In 2024/25, the Golden State Warriors generated the most revenue from the National Basketball Association franchises. Specifically, the Golden State Warriors generated 880 million U.S. dollars in revenue by the end of the season.

We note that there are approximately half the number of players in this dataset compared to the baseball dataset. Also, a higher percentage of players transition annually in the NBA (on average about 58%). A high amount of players switch teams each year, about 67%, while only 32% retire.

Greetings!

If you are reading this then you might be an NBA junkie like me. I always wanted to have access to some data pertaining to my favorite players and teams across the years, and here i've tried to compile and accumulate data i could get hands on since 1995.

A lot of the columns have been kept as raw as possible, but with additions like:

• Adding a season column to indicate which season it is relevant for, and will help doing aggregations across different years
• Adding a team column to indicate the team that the datapoints were relevant for. Makes making aggregations over time on a team level a bit easier
• Adding a team_retconcolumn which will map franchise renames to reflect their current date team name.

Note that duplicate player entries for a given season indicates a trade or switch of teams! Have fun!

About the data

There are 7 parquet files in this dataset:

1. total.parq is a parquet file containing regular season stat totals per player for each team for observed years 1995-2023
2. total_playoffs.parq is a parquet file containing playoff stat totals per player and for each team for observed years 1995-2023
3. advanced.parq is a parquet file containing regular seasonal advanced stats (re: VOIP) per player for each team for observed years 1995-2023
4. advanced_playoffs.parq is a parquet file containing more playoff advanced stats (re: VOIP) per player for each team for observed years 1995-2023
5. average.parq is a parquet file containing regular season stats averages per player for each team for observed years 1995-2023
6. average_playoffs.parq is a parquet file containing playoff stat averages per player for each team for observed years 1995-2023
7. roster.parq is a parquet file containing the roster per team for seasons 1995-2023

Loading the data

If you are familiar with pandas, it is just as easy to read a parquet file as it is reading a standard csv file. The compression and space occupancy for parquet is however much lower!

you can load it by simply writing:

import pandas as pd
df= pd.read_parquet('total.parq')

in a notebook.

All data is sourced and can be found at basketball-reference.com

Taken from the NBA.com the 2023-2024 season stats averages for individual players as of January 10th. This consists of game averages in the typical categories like points, rebounds, assists, steals, and blocks. It also has shooting percentages from the field, from beyond the arc, and from the free-throw line. Could be paired with team defensive statistics for fun predictability models regarding a players performance in the next game.

Update 2023-10-13: The data now includes 2022 season.

Update 2022-08-06: The data now includes 2021 season.

Update 2021-08-02: The data now includes 2020 season and metrics for 2019 have been updated.

Update 2020-08-03: The data now includes 2017, 2018 and 2019 seasons. Keep in mind that metrics like gp, pts, reb, etc. are not complete for 2019 season, as it is ongoing at the time of upload.

Context

As a life-long fan of basketball, I always wanted to combine my enthusiasm for the sport with passion for analytics 🏀📊. So, I utilized the NBA Stats API to pull together this data set. I hope it will prove to be as interesting to work with for you as it has been for me!

Content

The data set contains over two decades of data on each player who has been part of an NBA teams' roster. It captures demographic variables such as age, height, weight and place of birth, biographical details like the team played for, draft year and round. In addition, it has basic box score statistics such as games played, average number of points, rebounds, assists, etc.

The pull initially contained 52 rows of missing data. The gaps have been manually filled using data from Basketball Reference. I am not aware of any other data quality issues.

Analysis Ideas

The data set can be used to explore how age/height/weight tendencies have changed over time due to changes in game philosophy and player development strategies. Also, it could be interesting to see how geographically diverse the NBA is and how oversees talents have influenced it. A longitudinal study on players' career arches can also be performed.

game simulator (basketball): NBA 2023-2024

The aim of this project is to generate simulations of basketball games between NBA finals teams for the 2023-2024 season for the purpose of modeling predicted outcomes from a player efficiency metric (the "r metric").

A simulated 82 game season will be run daily.

2022-2023 box score statistics for players (on a per 100 possessions basis) were gathered from https://www.basketball-reference.com/.

