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

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.

Context

This dataset contains 2022-2023 regular season NBA player stats per game. Note that there are duplicate player names resulted from team changes.

• 2021-2022 NBA Player Stats
• 2023-2024 NBA Player Stats

Content

+500 rows and 30 columns. Columns' description are listed below.

• Rk : Rank
• Player : Player's name
• Pos : Position
• Age : Player's age
• Tm : Team
• G : Games played
• GS : Games started
• MP : Minutes played per game
• FG : Field goals per game
• FGA : Field goal attempts per game
• FG% : Field goal percentage
• 3P : 3-point field goals per game
• 3PA : 3-point field goal attempts per game
• 3P% : 3-point field goal percentage
• 2P : 2-point field goals per game
• 2PA : 2-point field goal attempts per game
• 2P% : 2-point field goal percentage
• eFG% : Effective field goal percentage
• FT : Free throws per game
• FTA : Free throw attempts per game
• FT% : Free throw percentage
• ORB : Offensive rebounds per game
• DRB : Defensive rebounds per game
• TRB : 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

Acknowledgements

Data from Basketball Reference. Image from Clutch Points.

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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).

The CSV file has the following header: PLAYER_ID,Name,SEASON_ID,TEAM_ID,TEAM_ABBREVIATION,PLAYER_AGE,GP,GS,MIN,FGM,FGA,FG_PCT,FG3M,FG3A,FG3_PCT,FTM,FTA,FT_PCT,OREB,DREB,REB,AST,STL,BLK,TOV,PF,PTS * PLAYER_ID: The players NBA Player ID * NAME: The players name * SEASON_ID: The season for the rows stats * TEAM_ID: NBA team ID * TEAM_ABBREVIATION: abbreviated team name * PLAYER_AGE: players age that season * GP: games played * GS: games started * MIN: minutes played * FGM: field goals made * FGA: field goals attempted * FG_PCT: field goal percentage * FG3M: three point field goals made * FG3A: three point field goals attempted * FG3_PCT: three point field goal percentage * FTM: free throws made * FTA: attempted free throws * FT_PCT: free throw percentage * OREB: offensive rebounds * DREB: defensive rebounds * REB: rebounds * AST: assists * STL: steals * BLK: blocks * TOV: turnovers * PF: personal fouls * PTS: points

Missing stats filled in with None

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.

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.

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)

As of 2024, the largest luxury tax bill footed by a team in the NBA came in the 2023/24 season, when the Golden State Warriors were taxed 176.9 million U.S. dollars by the league. The Warriors also held the other top-three spots, bringing their overall luxury tax payments from 2021/22 to 2023/24 to 510.9 million U.S. dollars.

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.

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.

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

In the summer of 2007, former National Basketball Association (NBA) referee Tim Donaghy was found to have bet on games that he officiated. Donaghy subsequently alleged that referee bias is rampant throughout the league. The scandal created widespread speculation about the legitimacy of controversial games in recent history, though NBA Commissioner David Stern argued vehemently that Donaghy was an isolated individual in his deviance. In light of this contentious issue, this paper empirically investigates whether there is evidence of referee bias. Specifically, home bias and prolonged playoff series bias are examined through analysis of multiple statistical categories, including discretionary turnovers (DTOs) and non-discretionary turnovers (NTOs). To analyze prolon ged series bias, this study observes teams facing elimination (down 2-3, 1-3, or 0-3) as well as those threatening to eliminate (up 3-2, 3-1, or 3-0). In these “one-sided elimination games,” or “pre-game 7 elimination games,” only a win by the team trailing in the series can extend the series to an additional game, generating added league revenue from ticket and advertisement sales. So, if the data analysis suggests that particular statistics are significantly favored toward teams facing “one-sided elimination,” it may suggest prolonged series bias among referees.

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.

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 standardized regressions results from pooled top scorers data.

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.

This dataset contains a broad range of statistical data from every team since the very beginning of the NBA (1950) and WNBA (1997) obtained from Basketball Reference.

Semi- partial correlations between the defensive, offensive and opportunity-adjusted MSEs across games.