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.

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.

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.

An average of **** million viewers tuned in to watch NBA regular season games across ABC, ESPN and TNT in the 2024/25 season. This marked a slight decline in the number of viewers from the previous season.

First held in 1947, the National Basketball Association (NBA) Draft is an annual pre-season event through which teams in the league select recruits from a pool of college and professional men's basketball players. The 2025 NBA Draft was shown on ESPN and ABC in the United States and attracted an average viewership of **** million viewers. The previous year had reached a record-high of **** million viewers.

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

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.

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.

Current and updated dataset of NBA Playoff statistics since the 1949-1950 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 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!

If you want the regular season statistics check out my other data set.

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

The games of the 2024 NBA Finals had an average TV rating of ***. The 2024 championships were contested between the Boston Celtics and the Dallas Mavericks and saw the Celtics win 4-1 to claim their record-breaking **** NBA title in franchise history.

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

A January 2024 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, almost ** percent of fans' income was below ****** U.S. dollars.

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.

Context

I was having an everyday conversation with two of my friends here about how much programming knowledge we need for our college classes. One of my friends is extremely knowledgeable on basketball statistics; he can recall seemingly randomly stats for almost any college player. When we learned about his process for writing articles and making conclusions based on data, we realized that using machine learning would expedite his process almost immediately. So the first step would be to compile all the data we need.

Content

Some of the statistics are obvious such as points, blocks, etc. However, some advanced statistics employ complicated equations, such as offensive rating and PORPAG. These statistics all need to be taken with a grain of salt, since some can be misleading. Specifically, plus/minus may seem to be an effective statistic for ranking how much of an impact players have on their team, but this can be heavily impacted by rotations.

For instance, on my favorite NBA team, the Golden State Warriors, plus/minus is almost irrelevant, since any player that is on the court with Stephen Curry almost always has a much better plus/minus than players who are forced to play without his presence on the court.

However, with the sheer bulk of stats present, I'm hoping there will be clear patterns that emerge with further digging into the data.

Acknowledgements

Avinash Chauhan and Logan Norman, who helped inspire this idea.

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...

This dataset opens the door to the intricacies of the 2023 NBA season, offering a profound understanding of the art of scoring in professional basketball. Within its comprehensive analysis, it showcases the remarkable prowess of 3 players LeBron James, James Harden, and Stephen Curry—true icons of the sport. Delve deep into the strategic brilliance that defines these players' shooting trends, performance metrics, and unwavering precision on the court. Whether you're a passionate basketball enthusiast or a data-driven analyst, this dataset provides a unique and invaluable window into the mastery of these legendary athletes and the ever-evolving game of basketball.

Key Features

How to use this dataset

Data Scientists and Analysts: Employ advanced statistical analysis to uncover hidden patterns and insights in the shooting trends of LeBron James, James Harden, and Stephen Curry.

Basketball Researchers and Analysts: Evaluate the impact of shooting techniques and performance on game outcomes.

NBA Coaches and Officials: Utilize the dataset to study the strengths and weaknesses of individual players, enabling more targeted coaching and defensive strategies.

Sports Journalists and Commentators: Access detailed statistics to enhance game commentary and provide viewers with deeper insights into player performance.

Basketball Enthusiasts and Fans: Gain a new perspective on the game by exploring the shooting trends and performance of their favorite players.

The graph shows the average TV ratings of selected National Basketball Association games / events in 2018. An estimated *** percent of all U.S. households tuned in to at least **** cumulative minutes of the NBA All-Star Game in 2018.

The database contains several datasets and files with NBA statistical data spanning four seasons (2015-2016 to 2018-2019). These datasets were procured from the Basketball Reference database (https://www.basketball-reference.com/), a publicly accessible source of NBA data.

The main file, `dat.cleaned.csv`, includes the Win/Loss records for all thirty NBA teams, along with box scores and advanced statistics. The data captured over the four seasons correspond to about 4,920 regular-season games. A distinguishing feature of this dataset is the repeated measurements per player within a team across the seasons. However, it's important to note that these repeated measurements are not independent, necessitating the use of hierarchical modelling to properly handle the data.

Two sets of additional text files (`per_2017.txt`, `per_2018.txt`, `rpm_2017.txt`, `rpm_2018.txt`) provide specific metrics for player performance. The 'PER' files contain the Athlete Efficiency Rating (PER) for the years 2017 and 2018. The 'RPM' files contain the ESPN-developed score called Real Plus-Minus (RPM) for the same years.

However, potential biases or limitations within the datasets should be acknowledged. For instance, the Basketball Reference website might not include data from some matches or may exclude certain variables, potentially affecting the quality and accuracy of the dataset.

The NBA All-Star game is an annual basketball exhibition game contested between the Eastern Conference and Western Conference All-Stars. The 24 players featured in the game are selected through a voting process among fans, players, and media. The 2025 All-Star game was watched by 4.72 million viewers, which was a decrease of 13 percent from the 5.4 million viewers who tuned into the previous year's game.

Comprehensive box score statistics and player performance metrics from the Lakers vs Grizzlies game including points, rebounds, assists, field goal percentages, plus-minus ratings, and advanced statistics.