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.

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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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

Introduction: This dataset provides comprehensive statistics for NBA players during the 2022-2023 regular season. It encompasses over 500 rows and 30 columns, presenting a wide range of player performance metrics. The data is derived from Basketball Reference, ensuring accuracy and reliability. However, it's worth noting that there may be duplicate player names due to team changes throughout the season (Which will show TOT for the total status of the player).

Columns Description: 1. Rk: Rank 2. Player: Player's name 3. Pos: Position 4. Age: Player's age 5. Tm: Team 6. G: Games played 7. GS: Games started 8. MP: Minutes played per game 9. FG: Field goals per game 10. FGA: Field goal attempts per game 11. FG%: Field goal percentage 12. 3P: 3-point field goals per game 13. 3PA: 3-point field goal attempts per game 14. 3P%: 3-point field goal percentage 15. 2P: 2-point field goals per game 16. 2PA: 2-point field goal attempts per game 17. 2P%: 2-point field goal percentage 18. eFG%: Effective field goal percentage 19. FT: Free throws per game 20. FTA: Free throw attempts per game 21. FT%: Free throw percentage 22. ORB: Offensive rebounds per game 23. DRB: Defensive rebounds per game 24. TRB: Total rebounds per game 25. AST: Assists per game 26. STL: Steals per game 27. BLK: Blocks per game 28. TOV: Turnovers per game 29. PF: Personal fouls per game 30. PTS: Points per game

Acknowledgements: Reference: basketball-reference.com

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.

• Player: string - name of the player
• Pos (Position): string - position played by the player
• Age: integer - age of the player as of February 1, 2023
• Tm (Team): string - team the player belongs to
• G (Games Played): integer - number of games played by the player
• GS (Games Started): integer - number of games started by the player
• MP (Minutes Played): integer - total minutes played by the player
• FG (Field Goals): integer - number of field goals made by the player
• FGA (Field Goal Attempts): integer - number of field goal attempts by the player
• FG% (Field Goal Percentage): float - percentage of field goals made by the player
• 3P (3-Point Field Goals): integer - number of 3-point field goals made by the player
• 3PA (3-Point Field Goal Attempts): integer - number of 3-point field goal attempts by the player
• 3P% (3-Point Field Goal Percentage): float - percentage of 3-point field goals made by the player
• 2P (2-Point Field Goals): integer - number of 2-point field goals made by the player
• 2PA (2-point Field Goal Attempts): integer - number of 2-point field goal attempts by the player
• 2P% (2-Point Field Goal Percentage): float - percentage of 2-point field goals made by the player
• eFG% (Effective Field Goal Percentage): float - effective field goal percentage of the player
• FT (Free Throws): integer - number of free throws made by the player
• FTA (Free Throw Attempts): integer - number of free throw attempts by the player
• FT% (Free Throw Percentage): float - percentage of free throws made by the player
• ORB (Offensive Rebounds): integer - number of offensive rebounds by the player
• DRB (Defensive Rebounds): integer - number of defensive rebounds by the player
• TRB (Total Rebounds): integer - total rebounds by the player
• AST (Assists): integer - number of assists made by the player
• STL (Steals): integer - number of steals made by the player
• BLK (Blocks): integer - number of blocks made by the player
• TOV (Turnovers): integer - number of turnovers made by the player
• PF (Personal Fouls): integer - number of personal fouls made by the player
• PTS (Points): integer - total points scored by the player

Source: https://www.basketball-reference.com/ Data Use License: https://www.sports-reference.com/data_use.html?_hstc=180814520.c111ec346015608b5d02af39188b9a67.1693356557509.1693356557509.1693356557509.1&_hssc=180814520.1.1693356557509&_hsfp=2038027543

Abstract AIMS This study aimed to verify th erelation ship between of anthropometric and physical performance variables with game-related statistics in professional elite basketball players during a competition. METHODS Eleven male basketball players were evaluated during 10 weeks in two distinct moments (regular season and playoffs). Overall, 11 variables of physical fitness and 13 variables of game-related statistics were analysed. RESULTS The following significant Pearson’scorrelations were found in regular season: percentage of fat mass with assists (r = -0.62) and steals (r = -0.63); height (r = 0.68), lean mass (r = 0.64), and maximum strength (r = 0.67) with blocks; squat jump with steals (r = 0.63); and time in the T-test with success ful two-point field-goals (r = -0.65), success ful free-throws (r = -0.61), and steals (r = -0.62). However, in playoffs, only stature and lean mass maintained these correlations (p ≤ 0.05). CONCLUSIONS The anthropometric and physical characteristics of the players showed few correlations with the game-related statistics in regular season, and these correlations are even lower in the playoff games of a professional elite Champion ship, wherefore, not being good predictors of technical performance.

