FIVB Beach data analysis for win% on court and hour played

Tyler Widdison

This paper examines the relationship between win%, court and hour. The represented data is from the top 8 teams. This project was completed for my UCLA Extension class, Introduction to Data Science. For this project I web scrapped the FIVB Beach Volleyball schedule/results and rankings from 2001 - 2019. To gain additional data wrangling, EDA and visualization skills and build a database of historical performances that can be used for future research.

Win% and top 8 teams

Since 2001, the FIVB has databased the results of FIVB beach volleyball tournaments. Players typically play in as many tournaments necessary to qualify for that specific Olympic quad.

ppy_df %>%
  dplyr::group_by(player, year) %>%
  dplyr::summarise(total_matches = sum(total_matches)) %>%
  ggplot(aes(total_matches)) + 
  geom_density(fill = 'mediumaquamarine') + 
  geom_vline(aes(xintercept = mean(total_matches)), linetype= 'dashed')