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 NBA SportVU dataset contains player and ball trajectories for 631 games from the 2015-2016 NBA season. The raw tracking data is in the JSON format, and each moment includes information about the identities of the players on the court, the identities of the teams, the period, the game clock, and the shot clock.

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.

During the 2024 NBA Finals, Game 5 had the highest rating in the United States with an estimated 6.3 percent of households having tuned in to watch the Boston Celtics secure their record-breaking 18th NBA championship title against the Dallas Mavericks. Game 4 had the lowest rating with 4.7.

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.

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.

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

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.

Basketball Player Analysis - 2023/2024 Season

Introduction

This dataset provides a comprehensive overview of basketball players' performance during the 2023/2024 season. The following analysis highlights intriguing insights into individual statistics and players' impact on the games.

Data Used

• Source: Basketball Reference
• Key Variables:
  • Player Name
  • Points per Game
  • Assists
  • Rebounds
  • Other relevant statistics

Key Insights

1. Points per Game:

   • Average points of top players.
   • Distribution graph of scoring.

2. Assists and Rebounds:

   • Relationship between assists and rebounds.
   • Emphasis on versatile players.

3. Efficiency:

   • Shooting efficiency analysis.
   • Players with the best performance in crucial moments.

Code

Link to the code snippet on my GitHub: etl_nba_data

Feel free to explore the detailed code for extracting insights from the dataset.

Enjoy the read!

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.

Nba

## Overview

Nba is a dataset for object detection tasks - it contains Players annotations for 245 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 [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

NBA Court

## Overview

NBA Court is a dataset for object detection tasks - it contains Court annotations for 942 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 [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

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

NBA Player Game Stats - 2024/2025 Season

This dataset provides comprehensive performance statistics for NBA players throughout the 2024/2025 season. It includes both advanced and traditional stats, making it ideal for player performance analysis, efficiency assessments, and exploring game patterns and trends. Data was collected from reliable sources, ensuring quality and consistency across each record.

Column Descriptions

• Player: Name of the player.
• Tm: Abbreviation of the player's team.
• Opp: Abbreviation of the opposing team.
• Res: Result of the game for the player's team.
• MP: Minutes played, represented as a float (e.g., 23.5 = 23 minutes and 30 seconds).
• FG: Field goals made.
• FGA: Field goal attempts.
• FG%: Field goal percentage.
• 3P: 3-point field goals made.
• 3PA: 3-point field goal attempts.
• 3P%: 3-point shooting percentage.
• FT: Free throws made.
• FTA: Free throw attempts.
• FT%: Free throw percentage.
• ORB: Offensive rebounds.
• DRB: Defensive rebounds.
• TRB: Total rebounds.
• AST: Assists.
• STL: Steals.
• BLK: Blocks.
• TOV: Turnovers.
• PF: Personal fouls.
• PTS: Total points scored.
• GmSc: Game Score, a metric summarizing player performance for the game.
• Data: Date of the game in YYYY-MM-DD format.

Usage Examples

This dataset is perfectly suited for: - Statistical analysis: Gain insights into player and team performance trends. - Machine learning projects: Build predictive models using detailed player stats. - Performance prediction: Forecast player outcomes or team results. - Player comparisons: Analyze players across various metrics and categories. - Efficiency analysis: Evaluate player and team efficiency, comparing statistics across games. - Game trend exploration: Investigate patterns within the season, identifying shifts in strategies and performance.

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/)

Dataset

This dataset was created by Faisal

Released under Other (specified in description)

Contents

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.

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).

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.