Skip to content

Instantly share code, notes, and snippets.

Created November 29, 2023 09:25
Show Gist options
  • Save mrcaseb/d26dcf0236c1787f7c41dc095f8cb32c to your computer and use it in GitHub Desktop.
Save mrcaseb/d26dcf0236c1787f7c41dc095f8cb32c to your computer and use it in GitHub Desktop.
How to bring Statsbomb tracking data into a tidy form
# Go to
# and download the zipped json files containing the individual seasons, available via AWS S3
# Alternatively click the below links directly
# Unzip those files to a local directory. That directory will include deeply nested json files
# Loop over those files and put their path in the below function. The function will
# parse the nested json and either return a tidy data frame into memory (don't do this) or
# write it to disc as compressed csv (please do this)
# Let's say you have all the statsbomb json files saved in the folder "data-raw/raw_sb"
# and you want parsed csv files in the folder "data-raw/stats-bomb-parsed",
# then you could run something like
# purrr::walk(list.files("data-raw/raw_sb/"), parse_statsbomb_tracking, write_to = "data-raw/stats-bomb-parsed")
# NB: An average machine needs 25-30 seconds to process one game. The 2021 and 2022
# data consist of 19+18 = 37 games so this could easily run 20 minutes!!
#' @export
parse_statsbomb_tracking <- function(path_to_json,
write_to = "data-raw/stats-bomb-tracking"
stopifnot(exprs = {
"Can only parse one game at a time" = length(path_to_json) == 1
"write_to must be a valid local directory or NULL" = is.null(write_to) || dir.exists(write_to)
g <- basename(path_to_json) |> tools::file_path_sans_ext()
msg = "Processing {.pkg {g}}. Please keep cool, this may take a while...",
msg_done = "Processed {.pkg {g}}"
raw <- RJSONIO::fromJSON(path_to_json, simplify = FALSE)
game_info <- purrr::discard(raw, is.list) |>
tibble::as_tibble() |>
home = tibble::as_tibble(raw$home_team),
away = tibble::as_tibble(raw$away_team)
) |>
tidyr::unpack(c("home", "away"), names_sep = "_")
df <- tibble::tibble(r = raw$plays) |>
tidyr::unnest_wider(r) |>
# Number of plays times 22, because data per player on the field
tidyr::unnest_longer(tracks) |>
# add track info with 8 additional columns
tidyr::unnest_wider(tracks, names_sep = "_") |>
# dd player info with 5 additional columns
tidyr::unnest_wider(tracks_player) |>
# Now extend by the frames of each player.
# The number of frames varies per play AND per player
tidyr::unnest_longer(tracks_steps) |>
# the tracking data is now again in a list that needs to be expanded
# Bind with game info and finish
out <- dplyr::bind_cols(game_info, df)
rm(raw, game_info, df)
if (!is.null(write_to) && dir.exists(write_to)){
game_id <- out$nfl_game_id[[1]]
save_path <- file.path(write_to, paste0(game_id, ".csv.gz"))
data.table::fwrite(out, save_path)
} else {
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment