Reading plays - dvw files

DVW files

The data project scouting software, data volley 4 produces .dvw files. These files are similar to a text file. If your interested in some example files, you can find them here.

Python - Reading plays from one dvw file

The provided example file is used. You would typically fill in the None with the actual path of your dvw files.

Code

from datavolley import read_dv
import pandas as pd
from IPython.display import Markdown
pd.set_option('display.max_colwidth', 1000)
dv_instance = read_dv.DataVolley(None) # Replace `None` with path of your dvw file 
df = dv_instance.get_plays()
df = df[df['skill'].notna()]
df = df.head(5)
Markdown(df.to_markdown(index = False, tablefmt="github", maxcolwidths=[None]))

match_id	video_file_number	video_time	code	team	player_number	player_name	player_id	skill	evaluation_code	setter_position	attack_code	set_code	set_type	start_zone	end_zone	end_subzone	num_players_numeric	home_team_score	visiting_team_score	home_setter_position	visiting_setter_position	custom_code	home_p1	home_p2	home_p3	home_p4	home_p5	home_p6	visiting_p1	visiting_p2	visiting_p3	visiting_p4	visiting_p5	visiting_p6	start_coordinate	mid_coordinate	end_coordinate	point_phase	attack_phase	start_coordinate_x	start_coordinate_y	mid_coordinate_x	mid_coordinate_y	end_coordinate_x	end_coordinate_y	set_number	home_team	visiting_team	home_team_id	visiting_team_id	point_won_by	serving_team	receiving_team	rally_number	possesion_number
f21c7df0-9f7f-49fd-a16d-79bf997088df	1	494	*19SM+~78A~00	University of Louisville	19	Shannon Shields	-296094	Serve	+	1	nan	nan	nan	7	8	A	nan	1	0	1	6	00	19	9	11	15	10	7	1	16	17	10	6	8	431		7642	Serve	nan	1.26875	0.092596			1.68125	5.425924	1	University of Louisville	University of Dayton	17	42	University of Louisville	University of Louisville	University of Dayton	1	0
f21c7df0-9f7f-49fd-a16d-79bf997088df	1	495	a02RM-~58AM00B	University of Dayton	2	Maura Collins	-230138	Reception	-	6	nan	nan	nan	5	8	A	nan	1	0	1	6	00B	19	9	11	15	10	7	1	16	17	10	6	8	431		7642	Reception	nan	1.26875	0.092596			1.68125	5.425924	1	University of Louisville	University of Dayton	17	42	University of Louisville	University of Louisville	University of Dayton	1	1
f21c7df0-9f7f-49fd-a16d-79bf997088df	1	497	a08ET#~~8C~00	University of Dayton	8	Brooke Westbeld	-232525	Set	#	6	nan	nan	~	nan	8	C	nan	1	0	1	6	00	19	9	11	15	10	7	1	16	17	10	6	8	3147			Reception	nan	1.86875	2.09259					1	University of Louisville	University of Dayton	17	42	University of Louisville	University of Louisville	University of Dayton	1	1
f21c7df0-9f7f-49fd-a16d-79bf997088df	1	499	a10AT-X546CH200F	University of Dayton	10	Jamie Peterson	-11802	Attack	-	6	X5	nan	nan	4	6	C	2	1	0	1	6	00F	19	9	11	15	10	7	1	16	17	10	6	8	4512	5522	8150	Reception	Reception	0.55625	3.12963	0.9312499999999999	3.8703700000000008	1.9812500000000002	5.796294	1	University of Louisville	University of Dayton	17	42	University of Louisville	University of Louisville	University of Dayton	1	1
f21c7df0-9f7f-49fd-a16d-79bf997088df	1	499	*11BT+~~2C~00	University of Louisville	11	Anna Stevenson	-278838	Block	+	1	nan	nan	nan	nan	2	C	nan	1	0	1	6	00	19	9	11	15	10	7	1	16	17	10	6	8	4578			Serve	nan	3.03125	3.12963					1	University of Louisville	University of Dayton	17	42	University of Louisville	University of Louisville	University of Dayton	1	2

Python - Reading plays from multiple files

Code

import os

# Assign path
dvw_path_folder = os.path.expanduser("~\\desktop\\dvws")
file_extension = ".dvw"

# Get a list of all files with the specified extension in the directory
file_list = [f for f in os.listdir(dvw_path_folder) if f.endswith(file_extension)]

# Initialize an empty DataFrame to store combined data
combined_df = pd.DataFrame()

def process_file(path):
    dv_instance = read_dv.DataVolley(os.path.join(dvw_path_folder, path))
    df = dv_instance.get_plays()
    return df

# Loop through each file path
for file_name in file_list:
    combined_df = pd.concat([combined_df, process_file(file_name)], ignore_index=True)

match_count = combined_df[['match_id']].drop_duplicates()
Markdown(match_count.to_markdown(index = False))

match_id
ce8d628b-c22e-4483-9ada-3cf30fa667df
0cdcc9f8-6fd6-4875-97d3-b558d1e41f6e
02c95bfa-05f1-49df-8620-1e6f029e0c68
bc4ea206-b184-419d-8a05-89a5c440b608

---

R - Reading plays from one dvw file

The provided example file is used. You would typically fill in the None with the actual path of your dvw files.

