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Name:

Description:

For most football fans, May - July represents a lull period due to the lack of club football. What makes up for it, is the intense transfer speculation that surrounds all major player transfers today. Their market valuations also lead to a few raised eyebrows, lately more than ever. I was curious to see how good a proxy popularity could be for ability, and the predictive power it would have in a model estimating a player's market value.

Variables:

name: Name of the player

club: Club of the player

age : Age of the player

position : The usual position on the pitch

position_cat :

  • 1 for attackers

  • 2 for midfielders

  • 3 for defenders

  • 4 for goalkeepers

market_value : As on transfermrkt.com on July 20th, 2017

page_views : Average daily Wikipedia page views from September 1, 2016 to May 1, 2017

fpl_value : Value in Fantasy Premier League as on July 20th, 2017

fpl_sel : % of FPL players who have selected that player in their team

fpl_points : FPL points accumulated over the previous season

region:

  • 1 for England

  • 2 for EU

  • 3 for Americas

  • 4 for Rest of World

nationality

new_foreign : Whether a new signing from a different league, for 2017/18 (till 20th July)

age_cat

club_id

big_club: Whether one of the Top 6 clubs

new_signing: Whether a new signing for 2017/18 (till 20th July)

Link To Google Sheets:

Rows:

Columns:

License Type:

References/Notes/Attributions:

R Dataset Upload:

Use the following R code to directly access this dataset in R.

d <- read.csv("https://www.key2stats.com/English_Premier_League_Players_Dataset__2017_18_1616_1.csv")

R Coding Interface:


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