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.

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.

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

Check NBA Data

## Overview

Check NBA Data is a dataset for object detection tasks - it contains Check NBA Data annotations for 2,000 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

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 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 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.

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.

Source of the data: Sportsradar API (https://developer.sportradar.com/docs/read/basketball/NBA_v8)

  NBA Play-by-Play Data Extraction and Analysis





  Overview

This project aims to retrieve play-by-play data for NBA matches in the 2023 season using the Sportradar API. The play-by-play data is fetched from the API, saved into JSON files, and then used to extract relevant features for analysis and other applications. The extracted data is saved in Parquet files for easy access… See the full description on the dataset page: https://huggingface.co/datasets/farazjawed/NBA_PLAY_BY_PLAY_DATA_2023.

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.

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

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.

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 ---

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

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).

Column Labels

• season – Year the season ended. For example, the 2018-19 season is encoded as 2019.
• date – Date of the game
• regular – Regular season game (True or False)
• playoffs – Playoff game (True or False)
• away – Away team
• home – Home team
• score_away – Away team's score
• score_home – Home team's score
• q1_away – Away team's 1st quarter score
• q2_away – Away team's 2nd quarter score
• q3_away – Away team's 3rd quarter score
• q4_away – Away team's 4th quarter score
• ot_away – Away team's overtime score
• q1_home – Home team's 1st quarter score
• q2_home – Home team's 2nd quarter score
• q3_home – Home team's 3rd quarter score
• q4_home – Home team's 4th quarter score
• ot_home – Home team's overtime score
• whos_favored – Betting favorite (home or away)
• spread – Point spread (always a positive number)
• total – Over/Under
• moneyline_away – American moneyline odds for away team
• moneyline_home – American moneyline odds for home team
• h2_spread – Second half point spread
• h2_total – Second half over/under
• id_spread – 1 if favorite covered, 0 if underdog covered. 2 if push
• id_total – 1 if total went over, 0 if under, 2 if push

Data Sources

• October 30, 2007 to January 16, 2023: Sportsbook Reviews Online
• Since January 17, 2023: ESPN

I scraped SportsbookReviewsOnline.com and fixed a few errors. They seem to have stopped updating the page so all future data will come from ESPN.

Notes

Seattle moved to Oklahoma City beginning in the 2008-09 season. I encode them as okc for consistency.

New Jersey moved to Brooklyn beginning in the 2012-13 season. I encode them as bkn for consistency.

2H and Moneyline odds are absent from the ESPN data (since Jan 2023). Note that ESPN uses non-integer values exclusively so there are no pushes.

Dataset Overview

  Intro

This dataset was downloaded from the good folks at fivethirtyeight. You can find the original (or in the future, updated) versions of this and several similar datasets at this GitHub link.

  Data layout

Here are the columns in this dataset, which contains data on every NBA player, broken out by season, since the 1976 NBA-ABA merger:

Column Description

player_name Player name

player_id Basketball-Reference.com player ID

season… See the full description on the dataset page: https://huggingface.co/datasets/andrewkroening/538-NBA-Historical-Raptor.

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…).

Context

The two tables are from datasets used in this notebook

Beginners Guide to SQLite

Content

NBA Players' stats and salaries

Acknowledgements

Data source:

NBA Player Salary Dataset (2017 - 2018): https://www.kaggle.com/koki25ando/salary

NBA Players stats since 1950: https://www.kaggle.com/koki25ando/22000-scotch-whisky-reviews

Method

I use RSQLite package to generate this database:

library(RSQLite)
library(data.table)
Seasons_Stats = fread("./data/Seasons_Stats.csv")
NBA_season1718_salary = fread("./data/NBA_season1718_salary.csv")

conn = dbConnect(RSQLite::SQLite(), './data/new.sqlite')

dbWriteTable(conn, 'NBA_season1718_salary', NBA_season1718_salary)
dbWriteTable(conn, 'Seasons_Stats', Seasons_Stats)

dbListTables(conn)
dbDisconnect(conn)

Complete record of all basketball players in NBA history with career statistics and biographical information