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, 70.4 percent of NBA players were African American, as well as half of the head coaches within the league.

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.

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.

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 63 percent of respondents aged 65 and older had no interest in the NBA.

NBA Player Statistics Dataset (Regular Season & Playoffs)

Overview

This dataset contains detailed NBA player statistics for both the regular season and playoffs, including per-game performance metrics and advanced analytics such as Player Efficiency Rating (PER). The dataset is useful for basketball analytics, machine learning projects, and statistical research on player performance.

Dataset Features

• Basic Information

  • Player: Name of the player
  • Age: Player's age in the season
  • Team: Team abbreviation
  • Pos: Position played (e.g., PG, SG, SF, PF, C)
  • Season Type: Indicates whether stats are from Regular Season or Playoffs

• Per-Game Statistics

  • G: Games played
  • GS: Games started
  • MP: Minutes played per game
  • FG, FGA, FG%: Field goals made, attempted, and percentage
  • 3P, 3PA, 3P%: Three-pointers made, attempted, and percentage
  • 2P, 2PA, 2P%: Two-pointers made, attempted, and percentage
  • FT, FTA, FT%: Free throws made, attempted, and percentage
  • ORB, DRB, TRB: Offensive, defensive, and total rebounds per game
  • AST: Assists per game
  • STL: Steals per game
  • BLK: Blocks per game
  • TOV: Turnovers per game
  • PF: Personal fouls per game
  • PTS: Points per game

• Advanced Metrics

  • PER: Player Efficiency Rating, a metric that measures per-minute performance while adjusting for pace

Usage

This dataset is ideal for:
✅ Basketball analytics (player comparisons, efficiency analysis)
✅ Machine learning projects (predicting player performance, clustering player roles)
✅ Data visualization (trends in player stats, team comparisons)

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 27 percent of fans of the NBA were Black, this number varied between fans of each individual team. Approximately 30 percent of Los Angeles Clippers fans were Black, while this figure stood at 19 percent among supporters of the Boston Celtics.

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.

Dataset

This dataset was created by Orel Sorek ML

Released under Other (specified in description)

Contents

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…).

The National Basketball Association (NBA) is one of the most popular and widely watched sports leagues in the world, with millions of fans tuning in to watch their favorite teams and players compete on the court. This dataset provides a detailed breakdown of the salaries earned by NBA players based on their individual attributes and career trajectory.

The dataset includes information on a variety of player attributes, including age, height, weight, position, and years of experience. It also includes data on each player's career statistics, such as points per game, rebounds per game, and assists per game, among others.

By analyzing this data, fans and analysts can gain insights into how different attributes and career milestones impact NBA player salaries. They can also identify trends and patterns that might inform player evaluations and team-building strategies.

Overall, this dataset is a valuable resource for anyone interested in understanding the complex factors that drive NBA player salaries and the intricate workings of the league's salary cap system. It offers a comprehensive view of the NBA landscape and sheds light on the factors that contribute to the success and earning potential of individual players.

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 28 January 2022.

--- 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 ---

Dataset obtained from nba_api endpoints. Includes all players statistics (also advanced statistics and ranks) from all games of 2024-25 NBA regular season until the last update (12/12/2024).

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/)

All data comes from publicly available datasets compiled into one.

• MLB Statistics
• MLB Salaries
• NBA Statistics
• NBA Salaries
• NFL Statistics
• NFL Salaries
• NHL Statistics
• NHL Salaries
• UFC Statistics

In 2023, over 92 percent of the team presidents within the NBA were white, while 7.9 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 18.4 percent, which was a significant increase from 2022, when that figure was 10.5 percent.

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.

Fantasy sports, particularly the daily variety in which new lineups are selected each day, are a rapidly growing industry. The two largest companies in the daily fantasy business, DraftKings and Fanduel, have been valued as high as $2 billion. This research focuses on the development of a complete system for daily fantasy basketball, including both the prediction of player performance and the construction of a team. First, a Bayesian random effects model is used to predict an aggregate measure of daily NBA player performance. The predictions are then used to construct teams under the constraints of the game, typically related to a fictional salary cap and player positions. Permutation based and K-nearest neighbors approaches are compared in terms of the identification of “successful” teams—those who would be competitive more often than not based on historical data. We demonstrate the efficacy of our system by comparing our predictions to those from a well-known analytics website, and by simulating daily competitions over the course of the 2015–2016 season. Our results show an expected profit of approximately $9,000 on an initial $500 investment using the K-nearest neighbors approach, a 36% increase relative to using the permutation-based approach alone. Supplementary materials for this article are available online.

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 63 percent of female respondents had no interest in the NBA.