The goal of this project was to extract data from an NBA stats website using web scraping techniques and then perform data analysis to create visualizations using Python. The website used was "https://www.basketball-reference.com/", which contains data on players and teams in the NBA. The code for this project can be found on my GitHub repository at "https://github.com/Duggsdaddy/Srihith_I310D.git".

The data was extracted using the BeautifulSoup library in Python, and the data was stored in a Pandas DataFrame. The data was cleaned and processed to remove any unnecessary columns or rows, and the data types of the columns were checked and corrected where necessary.

The data was analyzed using various Python libraries such as Matplotlib, Seaborn, and Plotly to create visualizations like bar graphs, line graphs, and box plots. The visualizations were used to identify trends and patterns in the data.

The project follows ethical web scraping practices by not overwhelming the website with too many requests and by giving proper attribution to the website as the source of the data.

Overall, this project demonstrates how web scraping and data analysis techniques can be used to extract meaningful insights from data available on the internet.

Here's a data dictionary for the table

Player: string - name of the player Pos (Position): string - position played by the player Age: integer - age of the player as of February 1, 2023 Tm (Team): string - team the player belongs to G (Games Played): integer - number of games played by the player GS (Games Started): integer - number of games started by the player MP (Minutes Played): integer - total minutes played by the player FG (Field Goals): integer - number of field goals made by the player FGA (Field Goal Attempts): integer - number of field goal attempts by the player FG% (Field Goal Percentage): float - percentage of field goals made by the player 3P (3-Point Field Goals): integer - number of 3-point field goals made by the player 3PA (3-Point Field Goal Attempts): integer - number of 3-point field goal attempts by the player 3P% (3-Point Field Goal Percentage): float - percentage of 3-point field goals made by the player 2P (2-Point Field Goals): integer - number of 2-point field goals made by the player 2PA (2-point Field Goal Attempts): integer - number of 2-point field goal attempts by the player 2P% (2-Point Field Goal Percentage): float - percentage of 2-point field goals made by the player eFG% (Effective Field Goal Percentage): float - effective field goal percentage of the player FT (Free Throws): integer - number of free throws made by the player FTA (Free Throw Attempts): integer - number of free throw attempts by the player FT% (Free Throw Percentage): float - percentage of free throws made by the player ORB (Offensive Rebounds): integer - number of offensive rebounds by the player DRB (Defensive Rebounds): integer - number of defensive rebounds by the player TRB (Total Rebounds): integer - total rebounds by the player AST (Assists): integer - number of assists made by the player STL (Steals): integer - number of steals made by the player BLK (Blocks): integer - number of blocks made by the player TOV (Turnovers): integer - number of turnovers made by the player PF (Personal Fouls): integer - number of personal fouls made by the player PTS (Points): integer - total points scored by the player

We analyse and compare NBA and Euroleague basketball through box-score statistics in the period from 2000 to 2017. Overall, the quantitative differences between the NBA and Euroleague have decreased and are still decreasing. Differences are even smaller after we adjust for game length and when playoff NBA basketball is considered instead of regular season basketball. The differences in factors that contribute to success are also very small—(Oliver’s) four factors derived from box-score statistics explain most of the variability in team success even if the coefficients are determined for both competitions simultaneously instead of each competition separately. The largest difference is game pace—in the NBA there are more possessions per game. The number of blocks, the defensive rebounding rate and the number of free throws per foul committed are also higher in the NBA, while the number of fouls committed is lower. Most of the differences that persist can be reasonably explained by the contrasts between the better athleticism of NBA players and more emphasis on tactical aspects of basketball in the Euroleague.

We asked German consumers about "Most popular sports activities" and found that "Running/jogging" takes the top spot, while "Rugby" is at the other end of the ranking.These results are based on a representative online survey conducted in 2025 among 20,170 consumers in Germany.

Introduction: This dataset provides comprehensive statistics for NBA players during the 2023-2024 regular season. It encompasses over 400 rows and 30 columns, presenting a wide range of player performance metrics. The data is derived from Basketball Reference, ensuring accuracy and reliability. However, it's worth noting that there may be duplicate player names due to team changes throughout the season (Which will show TOT for the total status of the player).

