The National Basketball Association has one of the highest percentages of African American players from the big four professional sports leagues in North America. In 2023, approximately **** percent of NBA players were African American. Meanwhile, ethnically white players constituted a **** percent share of all NBA players that year. After the WNBA and NBA, the National Football League had the largest share of African Americans in a professional sports league in North America. How do other roles in the NBA compare? When it comes to African American representation in the NBA, no other role in the NBA is as well represented by African Americans as players. Meanwhile, on the opposite end of the scale, less than **** percent of team governors in the NBA were African American in 2023. During the 2022/23 season, the role with the second-highest share of African Americans was head coach, with a share of ** percent. That season, the number of African American head coaches in the NBA exceeded the number of white head coaches for the first time. African Americans in the NFL In 2022, the greatest share of players by ethnicity in the NFL were African American, with more than half of all NFL players falling within this group. The representation of African Americans in American Football extended beyond the playing field, with **** percent of NFL assistant coaches being African American in 2022 as well. However, positions such as vice presidents and head coaches were less representative of the African American population, as less than ** percent of the individuals fulfilling these roles in 2022 were African American.

A survey conducted in January 2025 in the United States revealed that over half of NBA fans were Caucasian. Meanwhile, 20.7 percent were Hispanic.

NBA Statistics Repository

Welcome to the NBA Statistics Repository for teams and players. This repository contains a rich and diverse dataset spanning from 1996 to 2023, drawn from NBA game statistics. It's ideal for data analysts, basketball fans, researchers, and anyone interested in the detailed numbers behind the sport.

Repo Structure

This repository contains a series of CSV files detailing the performances of teams and players from 1996 to 2023. A list of these files is provided below:

1. player_index.csv: An index of all players with general information.
2. player_stats_advanced_po.csv and player_stats_advanced_rs.csv: Advanced statistics for players during playoffs (po) and regular season (rs).
3. player_stats_defense_po.csv and player_stats_defense_rs.csv: Defensive statistics for players during the playoffs and regular season.
4. player_stats_misc_po.csv and player_stats_misc_rs.csv: Miscellaneous player statistics for the playoffs and regular season.
5. player_stats_scoring_po.csv and player_stats_scoring_rs.csv: Scoring statistics for players during the playoffs and regular season.
6. player_stats_traditional_po.csv and player_stats_traditionnal_rs.csv: Traditional player statistics during the playoffs and regular season.
7. player_stats_usage_po.csv and player_stats_usage_rs.csv: Player usage statistics during the playoffs and regular season.
8. team_stats_advanced_po.csv and team_stats_advanced_rs.csv: Advanced team statistics during the playoffs and regular season.
9. team_stats_defense_po.csv and team_stats_defense_rs.csv: Defensive team statistics during the playoffs and regular season.
10. team_stats_four_factors_po.csv and team_stats_four_factors_rs.csv: Four factors team statistics during the playoffs and regular season.
11. team_stats_misc_po.csv and team_stats_misc_rs.csv: Miscellaneous team statistics during the playoffs and regular season.
12. team_stats_opponent_po.csv and team_stats_opponent_rs.csv: Team opponent statistics during the playoffs and regular season.
13. team_stats_scoring_po.csv and team_stats_scoring_rs.csv: Scoring team statistics during the playoffs and regular season.
14. team_stats_traditional_po.csv and team_stats_traditional_rs.csv: Traditional team statistics during the playoffs and regular season.

How to Use this Data

To use this data, simply clone this repository and use a software capable of reading CSV files, such as Excel, R, Python (with pandas), etc.

Contributions

Contributions to this repo are welcome. If you have additional data to add or corrections to make, please feel free to open a pull request.

License

These data are released under the MIT License. See the LICENSE file for more information.

Of the big four professional sports leagues in North America, the NFL and the NBA have the highest percentage of African American players. In 2023, **** percent of NBA players were African American, as well as half of the head coaches within the league.

NBA Rookies Performance Statistics and Minutes Played: 1980-2016

Tracking Basketball Prodigies' Growth and Achievements

By Gabe Salzer [source]

About this dataset

This dataset contains essential performance statistics for NBA rookies from 1980-2016. Here you can find minute per game stats, points scored, field goals made and attempted, three-pointers made and attempted, free throws made and attempted (with the respective percentages for each), offensive rebounds, defensive rebounds, assists, steals blocks turnovers efficiency rating and Hall of Fame induction year. It is organized in descending order by minutes played per game as well as draft year. This Kaggle dataset is an excellent resource for basketball analysts to gain a better understanding of how rookies have evolved over the years—from their stats to how they were inducted into the Hall of Fame. With its great detail on individual players' performance data this dataset allows you to compare their performances against different eras in NBA history along with overall trends in rookie statistics. Compare rookies drafted far apart or those that played together- whatever your goal may be!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset is perfect for providing insight into the performance of NBA rookies over an extended period of time. The data covers rookie stats from 1980 to 2016 and includes statistics such as points scored, field goals made, free throw percentage, offensive rebounds, defensive rebounds and assists. It also provides the name of each rookie along with the year they were drafted and their Hall of Fame class.

