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.

I scraped player stats for all NBA seasons, ranging from 1949-50 to 2021-22, from Basketball Reference.

The team Payroll and player salary data was scraped from Hoops Hype.

I utilized the nba_api python package to scrape all of the box score data.

To see the code for scraping both sites see my Github repo.

This data set is an update to datasets such as NBA Player Stats & NBA Data from Basketball Reference.

NBA Player Game Stats - 2024/2025 Season

This dataset provides comprehensive performance statistics for NBA players throughout the 2024/2025 season. It includes both advanced and traditional stats, making it ideal for player performance analysis, efficiency assessments, and exploring game patterns and trends. Data was collected from reliable sources, ensuring quality and consistency across each record.

Column Descriptions

• Player: Name of the player.
• Tm: Abbreviation of the player's team.
• Opp: Abbreviation of the opposing team.
• Res: Result of the game for the player's team.
• MP: Minutes played, represented as a float (e.g., 23.5 = 23 minutes and 30 seconds).
• FG: Field goals made.
• FGA: Field goal attempts.
• FG%: Field goal percentage.
• 3P: 3-point field goals made.
• 3PA: 3-point field goal attempts.
• 3P%: 3-point shooting percentage.
• FT: Free throws made.
• FTA: Free throw attempts.
• FT%: Free throw percentage.
• ORB: Offensive rebounds.
• DRB: Defensive rebounds.
• TRB: Total rebounds.
• AST: Assists.
• STL: Steals.
• BLK: Blocks.
• TOV: Turnovers.
• PF: Personal fouls.
• PTS: Total points scored.
• GmSc: Game Score, a metric summarizing player performance for the game.
• Data: Date of the game in YYYY-MM-DD format.

Usage Examples

This dataset is perfectly suited for: - Statistical analysis: Gain insights into player and team performance trends. - Machine learning projects: Build predictive models using detailed player stats. - Performance prediction: Forecast player outcomes or team results. - Player comparisons: Analyze players across various metrics and categories. - Efficiency analysis: Evaluate player and team efficiency, comparing statistics across games. - Game trend exploration: Investigate patterns within the season, identifying shifts in strategies and performance.

The sample for this study is composed of NBA players from the 1999–2000 season through the 2015–2016 season. Data on the ethnicities of NBA players was manually collected by searching websites such as Wikipedia, Facebook, Google, and Baidu Encyclopedia; where it was impossible to make this judgment based on player data, players’ pictures published on the Basketball Reference website (http://www.basketball-reference.com) were examined to determine ethnicity (Wallace, 1988). Player salaries were collected from the ESPN website (http://www.espn.com/nba/salaries); player characteristics and technical data come from the ESPN website and the Basketball Reference website. Players who changed teams within a season were eliminated from the sample, as were players who made less than two appearances on the court within a season.

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…).

Analysis of ‘NBA Players’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/justinas/nba-players-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

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

Update 08-03-2020: 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.

--- Original source retains full ownership of the source dataset ---

Description

NBA anba WNBA dataset is a large-scale play-by-play and shot-detail dataset covering both NBA and WNBA games, collected from multiple public sources (e.g., official league APIs and stats sites). It provides every in-game event—from period starts, jump balls, fouls, turnovers, rebounds, and field-goal attempts through free throws—along with detailed shot metadata (shot location, distance, result, assisting player, etc.).

Also you can download dataset from github or GoogleDrive

Tutorials

1. NBA play-by-play dataset R example

I will be grateful for ratings and stars on github, but the best gratitude is use of dataset for your projects.

Useful links:

• nba-on-court: package for work with NBA and WNBA play-by-play data
• Ryan Davis: Analyze the Play by Play Data
• Python nba_api package for work with NBA API: https://github.com/swar/nba_api
• R hoopR package for work with NBA API: https://hoopr.sportsdataverse.org/

Motivation

I made this dataset because I want to simplify and speed up work with play-by-play data so that researchers spend their time studying data, not collecting it. Due to the limits on requests on the NBA and WNBA website, and also because you can get play-by-play of only one game per request, collecting this data is a very long process.

Using this dataset, you can reduce the time to get information about one season from a few hours to a couple of seconds and spend more time analyzing data or building models.

I also added play-by-play information from other sources: pbpstats.com, data.nba.com, cdnnba.com. This data will enrich information about the progress of each game and hopefully add opportunities to do interesting things.

Contact Me

If you have any questions or suggestions about the dataset, you can write to me in a convenient channel for you:

• LinkedIn
• GIthub
• X
• Telegram

Context

This dataset was collected to work on NBA games data. I used the nba stats website to create this dataset.

You can find more details about data collection in my GitHub repo here : nba predictor repo.

If you want more informations about this api endpoint feel free to go on the nba_api GitHub repo that documentate each endpoint : link here

Content

You can find 5 datasets :

• games.csv : all games from 2004 season to last update with the date, teams and some details like number of points, etc.
• games_details.csv : details of games dataset, all statistics of players for a given game
• players.csv : players details (name)
• ranking.csv : ranking of NBA given a day (split into west and east on CONFERENCE column
• teams.csv : all teams of NBA

Acknowledgements

I would like to thanks nba stats website which allows all NBA data freely open to everyone and with a great api endpoint.

