Regression Analysis of Ipl Players Auction
Regression Analysis of Pricing of IPL Players | Project Report | | | |
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Pricing of Players in the Indian Premier League
Executive Summary
In the project, price for the players in IPL are analysed against various factors. Not all factors drove the price of a player were directly related to their performance on the field, whereas there are specific factors which had a direct impact on player’s remuneration. These factors ranged from performance measure of players such as Strike rate (in case of a batsman) to physical attributes of players such as age. We applied techniques of multiple linear regression to determine such factors which were deterministic in pricing the players.
Best Regression model(s)
The following are the independent variables which are derived after doing regression analysis.
Where, * Country = 1 for India, 0 for non-India * Age_3=1 for age < 25 years, 0 for others * T_Runs is test run scored * Runs_ODI is run scored in ODIs * ODI_Wickets is wickets taken in ODIs * RUN_S is runs scored * BASE_PRICE is base price * YEAR = 0 for year <2011, 0 for others Linear regression model has been developed using Backward variable selection method. The criterion used for Backward method is Probability of F-to-remove >= 0.100
As seen from the above table in our model the ‘R Square’ value of is 0.618 and ‘Adjusted R Square’ value is 0.592.
Team variable is removed
Cricket in the T20 format is considered a young man’s sport, is there evidence that the player’s price is influenced by age?
From our analysis we have seen that the price of a player is greater if the player is less than 25 years of age.
Variable | B | Std.error | Beta | t | Sig. | AGE | 173039.227 | 76474.862 | .140 | 2.263 | .025 |
As we can see from the data the Significance value is less than 0.05 which indicates that the player’s price is influenced by age.
What is the impact of ability to
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Pricing of Players in the Indian Premier League
Executive Summary
In the project, price for the players in IPL are analysed against various factors. Not all factors drove the price of a player were directly related to their performance on the field, whereas there are specific factors which had a direct impact on player’s remuneration. These factors ranged from performance measure of players such as Strike rate (in case of a batsman) to physical attributes of players such as age. We applied techniques of multiple linear regression to determine such factors which were deterministic in pricing the players.
Best Regression model(s)
The following are the independent variables which are derived after doing regression analysis.
Where, * Country = 1 for India, 0 for non-India * Age_3=1 for age < 25 years, 0 for others * T_Runs is test run scored * Runs_ODI is run scored in ODIs * ODI_Wickets is wickets taken in ODIs * RUN_S is runs scored * BASE_PRICE is base price * YEAR = 0 for year <2011, 0 for others Linear regression model has been developed using Backward variable selection method. The criterion used for Backward method is Probability of F-to-remove >= 0.100
As seen from the above table in our model the ‘R Square’ value of is 0.618 and ‘Adjusted R Square’ value is 0.592.
Team variable is removed
Cricket in the T20 format is considered a young man’s sport, is there evidence that the player’s price is influenced by age?
From our analysis we have seen that the price of a player is greater if the player is less than 25 years of age.
Variable | B | Std.error | Beta | t | Sig. | AGE | 173039.227 | 76474.862 | .140 | 2.263 | .025 |
As we can see from the data the Significance value is less than 0.05 which indicates that the player’s price is influenced by age.
What is the impact of ability to