The players stats were filtered and transformed to reflect a focus on box score stats measuring playing efficiency, as opposed to measures of volume. For example, Real Shooting Percentage (True Shooting Percentage adjusted for volume, based on points generated above average) was incorporated into the metric as opposed to Points Per Game; Adjusted Assist to Turnover Ratio (Assist to Turnover adjusted for volume, based on assists to turnovers generated above average) was incorporated as opposed to Assists Per Game. The complete list of stats used for the r metric is as follows:

Real Shooting Percentage
Offensive Rebounds
Adjusted Assist to Turnover Ratio
Steals
Blocks
Personal Fouls 

The r metric efficiency rating was derived from performing a boosted regression on the overall team stats for a selection of teams for NBA seasons from 1980 to the present against their Point Differential and then applying the resulting predicted values to individual players.

An R function was created to generate simulated game outcomes from a Kaggle notebook. The output is produced as a ggplot (visualizing the r metric (in pink) against the traditional box score stats (coded by team in blue/red) and a csv file as a box score. The notebook is scheduled to run daily, randomly selecting teams to play against one another and generating an outcome based on the player stats and metric for each team with an element of random variation.

Dataset Overview: The dataset provides statistical information about EuroLeague basketball teams, covering various seasons. The data includes team performance metrics, efficiency ratings, and other basketball-specific statistics. Each team played 20 games during the season.

Variables: TEAM: The name of the basketball team. Pts: The average points scored per game by the team. FG%: Field Goal Percentage – the success rate of field goal attempts. 3PT%: Three-Point Field Goal Percentage – the success rate of three-point shot attempts. FT%: Free Throw Percentage – the success rate of free throw attempts. TR%: Total Rebound Percentage – the proportion of total rebounds (offensive and defensive) collected by the team. AST%: Assist Percentage – the percentage of field goals assisted by the team. TO Ratio: Turnover Ratio – the number of turnovers per 100 possessions. PACE: The estimated number of possessions per 48 minutes (game pace). OFF RTG: Offensive Rating – the number of points scored per 100 possessions. DEF RTG: Defensive Rating – the number of points allowed per 100 possessions. NET RTG: Net Rating – the difference between Offensive Rating and Defensive Rating. TS%: True Shooting Percentage – a shooting efficiency metric considering field goals, three-pointers, and free throws. Val: A performance evaluation index for teams. TAG_SEASON: A unique identifier for the team and season (e.g., "AEK_2001-2002"). Win: The total number of games won by the team in the given season.

NBA Player Statistics (2000-2009)

This dataset contains the regular season statistics of NBA players from the 2000-2001 season to the 2009-2010 season. The data is presented in separate CSV files for each season. I take this datasets from https://www.nba.com/stats/players/traditional?Season=2000-01&SeasonType=Regular+Season

Features:

Player: Name of the player.

Team: Team the player played for.

GP: Games Played

MIN: Minutes Played

PTS: Points

FGM: Fields Goals Made

FGA: Fields Goals Attempted

FG%: Field Goals Percentage

3PM: 3 Point Field Goals Made

3PA: 3 Point Field Goals Attempted

3P%: 3 Point Field Goals Percentage

FTM: Free Throws Made

FTA: Free Throws Attempted

FT%: Free Throws Percentage

OREB: Offensive Rebounds

DREB: Defensive Rebounds

REB: Rebounds

AST: Assists

STL: Steals

BLK: Blocks

TOV: Turnovers

EFF: Average efficiency rating per game. (PTS + REB + AST + STL + BLK − Missed FG − Missed FT - TO) / GP

Potential Use Cases:

Performance Analysis: Analyze the performance of players over multiple seasons based on various statistics such as points per game, rebounds, assists, etc. Identify top-performing players in different categories. Team Comparison: Compare the performance of different teams over the years. Analyze which teams have been consistently strong or have shown improvement over time. Player Development: Study the development of individual players over their careers. Analyze how their statistics have changed over time and identify factors contributing to their improvement. Predictive Modeling: Build predictive models to forecast player performance or team outcomes based on historical data. Use machine learning algorithms to predict future trends or outcomes. Fantasy Sports: Use the data for fantasy sports analysis and team selection. Create algorithms to optimize fantasy team selection based on player statistics and performance trends. The dataset provides a comprehensive view of player performance in the NBA during the specified period, making it valuable for various analytical and predictive purposes.

