Fit a Ridge-Regression RAPM Model from Possession Data

Fit a Ridge-Regression RAPM Model from Possession Data

Usage

nba_rapm(possessions, ...)

Arguments

possessions: A possession-level stint matrix as produced by nba_possession_lineups(), with columns off_player_1 through off_player_5, def_player_1 through def_player_5 (integer NBA Stats person IDs), and points (numeric, points scored on that possession).
...: Reserved for future keyword arguments (currently ignored).

Value

Returns a data.frame with one row per player:

col_name	types	description
player_id	integer	NBA 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 (~5,000–8,000) is needed for statistically meaningful estimates. Results from a single game (~150–250 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.

Details

 poss <- nba_possession_lineups(game_id = "0022200001")
 nba_rapm(poss)

Author

Saiem Gilani