NBA RAPM Datasets

Datasets to build regularized adjusted plus-minus (RAPM) models for NBA players during the 2022-2023 regular season

Motivation

In the National Basketball Association (NBA), traditional box-score statistics provide a limited view of a player’s performance. In order to measure an individual player’s contribution, it is necessary to adjust for the presence of their teammates and opposition. Different versions of regularized adjusted plus-minus (RAPM) models are popular approaches in the basketball analytics community for attempting to address this challenge. With the data provided here, you can explore building a RAPM model for NBA players in an attempt to estimate an individual player’s effect when on the court.

Data

There are three datasets below that are relevant for building RAPM models for player performances during the 2022-2023 NBA regular season. All of the data were collected using the hoopR package in R.

The first dataset nba_2223_season_stints.csv contains information about 32,358 unique stints during NBA games in which the same ten players (home and away) were on the court together prior to substitutions taking place. This dataset contains the following columns:

nba_2223_season_stints.csv

Variable	Description
game_id	Unique game ID
stint_id	Unique identifier within a game for a stint for particular combination of home and away lineup (in appearance of order, where 1 is the first stint in the game)
home_lineup	Concatenated string of the five home player IDs, separated by _
away_lineup	Concatenated string of the five away player IDs, separated by _
n_pos	Number of possessions (combined for both home and away) during the observed stint
home_points	Number of points scored by the home team during the stint
away_points	Number of points scored by the away team during the stint
minutes	Length of the stint in terms of minutes played
margin	Common response for RAPM models defined as: (home_points - away_points) / n_pos * 100

The second dataset nba_2223_season_rapm_data.csv.gz is the wide design matrix version of the above dataset. This file contains the same 32,358 unique stints, but replaces the home_lineup and away_lineup columns with 539 columns for the unique players that were observed during the course of the season. If the player was on the court for home team, a 1 is observed in their column. If a player was on the court for the away team, a -1 is observed in their column. Otherwise, if the player was not on the court, a 0 is observed.

And for context, the third dataset nba_2223_player_table.csv contains one row for each of the 539 players in the data with two columns:

nba_2223_player_table.csv

Variable	Description
player_id	Unique player ID that are the names of the RAPM data columns
player_name	First and last name for the player

Questions

1. Build a RAPM model! (More will be added to this later…)

References

Gilani S (2023). hoopR: Access Men’s Basketball Play by Play Data. R package version 2.1.0, https://CRAN.R-project.org/package=hoopR.