Fit a Ridge-Regression RAPM Model from WNBA Possession Data
Fit a Ridge-Regression RAPM Model from WNBA Possession Data
wnba_rapm(possessions, ...)Returns a data.frame with one row per player:
| col_name | types | description |
| player_id | integer | WNBA Stats person ID. Rows are sorted ascending by player_id. |
| o_rapm | numeric | Offensive RAPM (per-100-possession points added on offense). Positive = better offensive player. |
| d_rapm | numeric | Defensive RAPM (per-100-possession points saved on defense). Positive = better defensive player (sign is flipped so good defense is positive). |
| rapm | numeric | Total RAPM = o_rapm + d_rapm. Positive = net positive impact. |
| off_poss | integer | Number of possessions the player appeared on offense. |
| def_poss | integer | Number of possessions the player appeared on defense. |
Returns a 0-row frame with the same schema when input is empty or all possessions have NA lineup cells (never-raise).
Note: RAPM is expressed in per-100-possession units. A full season
of possessions (~3,000–5,000) is needed for statistically meaningful estimates. Results from a single game (~120–200 possessions) are highly unstable and are provided here for pipeline illustration only.
Results are deterministic: the cross-validation uses fixed, construction-based folds (not random), so repeated calls on the same possessions return identical output with no need to set a seed.
A possession-level stint matrix as produced by
wnba_possession_lineups(), with columns off_player_1 through
off_player_5, def_player_1 through def_player_5
(integer WNBA Stats person IDs), and points (numeric, points scored
on that possession).
Reserved for future keyword arguments (currently ignored).
Saiem Gilani
Other WNBA Lineup Functions:
wnba_gamerotation(),
wnba_leaguedashlineups(),
wnba_leaguelineupviz(),
wnba_possession_lineups(),
wnba_shot_zones(),
wnba_shotchartlineupdetail(),
wnba_teamdashlineups()