This data set is useful for researching how rookies’ stats have changed over time in order to compare different eras or identify trends in player performance. It can also be used to evaluate players by comparing their stats against those of other players or previous years’ stats.

In order to use this dataset effectively, a few tips are helpful:

• Consider using Field Goal Percentage (FG%), Three Point Percentage (3P%) and Free Throw Percentage (FT%) to measure a player’s efficiency beyond just points scored or field goals made/attempted (FGM/FGA).

• Lookout for anomalies such as low efficiency ratings despite high minutes played as this could indicate that either a player has not had enough playing time in order for their statistics to reach what would be per game average when playing more minutes or that they simply did not play well over that short period with limited opportunities.

• Try different visualizations with the data such as histograms, line graphs and scatter plots because each may offer different insights into varied aspects of the data set like comparison between individual years vs aggregate trends over multiple years etc.

  Lastly it is important keep in mind whether you're dealing with cumulative totals over multiple seasons versus looking at individual season averages or per game numbers when attempting analysis on these sets!

Research Ideas

• Evaluating the performance of historical NBA rookies over time and how this can help inform future draft picks in the NBA.
• Analysing the relative importance of certain performance stats, such as three-point percentage, to overall success and Hall of Fame induction from 1980-2016.
• Comparing rookie seasons across different years to identify common trends in terms of statistical contributions and development over time

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: NBA Rookies by Year_Hall of Fame Class.csv | Column name | Description | |:-----------------------|:------------------------------------------------------------------| | Name | The name of...

Current and updated dataset of NBA statistics since the 1981-1982 season!

All standard statistics like Assists Per Game, Minutes Per Game, etc. are present as well as advanced statistics like Player Efficiency Rating (PER), Value Over Replacement Player (VORP), Win Share, and more!

This dataset was created with the intent to build a Machine Learning Model to predict the NBA MVP (Complete project is in Code section) and was web scraped from https://www.basketball-reference.com.

Feel free to let me know if there are any statistics or player information that isn't present that you think should be added!

For more details on how some statistics are calculated, please see the https://www.basketball-reference.com/about/glossary.html

When surveyed in January 2025, it was found that the age group with the highest share of NBA fans was the 35 to 49-year-old demographic. In total, 26.2 percent of respondents in this age bracket were fans of the world's leading basketball league. Meanwhile, 7.2 percent of 13 to 17-year-olds were NBA fans.

Description

This dataset contains end-of-season box-score aggregates for NBA players over the 2012–13 through 2023–24 seasons, split into training and test sets for both regular season and playoffs. Each CSV has one row per player per season with columns for points, rebounds, steals, turnovers, 3-pt attempts, FG attempts, plus identifiers.

Brief overview of Files

1. end-of-season box-score aggregates (2012–13 – 2023–24) split into train/test;

2. the Jupyter notebook (Analysis.ipynb); All the code can be executed in there

3. the trained model binary (nba_model.pkl); Serialized Random Forest model artifact

4. Evaluation plots (LAL vs. whole‐league) for regular & playoff predictions are given as png outputs and uploaded in here

5. FAIR4ML metadata (fair4ml_metadata.jsonld);
   see README.md and abbreviations.txt for file details.”

6. For further information you can go to the github site (Link below)

File Details

Notebook

Analysis.ipynb: Involves the graphica output of the trained and tested data.

Trained/ Test csv Data

Others

abbrevations.txt: Involves the fundemental abbrevations of the columns in csv data

Additional Notes

Raw csv files are taken from Kaggle (Source: https://www.kaggle.com/datasets/shivamkumar121215/nba-stats-dataset-for-last-10-years/data)

Some preprocessing has to be done before uploading into dbrepo

Plots have also been uploaded as an output for visual purposes.

A more detailed version can be found on github (Link: https://github.com/bubaltali/nba-prediction-analysis/)

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.

Dataset Description

This dataset contains a single CSV file with lifetime statistics for NBA players. The data includes various box score stats and personal information for each player's career.

  Data Fields

The CSV file contains the following columns:

FULL_NAME: The player's full name AST: Total career assists BLK: Total career blocks DREB: Total career defensive rebounds FG3A: Total 3-point field goal attempts FG3M: Total 3-point field goals made FG3_PCT: 3-point field… See the full description on the dataset page: https://huggingface.co/datasets/Hatman/NBA-Player-Career-Stats.