Columns Description: 1. Rk: Rank 2. Player: Player's name 3. Pos: Position 4. Age: Player's age 5. Tm: Team 6. G: Games played 7. GS: Games started 8. MP: Minutes played per game 9. FG: Field goals per game 10. FGA: Field goal attempts per game 11. FG%: Field goal percentage 12. 3P: 3-point field goals per game 13. 3PA: 3-point field goal attempts per game 14. 3P%: 3-point field goal percentage 15. 2P: 2-point field goals per game 16. 2PA: 2-point field goal attempts per game 17. 2P%: 2-point field goal percentage 18. eFG%: Effective field goal percentage 19. FT: Free throws per game 20. FTA: Free throw attempts per game 21. FT%: Free throw percentage 22. ORB: Offensive rebounds per game 23. DRB: Defensive rebounds per game 24. TRB: Total rebounds per game 25. AST: Assists per game 26. STL: Steals per game 27. BLK: Blocks per game 28. TOV: Turnovers per game 29. PF: Personal fouls per game 30. PTS: Points per game

Acknowledgements: Reference: basketball-reference.com

We asked Spanish consumers about "Most popular sports activities" and found that "Hiking" takes the top spot, while "Rugby" is at the other end of the ranking.These results are based on a representative online survey conducted in 2025 among 7,095 consumers in Spain.

Descriptive data (±SD) for game-related statistical parameters between winning and losing teams during the post-season competitive period.

Dataset of 2604 National Basketball Association (NBA) players who played more than 1000 minutes in the regular seasons from the season 2005-2006 until 2015-2016. The dataset covered several stats related to salary, offensive, and defensive activities such as salary, minutes played, assists, field goal, free throws, offensive rebounds, blocks, steals, and defensive rebounds.

Dataset contains 5313 players, 1947-2025, scraped from https://www.basketball-reference.com/

Features: * Name: Players name * Debut: Year of players debut season * Final: Year of the last game played * Position: Players position(s) * Height: Height of player (inches) * Weight: Weight of player (lbs) * Birthday: Players Date of Birth * School: School(s) player attended * HOF: Hall of fame status (True or False) * Active: If player is currently playing (True or False) * G: amount of games played by player * PTS: average points scored by player per game * TRB: average rebounds by player per game * AST: average assists per game * FG%: field goal percentage * FG3%: three point field goal percentage * FT%: free throw percentage * eFG%: effective field goal percentage * PER: player effieciency rating * WS: win shares

Feature engineering: You can engineer features such as total_career_point, total_career_assists, etc by multiplying average stats by total games (found in G column).

Pandas info:

Descriptive data (±SD) for game-related statistical parameters between the regular and post-season competitive periods.

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Upward arrow (↑) represents a significant increase, arrow down (↓) represents a significant decrease, and dash (—) represents no statistically significant differences.

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!

This dataset includes detailed statistics on basketball players over multiple seasons, with a special focus on their offensive and defensive performance against other players and defenders. The files contain key metrics that can be used for performance analysis, predictions, and strategy evaluation. Ideal for researchers, sports analysts, and basketball enthusiasts.

The dataset provides in-depth insights into player performance, including shooting efficiency, scoring ability, defensive impact, and overall contribution to the team. It covers a wide range of metrics such as Field Goals Made (FGM), Field Goals Attempted (FGA), 3-Point Field Goals Made (FG3M), Free Throws Made (FTM), Rebounds (OREB, DREB), Assists (AST), Steals (STL), Blocks (BLK), Turnovers (TOV), and Personal Fouls (PF).

Additionally, the dataset includes advanced statistics like Plus/Minus (PM), which measures the net impact a player has on the game when they are on the court. These metrics can be extremely useful for analyzing player efficiency, defensive strategies, and overall team dynamics.

The dataset is structured in separate CSV files, making it easy to perform both player-specific and team-wide analyses. It includes data on head-to-head player vs. defender matchups, offering insights into how individual players perform when matched up against specific defenders. This is valuable for analyzing the effectiveness of defensive players and evaluating matchup strategies.

With data spanning the last 10 years of NBA seasons, users can explore trends in player performance over time, uncovering patterns and shifts in gameplay styles. Whether you are developing predictive models, conducting historical analysis, or simply diving into the numbers to better understand the game, this dataset is a valuable resource for any basketball-related project.