Context

• The National Basketball Association (NBA) is an American men's professional basketball league.
• It is composed of 30 teams (29 in the United States and 1 in Canada) and is one of the four major professional sports leagues in the United States and Canada.
• It is the premier men's professional basketball league in the world.
• The league was founded in New York City on June 6, 1946, as the Basketball Association of America (BAA).
• It changed its name to the National Basketball Association on August 3, 1949, after merging with the competing National Basketball League (NBL).
• The NBA's regular season runs from October to April, with each team playing 82 games.

• The league's playoff tournament extends into June. As of 2020, NBA players are the world's best paid athletes by average annual salary per player.

• The NBA is an active member of USA Basketball (USAB), which is recognized by the FIBA (International Basketball Federation) as the national governing body for basketball in the United States.

• The league's several international as well as individual team offices are directed out of its head offices in Midtown Manhattan, while its NBA Entertainment and NBA TV studios are directed out of offices located in Secaucus, New Jersey.

The NBA is the third wealthiest professional sport league after the National Football League (NFL) and Major League Baseball (MLB) by revenue.

Content

1. GM, GP; GS: games played; games started 2.PTS: points 3.FGM, FGA, FG%: field goals made, attempted and percentage 4.FTM, FTA, FT%: free throws made, attempted and percentage 5.3FGM, 3FGA, 3FG%: three-point field goals made, attempted and percentage 6.REB, OREB, DREB: rebounds, offensive rebounds, defensive rebounds 7.AST: assists 8.STL: steals 9.BLK: blocks 10.TO: turnovers 11.EFF: efficiency: NBA's efficiency rating: (PTS + REB + AST + STL + BLK − ((FGA − FGM) + (FTA − FTM) + TO)) 12.PF: personal fouls 13.MIN: minutes 14.AST/TO: assist to turnover ratio 15.PER: Player Efficiency Rating: John Hollinger's Player Efficiency Rating 16.PIR: Performance Index Rating: Euro league's and Euro cup's Performance Index Rating: (Points + Rebounds + Assists + 17.Steals + Blocks + Fouls Drawn) − (Missed Field Goals + Missed Free Throws + Turnovers + Shots Rejected + Fouls Committed)

Inspiration

This is one of the most common questions of everyone's life that how are these basketball players such rich and how are they getting paid, and even the stats about how everything is done on a team.

NBA-Raptor

This folder contains data behind the story Introducing RAPTOR, Our New Metric For The Modern and the interactive The Best NBA Players, According To RAPTOR.

https://user-images.githubusercontent.com/11506653/61015562-1ff45080-a35a-11e9-9b5c-4a824268c3aa.png" alt="nba team">

This dataset contains RAPTOR data for every player broken out by season since 2014 when NBA player-tracking data first became available, the box score and on/off plus-minus components of RAPTOR, which are then combined into a total RAPTOR rating.

These "historical" data files use full player-tracking RAPTOR for seasons since 2014, a version of RAPTOR that mixes box score value estimates with single-year regularized plus-minus data for seasons from 2001 through 2013, and a version of RAPTOR that only uses a box score estimate of value for the seasons from 1977 through 2000. In each era, the RAPTOR version with the highest level of detail is the version used.

a

I manually went through the data for all NBA Champions from 2000-2024. These winners all have something in common, appearing at the top of the ranks in these 5 advanced statistical categories: - Win Percentage: How many games a team has won out of the total games they played - Effective Field Goal Percentage: Total shots made out of attempted, but accounting for the fact that three-pointers are worth more than two-point field goals. - Defensive Efficiency: Points a team allows per 100 possessions. - Opponent Effective Field Goal Percentage: Refer to Effective Field Goal Percentage. - Average Scoring Margin: Average of how many points a team wins or loses by.

Here is the notebook that was used to create these files.

Data source: teamrankings.com

License: MIT

Context

nba-raptor This folder contains data behind the story Introducing RAPTOR, Our New Metric For The Modern NBA and the interactive The Best NBA Players, According To RAPTOR.

modern_RAPTOR_by_player.csv contains RAPTOR data for every player broken out by season since 2014, when NBA player-tracking data first became available.

modern_RAPTOR_by_team.csv contains RAPTOR data for every player broken out by team, season and season_type since 2014, when NBA player-tracking data first became available.

