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.

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.

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 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!

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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 | |:--------------|:----------------------...

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.

All information retrieved from basketball-reference.com

Rk -- Rank Pos -- Position Age -- Player's age on February 1 of the season Tm -- Team G -- Games MP -- Minutes Played PER -- Player Efficiency Rating A measure of per-minute production standardized such that the league average is 15. TS% -- True Shooting Percentage A measure of shooting efficiency that takes into account 2-point field goals, 3-point field goals, and free throws. 3PAr -- 3-Point Attempt Rate Percentage of FG Attempts from 3-Point Range FTr -- Free Throw Attempt Rate Number of FT Attempts Per FG Attempt ORB% -- Offensive Rebound Percentage An estimate of the percentage of available offensive rebounds a player grabbed while they were on the floor. DRB% -- Defensive Rebound Percentage An estimate of the percentage of available defensive rebounds a player grabbed while they were on the floor. TRB% -- Total Rebound Percentage An estimate of the percentage of available rebounds a player grabbed while they were on the floor. AST% -- Assist Percentage An estimate of the percentage of teammate field goals a player assisted while they were on the floor. STL% -- Steal Percentage An estimate of the percentage of opponent possessions that end with a steal by the player while they were on the floor. BLK% -- Block Percentage An estimate of the percentage of opponent two-point field goal attempts blocked by the player while they were on the floor. TOV% -- Turnover Percentage An estimate of turnovers committed per 100 plays. USG% -- Usage Percentage An estimate of the percentage of team plays used by a player while they were on the floor. OWS -- Offensive Win Shares An estimate of the number of wins contributed by a player due to offense. DWS -- Defensive Win Shares An estimate of the number of wins contributed by a player due to defense. WS -- Win Shares An estimate of the number of wins contributed by a player. WS/48 -- Win Shares Per 48 Minutes An estimate of the number of wins contributed by a player per 48 minutes (league average is approximately .100) OBPM -- Offensive Box Plus/Minus A box score estimate of the offensive points per 100 possessions a player contributed above a league-average player, translated to an average team. DBPM -- Defensive Box Plus/Minus A box score estimate of the defensive points per 100 possessions a player contributed above a league-average player, translated to an average team. BPM -- Box Plus/Minus A box score estimate of the points per 100 possessions a player contributed above a league-average player, translated to an average team. VORP -- Value over Replacement Player A box score estimate of the points per 100 TEAM possessions that a player contributed above a replacement-level (-2.0) player, translated to an average team and prorated to an 82-game season.

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.

File Details

Notebook

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

Trained/ Test 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)

A more detailed version can be found on github (Link: https://www.kaggle.com/datasets/shivamkumar121215/nba-stats-dataset-for-last-10-years/data)

NBA Stats: Post Season 2023/2024🏀

Welcome to the NBA Stats dataset for the post season 2023/2024! As an avid fan of basketball and sports analysis, I created this dataset to provide a comprehensive overview of player performance in the NBA during this exciting postseason.

The dataset comprises six sub-directories: - team estimated metrics - team games - team players dashboard - team players on/off details - team players on/off ratings - team season ranks by stats

The sub-directories contains CSV files of all team's estimated metrics, all stats for every game that each team played, stats for players on every team, rankings for each team's players on and off court, total stats for each team's players on and off court, and team's stats for season rankings.

Data for this dataset was collected from the official NBA website (https://www.nba.com/) using the NBA API library(https://github.com/swar/nba_api). The dataset is intended for sports enthusiasts, data analysts, and anyone interested in exploring and analyzing NBA player statistics for the 2023/2024 season.

My passion for basketball and sports analytics inspired me to compile this dataset. I believe it can be a valuable resource for researchers, analysts, and basketball enthusiasts who wish to delve deeper into the performance trends and metrics of NBA players during this exciting season.

We will create a customized NBA dataset tailored to your specific requirements. Data points may include player statistics, team rankings, game scores, player contracts, and other relevant metrics.

Utilize our NBA datasets for a variety of applications to boost strategic planning and performance analysis. Analyzing these datasets can help organizations understand player performance and market trends within the basketball industry, allowing for more precise team management and marketing strategies. You can choose to access the complete dataset or a customized subset based on your business needs.

Popular use cases include: enhancing player performance analysis, refining team strategies, and optimizing fan engagement efforts.

In 2023, over ** percent of the team presidents within the NBA were white, while *** percent were African American. This was a slight increase in the share of African American team presidents from the previous year. Meanwhile, the share of female team presidents in the NBA that year was **** percent, which was a significant increase from 2022, when that figure was **** percent.

