NBA Players Performance

Players Performance & Statistics

By [source]

About this dataset

This dataset contains comprehensive performance data of National Basketball Association (NBA) players during the 2019-20 season. It includes all the crucial performance metrics crucial to assess a player’s quality of play. Here, you can compare players across teams, positions and categories and gain deeper insight into their overall performance. This dataset includes useful statistics such as GP (Games Played), Player name, Position, Assists Turnovers Ratio, Blocks per Game, Fouls per Minutes Played, Rebounds per Game and more. Dive in to this detailed overview of NBA player performance and take your understanding of athletes within the organization to another level!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides an in-depth look into the performance of NBA Players throughout the 2019-20 season, allowing an informed analysis of various important statistics. There are a number of ways to use this dataset to both observe and compare players, teams and positions.

• By looking at the data you can get an idea of how players are performing across all metrics. The “Points Per Game” metric is particularly useful as it allows quick comparison between different players and teams on their offensive ability. Additionally, exploratory analysis can be conducted by looking at metrics like rebounds or assists per game which allows one to make interesting observations within the game itself such as ball movement being a significant factor for team success.

• This dataset also enables further comparison between players from different positions on particular metrics that might be position orientated or generic across all positions such as points per game (ppg). This includes adjusting for positional skill sets; For example guard’s field goal attempts might include more three point shots because it would benefit them more than larger forwards or centres who rely more heavily on in close shot attempts due to their size advantage over their opponents.

• This dataset also allows for simple visualisation of player performance with respect to each other; For example one can view points scored against assists ratio when comparing multiple point guards etc., providing further insight into individual performances on certain metrics which otherwise could not be analysed quickly with traditional methods like statistical analysis only within similarly situated groups (e.g.: same position). Furthermore this data set could aid further research in emerging areas such as targeted marketing analytics where identify potential customers based off publically available data regarding factors like ppg et cetera which may highly affect team success orotemode profitability dynamicsincreasedancefficiencyoftheirownopponentteams etcet

Research Ideas

• Develop an AI-powered recommendation system that can suggest optimal players to fill out a team based on their performances in the past season.
• Examine trends in player performance across teams and positions, allowing coaches and scouts to make informed decisions when evaluating talent.
• Create a web or mobile app that can compare the performances of multiple players, allowing users to explore different performance metrics head-to-head

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: assists-turnovers.csv | Column name | Description | |:--------------|:----------------------------------| | GP | Number of games played. (Integer) | | Player | Player name. (String) | | Position | Player position. (String) |

File: blocks.csv | Column name | Description | |:--------------|:----------------------------------| | GP | Number of games played. (Integer) | | Player | Player name. (String) | | Position | Player position. (String) |

File: fouls-minutes.csv | Column name | Description | |:--------------|:----------------------...

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.

In the 2023/24 season, the Golden State Warriors generated the most revenue from the National Basketball Association franchises. Specifically, the Golden State Warriors generated 800 million U.S. dollars in revenue by the end of the season.

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...

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

The NBA play style is always changing, and collecting the "Team Per Game" stats can be an insight into increasing team efficiency, pace of games, etc.

Content

The dataset contains the "Team Per Game" stats of NBA teams from 1980-2019 season.

The data recorded is that of a box score, containing: Points (PTS), Assists (AST), Steals (STL), etc.

There are 2 data sets: 1. main_df -> data is formatted as found, is not "clean" - still has asterisks in TEAM 2. playoff_labelled -> adds a "Playoff" column - indicating if the team made the playoffs for that year, and removes the asterisks in TEAM

Acknowledgements

Data source: Basketball Reference

Inspiration

Questions to explore: -> has the pace of the game changed? Has it increased/decreased over time? -> cluster the teams based on efficiency -> how do the teams from different eras compare?

The NBA and WNBA are the two top leagues for basketball in the United States for men and women, respectively. In the NBA, players took home an average annual salary of over ** million U.S. dollars for the 2024/25 season, with the league's minimum salary set at **** million U.S. dollars that year. In comparison, players in the WNBA received an average annual pay of ******* U.S. dollars in the 2025 season, with the highest-earning players in the WNBA receiving around ******* U.S. dollars annually.

Introduction:

Embark on an enthralling exploration into the illustrious careers of basketball's most iconic figures in the NBA Legends Dataset. This meticulously curated collection chronicles the remarkable odysseys of legendary players, offering intimate glimpses into their unparalleled skills, unwavering determination, and relentless pursuit of excellence. As a tribute to the enduring legacies and profound impacts these legends have had on the game and countless lives, this dataset encapsulates their transcendent influences, both on and off the court.

Column Descriptions:

1. season: The NBA season during which the game took place.
2. date: The date of the game.
3. age: Kobe Bryant's age at the time of the game.
4. team_played: The team Kobe Bryant played for during the game.
5. game_type: The type of game (regular season, playoffs, etc.).
6. venue: The arena or stadium where the game was held.
7. opponent: The opposing team.
8. win_lose: Indicates whether Kobe's team won or lost the game.
9. point_difference: The difference in points between Kobe's team and the opposing team.
10. game_started: Whether Kobe started the game or came off the bench.
11. minutes_played: The total minutes Kobe played in the game.
12. fieldgoal: The number of field goals Kobe made.
13. fieldgoal_attempts: The total number of field goal attempts by Kobe.
14. fieldgoal_percent: Kobe's shooting percentage for field goals.
15. 3pointers: The number of three-pointers Kobe made.
16. 3pointers_attempts: The total number of three-point attempts by Kobe.
17. 3pointers_percent: Kobe's shooting percentage for three-pointers.
18. freethrows: The number of free throws Kobe made.
19. freethrows_attempt: The total number of free throw attempts by Kobe.
20. freethrow_percent: Kobe's shooting percentage for free throws.
21. offensive_rebounds: The number of offensive rebounds by Kobe.
22. defensive_rebounds: The number of defensive rebounds by Kobe.
23. total_rebounds: The total number of rebounds by Kobe.
24. assists: The number of assists by Kobe.
25. steals: The number of steals by Kobe.
26. blocks: The number of blocks by Kobe.
27. turnovers: The number of turnovers by Kobe.
28. personal_fouls: The number of personal fouls committed by Kobe.
29. points: The total points scored by Kobe in the game.

Influence of NBA Legends:

The enduring legacies of NBA legends transcend basketball, serving as timeless sources of inspiration for athletes and enthusiasts alike. Their remarkable achievements, unwavering work ethics, and unyielding self-belief epitomize the essence of greatness and resilience. As we delve into the intricacies of their journeys through this dataset, may their indelible spirits continue to inspire and motivate us to pursue excellence in every aspect of life

Photo by JC Gellidon on Unsplash