Inspiration

• Predict NBA games winner

Enjoy it ! Nathan

This data has seasonal stats which can all be easily calculated to per game and other various labels and stats. I used nba_api to get all this data. You can check that out at: https://github.com/Tman1351/NBA-API-Data-Getter. Feel free to use it on whatever you want.

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

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/)

The National Basketball Association is a professional basketball league for men with 30 teams competing against each other in the United States. A survey conducted in March 2023 in the United States showed that 40 percent of black respondents had no interest in the NBA.

Context-

Stephen Curry's heroics in 3-point shooting lead me to create the dataset.

Content-

This Dataset contains 3-point shots made, attempted, Field Goal Percentage and Percentage share of 3-pointers in total points for the time period of 1996-2020. Initial 3 columns are taken from NBA.com official website and Percentage share of 3-pointers in total points was calculated using the data retrieved from official website.

Column Description-

A) For Sheet 1 (Year wise data) : This sheet has average stats for every NBA team for each season Teams: All the existing teams Every season e.g. 1996-97 has 4 columns under them: 3PM: Average 3-pointers per game made in that particular season for by specified team 3PA: Average 3-pointers per game attempted in that particular season by specified team 3P%: Average 3-pointer shooting percentage per game in that particular season by specified team 3P% share in Total points: Average share of 3-pointers in total points scored per game by the specified team

B) For Sheet 2 (NBA Average data) : This sheet has average stats for whole of NBA for each season Years: Played season year 3PM: Average 3-pointers per game made in that particular season for by specified team 3PA: Average 3-pointers per game attempted in that particular season by specified team 3P%: Average 3-pointer shooting percentage per game in that particular season by specified team 3P% share in Total points: Average share of 3-pointers in total points scored per game by the specified team

C) For Sheet 3 (GSW Average data) : This sheet has average stats only for GSW every season Years: Played season year 3PM: Average 3-pointers per game made in that particular season for by specified team 3PA: Average 3-pointers per game attempted in that particular season by specified team 3P%: Average 3-pointer shooting percentage per game in that particular season by specified team 3P% share in Total points: Average share of 3-pointers in total points scored per game by the specified team

D) For Sheet 4 (4-Year Range data) : This sheet has 4-year average stats for every NBA team Years: Played season year 3PM: Average 3-pointers per game made in that particular season for by specified team 3PA: Average 3-pointers per game attempted in that particular season by specified team 3P%: Average 3-pointer shooting percentage per game in that particular season by specified team 3P% share in Total points: Average share of 3-pointers in total points scored per game by the specified team

A September 2024 survey in the United States revealed that 16 percent of NBA fans had a preference for the Los Angeles Lakers. In second place, the Chicago Bulls were liked by 11 percent of fans.

Dataset Summary

nba_dataset is a large-scale play-by-play and shot-detail dataset covering both NBA and WNBA games, collected from multiple public sources (e.g., official league APIs and stats sites). It provides every in-game event—from period starts, jump balls, fouls, turnovers, rebounds, and field-goal attempts through free throws—along with detailed shot metadata (shot location, distance, result, assisting player, etc.).

Modality: Tabular, Text Format: Parquet Size: ~72.4… See the full description on the dataset page: https://huggingface.co/datasets/Vladislav/nba_dataset.

Context

Dataset is composed of scraped together files from basketball-reference.com

Content

Salary and Statistics (advanced/basic) for NBA players during the 17-18 regular season. The dataset is composed of a unique combination of Player/Team, therefore if you see a player listed twice, this indicates that the player played for multiple teams during the 17-18 season.

Dataset Card for Dataset Name

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

  Dataset Details

player stats for the regular seasons from 1996-2023

  Dataset Description

player stats for the regular seasons from 1996-2023, obtained by querying the stats.nba.com endpoint. The data is available as a delta file.

Curated by: [More Information Needed] Funded by [optional]: [More Information Needed] Shared… See the full description on the dataset page: https://huggingface.co/datasets/nsfwpenguins/nba_reg_player_stats.

As of 2024, the largest luxury tax bill footed by a team in the NBA came in the 2023/24 season, when the Golden State Warriors were taxed 176.9 million U.S. dollars by the league. The Warriors also held the other top-three spots, bringing their overall luxury tax payments from 2021/22 to 2023/24 to 510.9 million U.S. dollars.

The Basketball NBA market represents a dynamic segment of the global sports industry, characterized by a passionate fanbase, lucrative sponsorship deals, and an ever-expanding digital presence. As one of the premier professional basketball leagues worldwide, the NBA has cultivated a significant market size, boasting

The minimum salary for players signing contracts in the 2024/25 NBA season amounted to 1.16 million U.S. dollars. This represented an increase of around 3 percent from the previous season, when the figure stood at 1.12 million U.S. dollars.