Aggregate NBA Player-Tracking Stats Across Multiple Seasons

Fetches leaguedashptstats dashboards for each combination of seasons and season_types, then aggregates the results by entity (PLAYER_ID for Player, TEAM_ID for Team) following the contract ported from sportsdataverse-py's aggregate_tracking_frames:

• Identity columns (*_ID, *_NAME, *_ABBREVIATION, and any other non-numeric string column such as TEAM_CITY) are carried via first() — never summed.

• *_FG_PCT / *_FT_PCT are recomputed from the summed FGM/FGA and FTM/FTA denominators (denom 0 → NA); a *_FG_PCT / *_FT_PCT column whose makes/attempts pair is absent is dropped rather than recomputed.

• Other *_PCT columns (e.g., DRIVE_PTS_PCT — "% of total" rates) are dropped from the output because they are not additive.

• All remaining numeric columns are summed (numeric-content gate, mirroring the sportsdataverse-py dtype.is_numeric() rule).

Usage

nba_tracking_aggregate(
  seasons,
  season_types = "Regular Season",
  per_mode = "Totals",
  pt_measure_type = "Drives",
  player_or_team = "Player",
  league_id = "00",
  ...
)

Arguments

seasons

Character vector of NBA season strings, e.g. c("2022-23", "2023-24").

season_types

Character vector of season type(s), e.g. "Regular Season" or c("Regular Season", "Playoffs"). Defaults to "Regular Season".

per_mode

Per-mode string passed to nba_leaguedashptstats (default "Totals"). Use "Totals" for aggregation; per-game averages are not meaningfully additive.

pt_measure_type

Tracking measure type string, e.g. "Drives", "SpeedDistance", "Passing". Passed directly to nba_leaguedashptstats. Default "Drives".

player_or_team

"Player" (default) or "Team". Determines the entity key used to group aggregation (PLAYER_ID or TEAM_ID).

league_id

League identifier string. Default "00" (NBA).

...

Additional arguments forwarded to nba_leaguedashptstats (e.g., date_from, date_to, conference).

Value

A data.frame (tibble-compatible) with one row per entity (PLAYER_ID or TEAM_ID). Count columns are summed across all requested seasons/season types; *_FG_PCT and *_FT_PCT are recomputed from summed numerators/denominators; *_PCT "% of total" columns are dropped; identity string columns are carried from the first season. Returns a 0-row frame when no data are available.

Details

Aggregate NBA Player-Tracking Stats Across Multiple Seasons

 nba_tracking_aggregate(seasons = c("2022-23", "2023-24"),
                        pt_measure_type = "Drives",
                        player_or_team  = "Player")

Note

Designed and validated for COUNT-based tracking measures (e.g. "Drives"). Rate-only columns without a _pct suffix in some pt_measure_type values are summed across seasons (a known limitation shared with the sportsdataverse reference engine); verify aggregation semantics before using rate-heavy measures.

See also

Other NBA Player Tracking Functions: nba_boxscoreplayertrackv2(), nba_leaguedashoppptshot(), nba_leaguedashplayerptshot(), nba_leaguedashptdefend(), nba_leaguedashptstats(), nba_leaguedashptteamdefend(), nba_leaguedashteamptshot(), nba_playerdashptpass(), nba_playerdashptreb(), nba_playerdashptshotdefend(), nba_playerdashptshots(), nba_synergyplaytypes(), nba_teamdashptpass(), nba_teamdashptreb(), nba_teamdashptshots()

Author

Saiem Gilani

Examples

# \donttest{
  try({
    df <- nba_tracking_aggregate(
      seasons = c("2022-23", "2023-24"),
      pt_measure_type = "Drives",
      player_or_team  = "Player"
    )
    print(df[1:5, c("PLAYER_ID", "PLAYER_NAME", "DRIVES", "DRIVE_FG_PCT")])
  })
#> ✖ 2026-06-30 20:01:04.17127: Invalid arguments or no league dashboard player-tracking stats data for 2022-23 available!
#> ✖ Args: college = "", conference = "", country = "", date_from = "", date_to = "", division = "", draft_pick = "", draft_year = "", game_scope = "", height = "", last_n_games = 0, league_id = "00", location = "", month = 0, opponent_team_id = 0, outcome = "", po_round = "", per_mode = "Totals", period = "", player_experience = "", player_or_team = "Player", player_position = "", pt_measure_type = "Drives", season = "2022-23", season_segment = "", season_type = "Regular Season", starter_bench = "", team_id = "", vs_conference = "", vs_division = "", weight = ""
#> ✖ Error: Failed to perform HTTP request. Caused by error in `curl::curl_fetch_memory()`: ! Timeout was reached [stats.nba.com]: Operation timed out after 60001 milliseconds with 0 bytes received
#> ✖ 2026-06-30 20:02:04.20747: Invalid arguments or no league dashboard player-tracking stats data for 2023-24 available!
#> ✖ Args: college = "", conference = "", country = "", date_from = "", date_to = "", division = "", draft_pick = "", draft_year = "", game_scope = "", height = "", last_n_games = 0, league_id = "00", location = "", month = 0, opponent_team_id = 0, outcome = "", po_round = "", per_mode = "Totals", period = "", player_experience = "", player_or_team = "Player", player_position = "", pt_measure_type = "Drives", season = "2023-24", season_segment = "", season_type = "Regular Season", starter_bench = "", team_id = "", vs_conference = "", vs_division = "", weight = ""
#> ✖ Error: Failed to perform HTTP request. Caused by error in `curl::curl_fetch_memory()`: ! Timeout was reached [stats.nba.com]: Operation timed out after 60001 milliseconds with 0 bytes received
#> Error in df[1:5, c("PLAYER_ID", "PLAYER_NAME", "DRIVES", "DRIVE_FG_PCT")] : 
#>   Can't subset columns that don't exist.
#> ✖ Columns `PLAYER_ID`, `PLAYER_NAME`, `DRIVES`, and `DRIVE_FG_PCT` don't exist.
# }