Code

library(datavolley)
library(tidyverse)
x <- dv_read("datavolley//example_data.dvw") # Example data from python
px <- x$plays
px <- px %>% filter(!is.na(skill))
px <- head(px, 5)
knitr::kable(px)

match_id	point_id	time	video_file_number	video_time	code	team	player_number	player_name	player_id	skill	skill_type	evaluation_code	evaluation	attack_code	attack_description	set_code	set_description	set_type	start_zone	end_zone	end_subzone	end_cone	skill_subtype	num_players	num_players_numeric	special_code	timeout	end_of_set	substitution	point	home_team_score	visiting_team_score	home_setter_position	visiting_setter_position	custom_code	file_line_number	home_p1	home_p2	home_p3	home_p4	home_p5	home_p6	visiting_p1	visiting_p2	visiting_p3	visiting_p4	visiting_p5	visiting_p6	start_coordinate	mid_coordinate	end_coordinate	point_phase	attack_phase	start_coordinate_x	start_coordinate_y	mid_coordinate_x	mid_coordinate_y	end_coordinate_x	end_coordinate_y	home_player_id1	home_player_id2	home_player_id3	home_player_id4	home_player_id5	home_player_id6	visiting_player_id1	visiting_player_id2	visiting_player_id3	visiting_player_id4	visiting_player_id5	visiting_player_id6	set_number	team_touch_id	home_team	visiting_team	home_team_id	visiting_team_id	team_id	point_won_by	winning_attack	serving_team	phase	home_score_start_of_point	visiting_score_start_of_point
a9e6117d72bb9cf6065ceb32112aa955	0	NA	1	494	*19SM+~78A~00	University of Louisville	19	Shannon Shields	-296094	Serve	Jump-float serve	+	Positive, opponent some attack	NA	NA	NA	NA	NA	7	8	A	NA	NA	NA	NA	NA	FALSE	FALSE	FALSE	FALSE	1	0	1	6	00	123	19	9	11	15	10	7	1	16	17	10	6	8	431	NA	7642	NA	NA	1.26875	0.0925926	NA	NA	1.68125	5.425926	-296094	-225496	-278838	-224838	-75967	-224837	-231069	14555	-11804	-11802	-230139	-232525	1	2	University of Louisville	University of Dayton	17	42	17	University of Louisville	FALSE	University of Louisville	Serve	0	0
a9e6117d72bb9cf6065ceb32112aa955	0	NA	1	495	a02RM-~58AM00B	University of Dayton	2	Maura Collins	-230138	Reception	Jump-float serve reception	-	Negative, limited attack	NA	NA	NA	NA	NA	5	8	A	NA	Middle line	NA	NA	NA	FALSE	FALSE	FALSE	FALSE	1	0	1	6	00B	124	19	9	11	15	10	7	1	16	17	10	6	8	431	NA	7642	NA	NA	1.26875	0.0925926	NA	NA	1.68125	5.425926	-296094	-225496	-278838	-224838	-75967	-224837	-231069	14555	-11804	-11802	-230139	-232525	1	3	University of Louisville	University of Dayton	17	42	42	University of Louisville	FALSE	University of Louisville	Reception	0	0
a9e6117d72bb9cf6065ceb32112aa955	0	NA	1	497	a08ET#~~8C~00	University of Dayton	8	Brooke Westbeld	-232525	Set	Head ball set	#	Perfect	NA	NA	NA	NA	NA	NA	8	C	NA	NA	NA	NA	NA	FALSE	FALSE	FALSE	FALSE	1	0	1	6	00	125	19	9	11	15	10	7	1	16	17	10	6	8	3147	NA	NA	NA	NA	1.86875	2.0925926	NA	NA	NA	NA	-296094	-225496	-278838	-224838	-75967	-224837	-231069	14555	-11804	-11802	-230139	-232525	1	3	University of Louisville	University of Dayton	17	42	42	University of Louisville	FALSE	University of Louisville	Reception	0	0
a9e6117d72bb9cf6065ceb32112aa955	0	NA	1	499	a10AT-X546CH200F	University of Dayton	10	Jamie Peterson	-11802	Attack	Head ball attack	-	Poor, easily dug	X5	Go	NA	NA	NA	4	6	C	NA	Hard spike	2 player block	2	NA	FALSE	FALSE	FALSE	FALSE	1	0	1	6	00F	126	19	9	11	15	10	7	1	16	17	10	6	8	4512	5522	8150	NA	NA	0.55625	3.1296296	0.93125	3.87037	1.98125	5.796296	-296094	-225496	-278838	-224838	-75967	-224837	-231069	14555	-11804	-11802	-230139	-232525	1	3	University of Louisville	University of Dayton	17	42	42	University of Louisville	FALSE	University of Louisville	Reception	0	0
a9e6117d72bb9cf6065ceb32112aa955	0	NA	1	499	*11BT+~~2C~00	University of Louisville	11	Anna Stevenson	-278838	Block	Head ball block	+	Positive, block touch	NA	NA	NA	NA	NA	NA	2	C	NA	NA	NA	NA	NA	FALSE	FALSE	FALSE	FALSE	1	0	1	6	00	127	19	9	11	15	10	7	1	16	17	10	6	8	4578	NA	NA	NA	NA	3.03125	3.1296296	NA	NA	NA	NA	-296094	-225496	-278838	-224838	-75967	-224837	-231069	14555	-11804	-11802	-230139	-232525	1	4	University of Louisville	University of Dayton	17	42	17	University of Louisville	FALSE	University of Louisville	Reception	0	0

R - Reading plays from multiple files

Code

dvw_path_folder <- path.expand("~/dvws")
d <- dir(dvw_path_folder, pattern = "dvw$", full.names = TRUE)
lx <- lapply(d, dv_read, insert_technical_timeouts = FALSE)
px <- bind_rows(lapply(lx, plays))
match_count <- px %>% distinct(match_id)
knitr::kable(match_count)

match_id
2d8ebf675e98a874f0ab4f572ab78367
4e567edc1747c7b8243b731599ee7688
6f92782e87acbf537e9203740e728a7b
bd1e1273f6e6dc83b46f28f42dc45128