openvolley - Data analysis in R and Python

Code

from datavolley import read_dv
import pandas as pd

Code

import urllib.request
datavolleyfile="&stuttgart-schwerin-2018.dvw"
url_source_file = "https://raw.githubusercontent.com/openvolley/ovva/master/inst/extdata/demo/%26stuttgart-schwerin-2018.dvw"
source = urllib.request.urlopen(url_source_file).read().decode('UTF-8')
# Scrittura dei dati in un file locale
with open(datavolleyfile, 'w') as file:
    file.write(source)

Code

# change this if you have your file
datavolleyfile="&stuttgart-schwerin-2018.dvw"
# Reading the file
dvf = read_dv.DataVolley(datavolleyfile)
# extract all the actions
plays = dvf.get_plays()

Code

championship = dvf.match_info.championship.values[0]
match_day = dvf.match_info.day.values[0]
match_time = dvf.match_info.time.values[0]
match_season = dvf.match_info.season.values[0]

Code

total_minutes = dvf.sets_info.duration.sum()
hours = total_minutes // 60
minutes = total_minutes % 60

Code

string_sets = ""
duraton_sets = ""
for idx, row in dvf.sets_info.iterrows():
   string_sets += "(" + str(row["home4"]) + "-" + str(row["visitor4"]) + ") "
   duraton_sets += str(row["duration"]) + "',"

Code

duration_sets = duraton_sets.rstrip(",")

Code

print("""
%s - %s
%s
%s - %s 
%s-%s %s
duration: %s:%sh (%s)
""" % (match_day, match_time, championship, dvf.home_team, 
       dvf.visiting_team, dvf.home_setswon, dvf.visiting_setswon,
       string_sets,str(hours).zfill(2), str(minutes).zfill(2), duration_sets))


26/12/2023 - 17.00.00
Serie A1 Femminile
Wash4green Pinerolo - HONDA OLIVERO S.BERNARDO CUNEO 
2-3 (25-20) (13-25) (25-23) (22-25) (15-17) 
duration: 02:06h (27',22',29',26',22')

Code

print(dvf.home_team)
print("name\tname")
players = dvf.players_home
players['player_number'] = pd.to_numeric(players['player_number'], errors='coerce')
players = players.sort_values("player_number")
plays = dvf.get_plays()
for idx, row in players.iterrows():
    player_number = row["player_number"]
    player_name = row['player_name'].rstrip()
    player_id = row['player_id']
    points = plays[(plays.player_id == player_id) & plays.skill.isin(['Attack', 'Serve','Block']) & (plays.evaluation_code == "#")].shape[0]
    errors = plays[(plays.player_id == player_id) & plays.skill.isin(['Attack', 'Reception','Dig','Set','Block']) & (plays.evaluation_code == "=")].shape[0]
    print("""%s\t%s (%i,%i)""" % (player_number,player_name, points, errors))
print("coaches")
print(" " + dvf.home_coaches[0])
print(" " + dvf.home_coaches[1])
print("\nin parentheses: number of points, number of errors")

Wash4green Pinerolo
name    name
1   Indre Sorokaite (18,9)
2   Francesca Cosi (0,2)
3   Carlotta Cambi (0,5)
4   Giada Di Mario (0,5)
5   Tessa Polder (2,5)
8   Silvia Bussoli (0,0)
9   Matilde Rostagno (0,0)
10  Ilenia Moro (0,4)
11  Maja Storck (16,8)
12  Letizia Camera (0,1)
13  Anett Nemeth (4,1)
18  Yasmina Akrari (12,9)
19  Adelina Ungureanu (29,8)
coaches
 Marchiaro Michele
 Naddeo Alberto

in parentheses: number of points, number of errors

Code

dvf.home_coaches[0]

'Marchiaro Michele'

Code

print(dvf.visiting_team)
print("name\tname")
players = dvf.players_visiting
players['player_number'] = pd.to_numeric(players['player_number'], errors='coerce')
players = players.sort_values("player_number")
plays = dvf.get_plays()
for idx, row in players.iterrows():
    player_number = row["player_number"]
    player_name = row['player_name'].rstrip()
    player_id = row['player_id']
    points = plays[(plays.player_id == player_id) & plays.skill.isin(['Attack', 'Serve','Block']) & (plays.evaluation_code == "#")].shape[0]
    errors = plays[(plays.player_id == player_id) & plays.skill.isin(['Attack', 'Reception','Dig','Set','Block']) & (plays.evaluation_code == "=")].shape[0]
    print("""%s\t%s (%i,%i)""" % (player_number,player_name, points, errors))
print("coaches")
print(" " + dvf.visiting_coaches[0])
print(" " + dvf.visiting_coaches[1])
print("\nin parentheses: number of points, number of errors")

HONDA OLIVERO S.BERNARDO CUNEO
name    name
2   Francesca Scola (0,0)
4   Saly Thior (0,0)
5   Alice Tanase (0,0)
6   Federica Ferrario (0,5)
7   Amandha Sylves (13,8)
8   Lena Stigrot (17,10)
9   Anna Adelusi (7,6)
10  Madison Kubik (19,6)
12  Terry Ruth Enweonwu (6,11)
13  Noemi Signorile (3,2)
14  Anna Hall (15,2)
15  Serena Scognamillo (0,0)
17  Anna Haak (2,1)
21  Beatrice Molinaro (0,0)
coaches
 Bellano Massimo
 Aime Emanuele

in parentheses: number of points, number of errors