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.

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…).

This dataset contains detailed data on all nba players from 2023/24 season.

• Player: The name of the player.
• Nation: The player's nationality.
• Pos: The player's position (e.g., guard, forward, center).
• Age: The player's age.
• MP (Minutes Played): The total number of minutes the player has played.
• Starts: The number of games the player started.
• Min (Minutes): The total number of minutes played by the player (similar to MP).
• 90s (90s Played): The equivalent number of 90-minute matches played by the player (e.g., 1.5 = 135 minutes).
• Gls (Goals): The total number of goals scored by the player.
• Ast (Assists): The total number of assists made by the player.
• G+A (Goals + Assists): The total number of goals and assists combined.
• G-PK (Goals - Penalty Kicks): The total number of goals scored excluding penalty kicks.
• PK (Penalty Kicks): The number of penalty goals scored by the player.
• PKatt (Penalty Kicks Attempted): The number of penalty kicks attempted by the player.
• CrdY (Yellow Cards): The number of yellow cards received by the player.
• CrdR (Red Cards): The number of red cards received by the player.
• xG (Expected Goals): The expected number of goals from the player's shots.
• npxG (Non-Penalty Expected Goals): The expected goals excluding penalties.
• xAG (Expected Assists): The expected number of assists from the player's passes.
• npxG+xAG (Non-Penalty xG + xAG): The total of non-penalty expected goals and expected assists.
• PrgC (Progressive Carries): The number of times the player carried the ball forward.
• PrgP (Progressive Passes): The number of passes made by the player that moved the ball forward.
• PrgR (Progressive Runs): The number of times the player made runs forward with the ball.
• Gls (Goals): (Repeated, already defined) The total number of goals scored.
• Ast (Assists): (Repeated, already defined) The total number of assists made.
• G+A (Goals + Assists): (Repeated, already defined) The total number of goals and assists combined.
• G-PK (Goals - Penalty Kicks): (Repeated, already defined) The total number of goals scored excluding penalty kicks.
• G+A-PK (Goals + Assists - Penalty Kicks): The total number of goals and assists minus penalty goals.
• xG (Expected Goals): (Repeated, already defined) The expected number of goals from the player's shots.
• xAG (Expected Assists): (Repeated, already defined) The expected number of assists from the player's passes.
• xG+xAG (Expected Goals + Expected Assists): The total expected goals and assists.
• npxG (Non-Penalty Expected Goals): (Repeated, already defined) The expected goals excluding penalties.
• npxG+xAG (Non-Penalty xG + Expected Assists): The total of non-penalty expected goals and expected assists.

Analysis of ‘NBA Players Career Duration’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sveneschlbeck/nba-players-career-duration on 12 November 2021.

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

Context

In terms of competitiveness, work ethics and training mentality, few leagues worldwide are as hard as the National Basketball Association. If a Rookie (new player) is successful or not depends on many variables - especially on his performance in the first season. Sometimes, it is possible to use statistics about such players to predict wheter they will last 5 years in the NBA or not.

Content

The tabular data contains 22 columns, all regarding a player's performance records such as e.g. the number of 3 Points made.

Analysis

Take a look at the notebook "nba-players" to get started on how to transform, analyse or visualize the data. Interesting questions to answer might be: - Statistics about NBA Rookies (Percentage of Goal types, Number of played Games, etc.) - Statistics about NBA Games/Seasons (Average Rookie Performance, etc.) - Machine Learning models predicting a Player's Career Duration of more than 5 years (binary) or the probability therefore (Proba Prediction)

Data Source

https://data.world/exercises/logistic-regression-exercise-1

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

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.

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.

This paper examines the impact of lagged performance on free agent contracts for players in the National Basketball Association. The main approach of the paper is twofold. The first piece investigates how past performance affects future performance in the two seasons after contract year and compares it to the impact previous performance has on contract terms for free agent players. The second piece investigates the rationality of free agent contracts in their entirety by comparing the impact of lagged performance on total accumulated production and total dollar value paid. The goal is to determine if performance prior to contract year is underweighted in contract decision-making relative to its predictive power of future performance. There is evidence that performance in years prior to contract year is overlooked in contract determination decisions by NBA general managers, and there is mild evidence that performance data two years prior to contract year are underweighted given their predictive power of future performance.

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!

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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...

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.

Dataset d'estadístiques de jugadors de l'NBA en temporada regular. Estadístiques de la temporada 2015 a la temporada 2023.