As happens so often, I wanted to focus on something slightly different: the growing pay gap between male and female soccer player as we enter the age of $100 million salaries, boosted by the growing presence of wealthy, Gulf-based sovereign funds.
-- A general unavailability of individual salary data from the National Women's Soccer League
-- The basis of a series of visualization on the recent attempts by Gulf states to radically broaden their sports footprint
-- Sporting News salary data (https://www.sportingnews.com/us/soccer/news/erling-haaland-salary-man-city-premier-league-top-scorer/) and several other sources (https://salarysport.com/; https://www.expensivity.com/soccer-salary-inflation/; https://soccerblade.com/how-much-soccer-players-paid/)
-- Multiple news reports on LIV and PGA (https://www.espn.com/golf/story/_/id/37837794/inside-pga-tour-liv-golf-saudi-public-investment-fund-deal)
-- News reports on rejected Formula 1 bid (https://www.bloomberg.com/news/articles/2023-01-20/saudi-arabia-wealth-fund-explored-bid-to-buy-f1-motor-racing)
-- Wikipedia, multipe pages
-- Data cleanup: appending several tables; bringing consistency to tables; simplifying entries; removing unnecessary columns. Tool: python
-- Data analysis: Grouping and averaging; lots of mapping with interactive tool tips.
-- Visualization: Used mostly Datawrapper.
-- Deeper understanding of Datawrapper's capabilities; basic usage of Rawgraphs
-- Deciding the shape of the data; when to go with long data.
-- I really really really wanted to use Figma and Figma2html but ran out of time.
-- I had found a much, much better and more recent poll on sports fans' attitude toward human right -- but when I tried to use it, Statista wanted me to pay. So I had to look for publicly available Yougov data.
-- I still don't understand why I can't import Datawrapper's fonts, which automatically default to Times New Roman.