These "modern" data files contain the box score and on/off plus-minus components of RAPTOR, which are then combined into a total RAPTOR rating.

historical_RAPTOR_by_player.csv contains RAPTOR data for every player broken out by season since the 1976 ABA-NBA merger.

historical_RAPTOR_by_team.csv contains RAPTOR data for every player broken out by team, season and season_type since the 1976 ABA-NBA merger.

These "historical" data files use full player-tracking RAPTOR for seasons since 2014, a version of RAPTOR that mixes box score value estimates with single-year regularized plus-minus data for seasons from 2001 through 2013, and a version of RAPTOR that only uses a box score estimate of value for the seasons from 1977 through 2000. In each era, the RAPTOR version with the highest level of detail is the version used.

The linked file latest_RAPTOR_by_player.csv contains RAPTOR data for every player in the latest season.

The linked file latest_RAPTOR_by_team.csv contains RAPTOR data for every player broken out by team, season and season_type for the latest season.

These "latest" data files contain the box score and on/off plus-minus components of RAPTOR, which are then combined into a total RAPTOR rating.

Content

Column Description player_name Player name player_id Basketball-Reference.com player ID season Season season_type Regular season (RS) or playoff (PO) team Basketball-Reference ID of team poss Possessions played mp Minutes played raptor_box_offense Points above average per 100 possessions added by player on offense, based only on box score estimate raptor_box_defense Points above average per 100 possessions added by player on defense, based only on box score estimate raptor_box_total Points above average per 100 possessions added by player, based only on box score estimate raptor_onoff_offense Points above average per 100 possessions added by player on offense, based only on plus-minus data raptor_onoff_defense Points above average per 100 possessions added by player on defense, based only on plus-minus data raptor_onoff_total Points above average per 100 possessions added by player, based only on plus-minus data raptor_offense Points above average per 100 possessions added by player on offense, using both box and on-off components raptor_defense Points above average per 100 possessions added by player on defense, using both box and on-off components raptor_total Points above average per 100 possessions added by player on both offense and defense, using both box and on-off components war_total Wins Above Replacement between regular season and playoffs war_reg_season Wins Above Replacement for regular season war_playoffs Wins Above Replacement for playoffs predator_offense Predictive points above average per 100 possessions added by player on offense predator_defense Predictive points above average per 100 possessions added by player on defense predator_total Predictive points above average per 100 possessions added by player on both offense and defense pace_impact Player impact on team possessions per 48 minutes

NBA Player Stats: 2024-2025 Season Overview Based on the latest available data (as of November 27, 2025, covering the full regular season), here's a summary of key NBA player statistics. This includes top performers and notable players across metrics like points per game (PPG), rebounds per game (RPG), assists per game (APG), and more advanced stats. The data is sourced from official NBA records and focuses on per-game averages and totals for the season. For a quick visual snapshot of selected players' core stats (e.g., points, rebounds, assists, shooting efficiency), see the table below. It highlights a mix of stars, veterans, and role players to give a broad view. (Note: Full datasets have 400+ players; this is a curated sample.) Key Insights from the 2024-2025 Season:

Scoring Leaders: Giannis Antetokounmpo (MIL) led with 30.4 PPG, followed closely by Kevin Durant (PHX) at 26.6 PPG and Anthony Davis (TOT) at 24.7 PPG. Stephen Curry (GSW) was the 3-point king with 4.4 3PM per game. Rebounding Standouts: Rudy Gobert (MIN) dominated with 10.9 RPG, while Anthony Davis added 11.6 RPG on a high-usage forward. Playmaking: James Harden (LAC) averaged 8.7 APG, with LeBron James (LAL) close behind at 8.2 APG in his continued elite form. Efficiency: Gobert topped true shooting % (TS%) at 68.7%, showcasing paint dominance. Curry's FT% hit an impressive 93.3%. Advanced Metrics: Players like Draymond Green (GSW) excelled in defensive win shares (DWS) and plus/minus, contributing to team success without high scoring. Season Notes: Injuries impacted stars like Paul George (limited to 41 GP) and Kyrie Irving (50 GP), but rookies and depth players like those on SAS and PHI filled gaps effectively.

