FIVB Beach Volleyball Historic Top 8 Teams Analysis
Project Description
This is a repositiory for my final project in 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.
Source of the Data Set
The schedule data was web scrapped from 753 tournament web pages from old fivb, new fivb and fivb vis public pages.
The end of year ranking was taken on Oct 1 at the end of each year from 2001 - 2019 using FIVB ranking pages.
Originally I was going to use @BigTimeStats dataset, however after investigating naming convetions for players I found it difficult to add a end of the year ranking column matching player names. In the end I had to doctor the naming convention in order to match correct ranking and schedule player names. The final dataset looked like this (each row representing one match):
| no | date | time | court | result | duration | tourn | year | phase | team_a | team_a_country | team_b | team_b_country | winning_country | team_a_rank | player_1_team_a | player_2_team_a | team_b_rank | player_1_team_b | player_2_team_b | gender | team_a_sets_won | team_b_sets_won | team_a_game_one_points | team_b_game_one_points | team_a_game_two_points | team_b_game_two_points | team_a_game_three_points | team_b_game_three_points | tourn_rank |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | 2016-08-25 | 16:00 | 2 | 2-1 (23-21, 19-21, 15-7) | 0:45:00 | wlob2016 | 2016 | Pool B | larissa/talita | bra | van der vlist/van gestel | ned | bra | 3 | larissa | talita | 25 | van der vlist | van gestel | w | 2 | 1 | 23 | 21 | 10 | 21 | 15 | 7 | 43.03 |
Contents
| File | Description |
|---|---|
| finishes_main.csv | CSV file from webscrape of rankings |
| fivb_2001_to_2019_early_nov_results.csv | CSV file from webscrape of scheudle/results |
| Report.pdf | Report about findings |
| DS_Report.pdf | Report about code |
Tools
- R for data science means
- Python for web scrapping
- Excel/Google sheets for manual doctor edits
Future works
- Adding additional columns of height, weight, birthdate, fivb player number, and appending .dvw scouted matches data.
- Shiny apps for quick comparisons of players, teams, or countries
Author
Tyler Widdison