A simple data analysis of players of World Cup 2018 - more of a pandas exercise rather than a serious data analysis:)

Install python requiremenets:

pip install -r requirements.txt

And start a notebook server:

jupyter notebook

Go to http://localhost:8888/notebooks/wc2016.ipynb

Get the players data from fifa world cup 2018 official announcement pdf

Export the data (tables) using tabula and store it as csv.

And then do a simple analysis for simple stats like:

• age

  • average age of each national team
  • max (oldest), min (youngest)
  • age distribution

• height

  • average height of each team
  • max (tallest), min (shortest)

• bmi

  • of each player (mass/height^2)
  • average of team
  • max (fattest), min(lightest)
  • bmi distribution
  • bmi distribution by position (GK, DF, MF, FW)

• How many players play in domestic league per team

• Club representation What are the clubs that have the most players ? How do the numbers change as the tournament progresses (group-stage, round of 16, quarter finals ,..)

• Possible expansions (need merge with other datasets):

  • birthday paradox! (?) per match ? (what is the chance in 22 players to have the same birthday! - verify theoritical vs observation (group matches)) (needs group matches info)

  • panini misses! who are the players that panini missed or wrongly included? (needs panini album dataset - can be found here (?)

  • Team power ranking according to club ranking (needs club world ranking data)

  • Team value already here