This folder contains data behind the story Introducing RAPTOR, Our New Metric For The Modern NBA and the interactive The Best NBA Players, According To RAPTOR.

• modern_ RAPTOR_ by_ player.csv contains RAPTOR data for every player broken out by season since 2014, when NBA player-tracking data first became available.
• modern_ RAPTOR_ by_ team.csv contains RAPTOR data for every player broken out by team, season and season_type since 2014, when NBA player-tracking data first became available.

These "modern" data files contain the box score and on/off plus-minus components of RAPTOR, which are then combined into a total RAPTOR rating.

• historical_ RAPTOR_ by_ player.csv contains RAPTOR data for every player broken out by season since the 1976 ABA-NBA merger.
• historical_ RAPTOR_ by_ team.csv contains RAPTOR data for every player broken out by team, season and season_type since the 1976 ABA-NBA merger.

These "historical" data files use full player-tracking RAPTOR for seasons since 2014, a version of RAPTOR that mixes box score value estimates with single-year regularized plus-minus data for seasons from 2001 through 2013, and a version of RAPTOR that only uses a box score estimate of value for the seasons from 1977 through 2000. In each era, the RAPTOR version with the highest level of detail is the version used.

• latest_ RAPTOR_ by_ player.csv contains RAPTOR data for every player in the latest season.
• latest_ RAPTOR_ by_ team.csv contains RAPTOR data for every player broken out by team, season and season_type for the latest season.

These "latest" data files contain the box score and on/off plus-minus components of RAPTOR, which are then combined into a total RAPTOR rating.

1. General Information

League ID: A unique identifier (e.g., "L001") assigned to each league to allow easy referencing within the dataset. League Name: Official name of the sports league (e.g., Premier League, NBA). This field helps distinguish leagues by their global or regional branding. Country: The primary country or region where the league is based, giving insights into the geographical spread and local fan base.

2. Sport Type

Sport: Specifies the type of sport played in the league, such as Football, Basketball, American Football, or Cricket. This field is valuable for categorizing leagues and comparing similar sports across countries.

3. Financial Metrics

Revenue (USD): Estimated annual revenue generated by the league, presented in millions of USD. Revenue figures can reflect league profitability and influence on the sports economy. Average Player Salary (USD): The average annual salary of players within the league, also in millions of USD. This can indicate the level of investment in player talent and competitiveness within the league.

4. Teams and Structure

Top Team: A notable or high-performing team within the league, which helps identify prominent clubs or franchises that may drive popularity and revenue. Total Teams: The total number of teams participating in the league, providing a sense of the league's size and structure. Larger leagues may indicate more regional diversity and fan engagement. Founded Year: The year the league was established, offering historical context and allowing analysis of how older versus newer leagues perform financially and in popularity.

5. Popularity and Viewership

Viewership: Estimated viewership numbers in millions, indicating the league's global or regional popularity. High viewership can often correlate with higher sponsorships, broadcasting rights, and overall league valuation.

6. Analysis Applications

This dataset can be used for a variety of analyses:

Market Size Comparisons: Compare leagues by revenue and viewership across different countries and sports. Player Salary Trends: Assess trends in player salaries across leagues, helping understand the financial draw of each league. Viewership Patterns: Analyze which leagues have the largest fan bases and where these are located geographically. League Growth Potential: Determine which leagues are growing in revenue and popularity over time based on the founded year and financial metrics.

National Basketball Association

The National Basketball Association (NBA) is an American men's professional basketball league. It is composed of 30 teams (29 in the United States and 1 in Canada) and is one of the four major professional sports leagues in the United States and Canada. It is widely considered to be the premier men's professional basketball league in the world.

The league was founded in New York City on June 6, 1946, as the Basketball Association of America (BAA). It changed its name to the National Basketball Association on August 3, 1949, after merging with the competing National Basketball League (NBL). The NBA's regular season runs from October to April, with each team playing 82 games. The league's playoff tournament extends into June. As of 2015, NBA players are the world's best paid athletes by average annual salary per player.

Data is being taken from https://projects.fivethirtyeight.com/2020-nba-predictions/