Dataset: NBA Player Salaries (2022-23 Season)

About this Dataset

NBA Player Salaries (2022-23 Season)

This dataset merges player per-game and advanced statistics for the NBA's 2022-23 season with player salary data, creating a comprehensive resource for understanding the performance and financial aspects of professional basketball players. The dataset is the result of web scraping player salary information from Hoopshype, and downloading traditional per-game and advanced statistics from Basketball Reference.

Key Features:

Player Information: Player name, team(s) played for during the season.
Per Game Statistics: A wide array of per-game statistics, including points scored (PPG), assists (APG), rebounds (RPG), steals (SPG), blocks (BPG), and more.
Shooting Efficiency: Metrics like field goal percentage (FG%), three-point percentage (3P%), two-point percentage (2P%), and free throw percentage (FT%) for assessing scoring efficiency.
Advanced Statistics: A wide array of advanced metrics such as value over replacement player (VORP), win shares (WS) and true shooting percentage (TS%)
Salaries: The financial aspect of the dataset includes player salaries for the 2022-23 season, offering insights into player earning

Potential Uses:

Player Performance Analysis: Analysts can evaluate player performance in correlation with their earnings, identifying players who provide strong value for their salaries.
Team Budgeting and Strategy: Teams can use this dataset to assess the financial implications of player acquisitions, trades, and salary cap management.
Player Earnings Insights: The dataset offers insights into the income distribution of NBA players, helping fans and analysts understand the financial side of professional basketball.
Data-Driven Decisions: Data scientists can leverage this combined dataset for predictive modeling and machine learning applications, including salary predictions and player valuation.

Acknowledgements: Basketball Reference, Hoopshype

Tables

Nba 2022–23 All Stats With Salary

@kaggle.jamiewelsh2_nba_player_salaries_2022_23_season.nba_2022_23_all_stats_with_salary

93.68 KB
467 rows
52 columns


CREATE TABLE nba_2022_23_all_stats_with_salary (
  "unnamed_0" BIGINT,
  "player_name" VARCHAR,
  "salary" BIGINT,
  "position" VARCHAR,
  "age" BIGINT,
  "team" VARCHAR,
  "gp" BIGINT,
  "gs" BIGINT,
  "mp" DOUBLE,
  "fg" DOUBLE,
  "fga" DOUBLE,
  "fg_51ab2d" DOUBLE,
  "n_3p" DOUBLE,
  "n_3pa" DOUBLE,
  "n_3p_d87a85" DOUBLE,
  "n_2p" DOUBLE,
  "n_2pa" DOUBLE,
  "n_2p_c8658d" DOUBLE,
  "efg" DOUBLE,
  "ft" DOUBLE,
  "fta" DOUBLE,
  "ft_ba25d5" DOUBLE,
  "orb" DOUBLE,
  "drb" DOUBLE,
  "trb" DOUBLE,
  "ast" DOUBLE,
  "stl" DOUBLE,
  "blk" DOUBLE,
  "tov" DOUBLE,
  "pf" DOUBLE,
  "pts" DOUBLE,
  "total_minutes" BIGINT,
  "per" DOUBLE,
  "ts" DOUBLE,
  "n_3par" DOUBLE,
  "ftr" DOUBLE,
  "orb_6d1d9b" DOUBLE,
  "drb_f91726" DOUBLE,
  "trb_08d3e9" DOUBLE,
  "ast_d11618" DOUBLE,
  "stl_d4de98" DOUBLE,
  "blk_d028d3" DOUBLE,
  "tov_8a121e" DOUBLE,
  "usg" DOUBLE,
  "ows" DOUBLE,
  "dws" DOUBLE,
  "ws" DOUBLE,
  "ws_48" DOUBLE,
  "obpm" DOUBLE,
  "dbpm" DOUBLE,
  "bpm" DOUBLE,
  "vorp" DOUBLE
);

Nba Salaries

@kaggle.jamiewelsh2_nba_player_salaries_2022_23_season.nba_salaries

60.69 KB
467 rows
32 columns


CREATE TABLE nba_salaries (
  "unnamed_0" BIGINT,
  "player_name" VARCHAR,
  "salary" BIGINT,
  "position" VARCHAR,
  "age" BIGINT,
  "team" VARCHAR,
  "gp" BIGINT,
  "gs" BIGINT,
  "mp" DOUBLE,
  "fg" DOUBLE,
  "fga" DOUBLE,
  "fg_51ab2d" DOUBLE,
  "n_3p" DOUBLE,
  "n_3pa" DOUBLE,
  "n_3p_d87a85" DOUBLE,
  "n_2p" DOUBLE,
  "n_2pa" DOUBLE,
  "n_2p_c8658d" DOUBLE,
  "efg" DOUBLE,
  "ft" DOUBLE,
  "fta" DOUBLE,
  "ft_ba25d5" DOUBLE,
  "orb" DOUBLE,
  "drb" DOUBLE,
  "trb" DOUBLE,
  "ast" DOUBLE,
  "stl" DOUBLE,
  "blk" DOUBLE,
  "tov" DOUBLE,
  "pf" DOUBLE,
  "pts" DOUBLE,
  "player_additional" VARCHAR
);