- Added playoff play-by-play and shotdetail data for all seasons
- Added folder
loadingwith scripts for data collection - Added folder
build_datasetwith scripts for build files dataset directory - Season 2022/23 added in Kaggle dataset and Google Drive archive
Dataset contains play-by-play data from three sources: stats.nba.com, data.nba.com and pbpstats.com and also shots details. Dataset contains data from season 1996/97 for stats.nba.com and shotdetails, from season 2000/01 for pbpstats.com and from season 2016/17 for data.nba.com.
Data collected with scripts, which are located in loading folder. More info about loading data you can read in README file in these folder.
Detailed description data can be read in file description_fields.md.
Ryan Davis - Analyze the Play by Play Data
Python nba_api package for work with NBA API - https://github.com/swar/nba_api
R hoopR package for work with NBA API - https://hoopr.sportsdataverse.org/
I made this dataset because I want to simplify and speed up work with play-by-play data so that researchers spend their time studying data, not collecting it. Due to the limits on requests on the NBA website, and also because you can get play-by-play of only one game per request, collecting this data is a very long process.
Using this dataset, you can reduce the time to get information about one season from a few hours to a couple of seconds and spend more time analyzing data or building models.
I also added play-by-play information from other sources: pbpstats.com (there is information about the time of ownership and the type of its beginning) and data.nba.com (there you can find coordinates of actions on court). This data will enrich information about the progress of each game and hopefully add opportunities to do interesting things.
You can download dataset several ways:
Clone git repository to your device
git clone https://github.com/shufinskiy/nba_data.git
Download using R or Python
You can write your own loading functions or use ones I wrote for R and Python languages.
R:
load_nba_data <- function(path = getwd(),
seasons = seq(1996, 2023),
data = c("datanba", "nbastats", "pbpstats", "shotdetail", "cdnnba", "nbastatsv3"),
seasontype = 'rg',
untar = FALSE){
if(seasontype == 'rg'){
df <- expand.grid(data, seasons)
need_data <- paste(df$Var1, df$Var2, sep = "_")
} else if(seasontype == 'po'){
df <- expand.grid(data, 'po', seasons)
need_data <- paste(df$Var1, df$Var2, df$Var3, sep = "_")
} else {
df_rg <- expand.grid(data, seasons)
df_po <- expand.grid(data, 'po', seasons)
need_data <- c(paste(df_rg$Var1, df_rg$Var2, sep = "_"), paste(df_po$Var1, df_po$Var2, df_po$Var3, sep = "_"))
}
temp <- tempfile()
download.file("https://raw.githubusercontent.com/shufinskiy/nba_data/main/list_data.txt", temp)
f <- readLines(temp)
unlink(temp)
v <- unlist(strsplit(f, "="))
name_v <- v[seq(1, length(v), 2)]
element_v <- v[seq(2, length(v), 2)]
need_name <- name_v[which(name_v %in% need_data)]
need_element <- element_v[which(name_v %in% need_data)]
if(!dir.exists(path)){
dir.create(path)
}
for(i in seq_along(need_element)){
destfile <- paste0(path, '/', need_name[i], ".tar.xz")
download.file(need_element[i], destfile = destfile)
if(untar){
untar(destfile, paste0(need_name[i], ".csv"), exdir = path)
unlink(destfile)
}
}
}Python:
For download data in Python, you can use the nba-on-court package
from pathlib import Path
from itertools import product
import urllib.request
import tarfile
from typing import Union, Sequence
def load_nba_data(path: Union[Path, str] = Path.cwd(),
seasons: Union[Sequence, int] = range(1996, 2023),
data: Union[Sequence, str] = ("datanba", "nbastats", "pbpstats",
"shotdetail", "cdnnba", "nbastatsv3"),
seasontype: str = 'rg',
untar: bool = False) -> None:
"""
Loading a nba play-by-play dataset from github repository https://github.com/shufinskiy/nba_data
Args:
path (Union[Path, str]): Path where downloaded file should be saved on the hard disk
seasons (Union[Sequence, int]): Sequence or integer of the year of start of season
data (Union[Sequence, str]): Sequence or string of data types to load
seasontype (str): Part of season: rg - Regular Season, po - Playoffs
untar (bool): Logical: do need to untar loaded archive
Returns:
None
"""
if isinstance(path, str):
path = Path(path)
if isinstance(seasons, int):
seasons = (seasons,)
if isinstance(data, str):
data = (data,)
if seasontype == 'rg':
need_data = tuple(["_".join([data, str(season)]) for (data, season) in product(data, seasons)])
elif seasontype == 'po':
need_data = tuple(["_".join([data, seasontype, str(season)]) \
for (data, seasontype, season) in product(data, (seasontype,), seasons)])
else:
need_data_rg = tuple(["_".join([data, str(season)]) for (data, season) in product(data, seasons)])
need_data_po = tuple(["_".join([data, seasontype, str(season)]) \
for (data, seasontype, season) in product(data, ('po',), seasons)])
need_data = need_data_rg + need_data_po
with urllib.request.urlopen("https://raw.githubusercontent.com/shufinskiy/nba_data/main/list_data.txt") as f:
v = f.read().decode('utf-8').strip()
name_v = [string.split("=")[0] for string in v.split("\n")]
element_v = [string.split("=")[1] for string in v.split("\n")]
need_name = [name for name in name_v if name in need_data]
need_element = [element for (name, element) in zip(name_v, element_v) if name in need_data]
for i in range(len(need_name)):
t = urllib.request.urlopen(need_element[i])
with path.joinpath("".join([need_name[i], ".tar.xz"])).open(mode='wb') as f:
f.write(t.read())
if untar:
with tarfile.open(path.joinpath("".join([need_name[i], ".tar.xz"]))) as f:
f.extract("".join([need_name[i], ".csv"]), path)
path.joinpath("".join([need_name[i], ".tar.xz"])).unlink()Kaggle notebook with examples work with dataset (R)
Download from Google Drive
You can also download full version of the dataset from GoogleDrive.
If you have questions or proposal about dataset, you can write me convenient for you in a way.
