Data Last Updated: 3/18/2021
Beach Volleyball Match Data and Statistics
This repository contains most beach volleyball men's and women's match data and match statistics from the AVP & FIVB tours.
Date represents tournament date and is typically the first day of the tournament.
The winning team is denoted by "w_" and the losing team is denoted by "l_".
The player names have been sorted alphabetically within each row between player1 and player2. This means that "Nick Lucena" should always appear in "w_player1" and "Phil Dalhausser" should appear in "w_player2", regardless of the data source order. This allows easier matching downstream.
Age is represented in years and is calculated as: (event date - birthdate) / 365.25, rounded to 7 decimals.
Height is represented in inches.
"w_rank" and "l_rank" follow the convention "seed, qualifier". If players have individual seeds (e.g. in a King of Beach tournament), only the second player's seeding is retained at the time of the data pull, but before the players have been sorted alphabetically. This means that for these matches, the seeding may not correspond to the correct player.
Duration is represented as "h:mm" (hours:minutes)
Match stats (columns 34:65) represent match totals. Available for Contender's Bracket and above for about 20 FIVB and most AVP tournaments. "Hitpct" represents (kills - errors) / attacks.
Using this data, I created a beach volleyball Tableau dashboard to summarize some key metrics:
Read more on my blog here: https://bigtimestats.wordpress.com/2017/09/18/avp-fivb-beach-volleyball-match-database/
Here is some starter code in Python to download the data into memory and query it with SQL. This code was run in Google Colaboratory and prints the top 10 teams with the highest winning percentage:
import pandas as pd # !pip install pandasql import pandasql as ps url = ['https://raw.githubusercontent.com/BigTimeStats/beach-volleyball/master/data/full_archive/full_archive.csv' ] df = pd.read_csv(url, index_col=None, header=0, encoding = "latin-1") df.head(5) q1 = """ select player1, player2, count(*) as matches_played, round(AVG(Win) * 100, 0) as win_pct FROM (select w_player1 as player1, w_player2 as player2, 1 as Win from df UNION ALL select l_player1 as player1, l_player2 as player2, 0 as Win from df) a group by player1, player2 having count(*) > 100 order by AVG(Win) desc limit 10 """ print(ps.sqldf(q1, locals()))
player1 player2 matches_played win_pct 0 Kerri Walsh Jennings Misty May-Treanor 846 92.0 1 April Ross Kerri Walsh Jennings 241 88.0 2 Larissa Franca Talita Antunes 194 86.0 3 Phil Dalhausser Todd Rogers 689 85.0 4 Juliana Felisberta Larissa Franca 622 84.0 5 Elaine Youngs Holly McPeak 259 83.0 6 Ana Paula Henkel Sandra Pires 106 82.0 7 Jonas Reckermann Julius Brink 186 80.0 8 Elaine Youngs Nicole Branagh 369 79.0 9 Emanuel Rego Ricardo Santos 567 79.0
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