regression can be done when both dependent and independent variables are quantitative. So, all the variables which were qualitative are converted to quantitative variables by using dummy. Auction_year was also converted to quantitative by assuming year 2008 as 1, 2009 as 2 and 2011 as 3.

Step 3:

Perform regression on the data

The output of Regression is as follows-

Interpretation:

We can use above ANOVA table for checking whether the model is significant or not. As we can see the of sig. is .000 which is less than 0.05. Hence, the overall model is significant.

.000 < 0.05

Step 4:

One-Way ANOVA was used to check the significance of every dummy variable with the dependant variable. Post hoc analysis is also been done to check significance difference between samples.

AGE