/nflfastR-data

Data scraped from nflfastR package

Primary LanguageR

nflfastR-data

NFL play-by-play data scraped from the nflfastR package going back to 1999. Each season contains both regular season and postseason data, with game_type or week denoting which.

Data are stored in the data folder, available as either compressed csv (.csv.gz) .rds, or .parquet.

Note that the data come from two separate sources and all player IDs are inconsistent across the old (1999 - 2010) and new (2011 onward) formats.

Load data using R

If you're using R, you might as well load the data in the binary .rds format. The following example shows how to load the seasons 2010 to 2019 (binded into a single dataframe).

# define which seasons shall be loaded
seasons <- 2010:2019
pbp <- purrr::map_df(seasons, function(x) {
  readRDS(
    url(
      glue::glue("https://raw.githubusercontent.com/guga31bb/nflfastR-data/master/data/play_by_play_{x}.rds")
    )
  )
})

However, if you want to load the compressed csv data run this:

# define which seasons shall be loaded
seasons <- 2010:2019
pbp <- purrr::map_df(seasons, function(x) {
  readr::read_csv(
    glue::glue("https://raw.githubusercontent.com/guga31bb/nflfastR-data/master/data/play_by_play_{x}.csv.gz")
  )
})

Or you can read .parquet like this:

# define which seasons shall be loaded
seasons <- 2010:2019
pbp <- purrr::map_df(seasons, function(x) {
  download.file(glue::glue("https://raw.githubusercontent.com/guga31bb/nflfastR-data/master/data/play_by_play_{x}.parquet"), "tmp.parquet")
  df <- arrow::read_parquet("tmp.parquet")
  return(df)
}
)

Load data using Python

If you are using Python you can load the compressed csv data. The following example written by Deryck (thanks a lot!) loads the seasons 2017 to 2019 (binded into a single pandas dataframe) as well as rosters (from 2000 to latest season):

import pandas as pd 

#Enter desired years of data
YEARS = [2019,2018,2017]

data = pd.DataFrame()

for i in YEARS:  
    #low_memory=False eliminates a warning
    i_data = pd.read_csv('https://github.com/guga31bb/nflfastR-data/blob/master/data/' \
                         'play_by_play_' + str(i) + '.csv.gz?raw=True',
                         compression='gzip', low_memory=False)

    #sort=True eliminates a warning and alphabetically sorts columns
    data = data.append(i_data, sort=True)

#Give each row a unique index
data.reset_index(drop=True, inplace=True)