The National Basketball Association is a professional basketball league in North America. While ** percent of fans of the NBA were Black, this number varied between fans of each individual team. Approximately ** percent of Los Angeles Clippers fans were Black, while this figure stood at ** percent among supporters of the Boston Celtics.

This project analyzes historical NBA data (2012-2024, sourced from Kaggle) using a multi-output Random Forest model (scikit-learn) to predict key player statistics (points, rebounds, etc.). The experiment emphasizes reproducibility and FAIR data practices, producing the trained model, evaluation metrics, visualizations, FAIR4ML metadata, and this DMP as outputs. This work is part of the TU Wien Data Stewardship lecture.

Github: https://github.com/bubaltali/nba-prediction-analysis/

DBRepo: https://test.dbrepo.tuwien.ac.at/database/2e167490-c803-4a9a-a317-6e274c6b3a37/info

TUWRD. https://handle.test.datacite.org/10.70124/ymgzs-z3s43

The data was taken from: https://www.kaggle.com/datasets/shivamkumar121215/nba-stats-dataset-for-last-10-years/data

This combined dataset offers a comprehensive view of the NBA - - featuring game-level statistics (NBA_GAMES.csv) - player details (NBA_PLAYERS.csv) - individual player performance metrics (NBA_PLAYER_GAMES.csv) - team information (NBA_TEAMS.csv)

It includes matchup outcomes, shooting percentages, points, rebounds, assists, and advanced stats like PLUS-MINUS, player and team identifiers, active status, and historical details. Together, these datasets provide a robust resource for analyzing team dynamics, player contributions, and game trends across the league.

Key Relationships Between Datasets | Dataset | Primary Key | Foreign Key | |-------------------------|-----------------|-------------------------------------| | NBA_TEAMS | id | NBA_GAMES.Team_ID | | NBA_GAMES | Game_ID | NBA_PLAYER_GAMES.Game_ID | | NBA_PLAYER_GAMES | (Player_ID, Game_ID) | NBA_PLAYERS.id, NBA_GAMES.Game_ID | | NBA_PLAYERS | id | NBA_PLAYER_GAMES.Player_ID |

We analyse and compare NBA and Euroleague basketball through box-score statistics in the period from 2000 to 2017. Overall, the quantitative differences between the NBA and Euroleague have decreased and are still decreasing. Differences are even smaller after we adjust for game length and when playoff NBA basketball is considered instead of regular season basketball. The differences in factors that contribute to success are also very small—(Oliver’s) four factors derived from box-score statistics explain most of the variability in team success even if the coefficients are determined for both competitions simultaneously instead of each competition separately. The largest difference is game pace—in the NBA there are more possessions per game. The number of blocks, the defensive rebounding rate and the number of free throws per foul committed are also higher in the NBA, while the number of fouls committed is lower. Most of the differences that persist can be reasonably explained by the contrasts between the better athleticism of NBA players and more emphasis on tactical aspects of basketball in the Euroleague.

Dataset generado mediante web scraping responsable desde basketball-reference.com. Incluye estadísticas de jugadores NBA por temporada (PPG, RPG, APG, WS) y perfiles personales enriquecidos (altura, alias, logros…).

What determines a team’s home advantage, and why does it change with time? Is it something about the rowdiness of the hometown crowd? Is it something about the location of the team? Or is it something about the team itself, the quality of the team or the styles it may or may not play? To answer these questions, season performance statistics were downloaded for all NBA teams across 32 seasons (83–84 to 17–18). Data were also obtained for other potential influences identified in the literature including: stadium attendance, altitude, and team market size. Using an artificial neural network, a team’s home advantage was diagnosed using team performance statistics only. Attendance, altitude, and market size were unsuccessful at improving this diagnosis. The style of play is a key factor in the home advantage. Teams that make more two point and free-throw shots see larger advantages at home. Given the rise in three-point shooting in recent years, this finding partially explains the gradual decline in home advantage observed across the league over time.

A January 2025 survey in the United States revealed that over 60 percent of NBA fans who attended or watched games were male. Meanwhile, just under 40 percent of NBA fans were female.

Comprehensive YouTube channel statistics for NBA, featuring 23,600,000 subscribers and 16,578,563,315 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Sports category and is based in US. Track 71,934 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

The dataset contains data for each of the players who have interacted with the NBA during a specific period of time (last season) and collects all the accumulated statistics. In addition, it summarizes the performance of each player through the rest of the data by means of the player efficiency rating (PER) variable, a metric that takes into account all the data extracted from a player.

A January 2025 survey in the United States revealed that **** percent of NBA fans who attended or watched games had a household income between ****** and ****** U.S. dollars. Meanwhile, ** percent of fans' income was below ****** U.S. dollars.