Load wehoop WNBA play-by-play — load_wnba

helper that loads multiple seasons from the data repo either into memory or writes it into a db using some forwarded arguments in the dots

Usage

load_wnba_pbp(
  seasons = most_recent_wnba_season(),
  ...,
  dbConnection = NULL,
  tablename = NULL
)

Arguments

seasons: A vector of 4-digit years associated with given WNBA seasons. (Min: 2002)
...: Additional arguments passed to an underlying function that writes the season data into a database (used by update_wnba_db()).
dbConnection: A DBIConnection object, as returned by DBI::dbConnect()
tablename: The name of the play by play data table within the database

Value

A dataframe with 42 columns

shooting_play: Logical value (TRUE/FALSE) indicating whether the play was a shooting play
sequence_number: Sequence number is supposed to represent a shot-possession, examine the last two numbers to see if there are multiple events that occur within the same shot-possession. A shot-possession is basically any sequence of plays until there is a shot, change in possession, and probably things like technical fouls and the like. So as soon as a shot goes up, a new sequence starts regardless, even if the shooting team retains possession via offensive or deadball rebound. The first portion of the number is usually time related (i.e. the numeric representation of when the sequence started, from a seconds remaining in the period perspective or so)
period_display_value: Long form of period (1st quarter, 2nd Quarter, OT, etc.)
period_number: The numeric period of play in the game
home_score: Home score at the time of the play
coordinate_x: The entire scale is a rectangle of size 25x47, intended as a half-court representation of the basketball court (i.e. on the side of the offense), with each coordinate unit representing a foot. It appears that the basket is roughly represented as the (25, 0) point. This is a nonsensical definition when considering that the basket overhangs the court, with the backboard aligned 48 inches from the baseline, then the center of the hoop being roughly 11 inches from there. This is an idiosyncracy of either sensor placement or software and data entry. Use your best judgement in making your charts, I think you will find that making some translations will be helpful.
coordinate_y
scoring_play: Logical value (TRUE/FALSE) indicating whether the play was a play on which the offense scored
clock_display_value: Time left within the period
team_id: Unique team identification number for the offensive team
type_id: Unique play type identifcation number
type_text: Play type text description
away_score: Away score at the time of the play
id: Unique play identifcation number
text: Text description of the play
score_value: The points value of the shot taken
participants_0_athlete_id: Unique player identification number
participants_1_athlete_id: Unique player identification number
participants_2_athlete_id: Unique player identification number
type_abbreviation: Play type abbreviation
season: Season of the game
season_type: Season type of the game, 1 is pre-season, 2 is regular season, 3 is post-season, 4 is off-season
away_team_id: Unique away team identification number
away_team_name: Away team name
away_team_mascot: Away team mascot
away_team_abbrev: Text abbreviation for the away team
away_team_name_alt: Alternate versions of the away team abbreviation
home_team_id: Unique home team identification number
home_team_name: home team name
home_team_mascot: home team mascot
home_team_abbrev: Text abbreviation for the home team
home_team_name_alt: Alternate versions of the home team abbreviation
home_team_spread: The game spread with respect to the home team
game_spread: Game spread in (-X Team) format. There are almost none, I would recommend not trusting any of these three columns
home_favorite: Logical (TRUE/FALSE) indicating whether the home team is favored
clock_minutes: Clock minutes split from seconds for developer convenience
clock_seconds: Clock seconds split from minutes for developer convenience
half: Half of the game
lag_half: A lag column on the half
lead_half: A lead column on the half
game_play_number: Game play number
game_id: Unique identifier for the game event

Examples

# \donttest{
  try(load_wnba_pbp())
#> ──────────────────────────────────────────────────────────────── wehoop 2.0.0 ──
#> # A tibble: 102,191 × 62
#>    game_play_number        id sequence_number type_id type_text text  away_score
#>               <int>     <dbl>           <int>   <int> <chr>     <chr>      <int>
#>  1                1   4.02e 9               4     615 Jumpball  A'ja…          0
#>  2                2   4.02e 9               7     110 Driving … A'ja…          2
#>  3                3   4.02e 9               9      90 Out of B… Cour…          2
#>  4                4   4.02e10              10     120 Turnarou… Alys…          4
#>  5                5   4.02e10              11      92 Jump Shot Betn…          4
#>  6                6   4.02e10              13      92 Jump Shot Cayl…          4
#>  7                7   4.02e10              14     155 Defensiv… Jonq…          4
#>  8                8   4.02e10              15     143 Running … Brea…          4
#>  9                9   4.02e10              16     155 Defensiv… A'ja…          4
#> 10               10   4.02e10              17      90 Out of B… A'ja…          4
#> # ℹ 102,181 more rows
#> # ℹ 55 more variables: home_score <int>, period_number <int>,
#> #   period_display_value <chr>, clock_display_value <chr>, scoring_play <lgl>,
#> #   score_value <int>, team_id <int>, athlete_id_1 <int>, athlete_id_2 <int>,
#> #   athlete_id_3 <int>, wallclock <chr>, shooting_play <lgl>,
#> #   coordinate_x_raw <dbl>, coordinate_y_raw <dbl>, game_id <int>,
#> #   season <int>, season_type <int>, home_team_id <int>, …
# }