NBA Players Performance by Kaggle | Sports

About this Dataset

NBA Players Performance

Players Performance & Statistics

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
GP	Number of games played. (Integer)
Player	Player name. (String)
Position	Player position. (String)

File: rebounds.csv

Column name	Description
GP	Number of games played. (Integer)
Player	Player name. (String)
Position	Player position. (String)

File: scoring.csv

Column name	Description
GP	Number of games played. (Integer)
Player	Player name. (String)
Position	Player position. (String)

File: steals.csv

Column name	Description
GP	Number of games played. (Integer)
Player	Player name. (String)
Position	Player position. (String)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

Assists Turnovers

@kaggle.thedevastator_unlocking_the_secrets_of_nba_player_performance.assists_turnovers

20.82 KB
279 rows
16 columns


CREATE TABLE assists_turnovers (
  "unnamed_0" BIGINT,
  "gp_games_played" BIGINT,
  "gs_games_started" BIGINT,
  "mpg_minutes_per_game" DOUBLE,
  "oreb_offensive_rebounds" DOUBLE,
  "dreb_defensive_rebounds" DOUBLE,
  "reb_total_rebounds" DOUBLE,
  "rpg_rebounds_per_game" DOUBLE,
  "ast_total_assists" DOUBLE,
  "apg_assists_per_game" DOUBLE,
  "to_turnovers" DOUBLE,
  "topg_turnovers_per_game" DOUBLE,
  "a_to_assists_per_turnover" DOUBLE,
  "player" VARCHAR,
  "position" VARCHAR,
  "team" VARCHAR
);

Blocks

@kaggle.thedevastator_unlocking_the_secrets_of_nba_player_performance.blocks

14.61 KB
279 rows
10 columns


CREATE TABLE blocks (
  "unnamed_0" BIGINT,
  "gp_games_played" BIGINT,
  "gs_games_started" BIGINT,
  "stl_total_steals" DOUBLE,
  "spg_steals_per_game" DOUBLE,
  "blk_total_blocks" DOUBLE,
  "bpg_blocks_per_game" DOUBLE,
  "player" VARCHAR,
  "position" VARCHAR,
  "team" VARCHAR
);

Fouls Minutes

@kaggle.thedevastator_unlocking_the_secrets_of_nba_player_performance.fouls_minutes

19.6 KB
279 rows
15 columns


CREATE TABLE fouls_minutes (
  "unnamed_0" BIGINT,
  "gp_games_played" BIGINT,
  "gs_games_started" BIGINT,
  "blk_total_blocks" DOUBLE,
  "bpg_blocks_per_game" DOUBLE,
  "mpg_minutes_per_game" DOUBLE,
  "pf_total_personal_fouls" DOUBLE,
  "pfg_personal_fouls_per_game" DOUBLE,
  "flg_flagrant_fouls" DOUBLE,
  "tf_technical_fouls" DOUBLE,
  "ej_ejections" DOUBLE,
  "dsq_disqualifications" DOUBLE,
  "player" VARCHAR,
  "position" VARCHAR,
  "team" VARCHAR
);

Rebounds

@kaggle.thedevastator_unlocking_the_secrets_of_nba_player_performance.rebounds

27.13 KB
279 rows
21 columns


CREATE TABLE rebounds (
  "unnamed_0" BIGINT,
  "gp_games_played" BIGINT,
  "gs_games_started" BIGINT,
  "mpg_minutes_per_game" DOUBLE,
  "ppg_points_per_game" DOUBLE,
  "fgm_field_goals_made" DOUBLE,
  "fga_field_goals_attempted" DOUBLE,
  "fg_field_goal_percentage" DOUBLE,
  "n_3fgm_three_point_field_goals_made" DOUBLE,
  "n_3fga_three_point_field_goals_attempted" DOUBLE,
  "n_3fg_three_point_field_goal_percentage" VARCHAR,
  "ftm_free_throws_made" DOUBLE,
  "fta_free_throws_attempted" DOUBLE,
  "ft_free_throw_percentage" DOUBLE,
  "oreb_offensive_rebounds" DOUBLE,
  "dreb_defensive_rebounds" DOUBLE,
  "reb_total_rebounds" DOUBLE,
  "rpg_rebounds_per_game" DOUBLE,
  "player" VARCHAR,
  "position" VARCHAR,
  "team" VARCHAR
);

Scoring

@kaggle.thedevastator_unlocking_the_secrets_of_nba_player_performance.scoring

27.41 KB
250 rows
17 columns


CREATE TABLE scoring (
  "unnamed_0" BIGINT,
  "gp_games_played" BIGINT,
  "gs_games_started" BIGINT,
  "mpg_minutes_per_game" DOUBLE,
  "ppg_points_per_game" DOUBLE,
  "fgm_field_goals_made" BIGINT,
  "fga_field_goals_attempted" BIGINT,
  "fg_field_goal_percentage" DOUBLE,
  "n_3fgm_three_point_field_goals_made" BIGINT,
  "n_3fga_three_point_field_goals_attempted" BIGINT,
  "n_3fg_three_point_field_goal_percentage" VARCHAR,
  "ftm_free_throws_made" BIGINT,
  "fta_free_throws_attempted" BIGINT,
  "ft_free_throw_percentage" VARCHAR,
  "player" VARCHAR,
  "position" VARCHAR,
  "team" VARCHAR
);

Steals

@kaggle.thedevastator_unlocking_the_secrets_of_nba_player_performance.steals

17.15 KB
279 rows
13 columns


CREATE TABLE steals (
  "unnamed_0" BIGINT,
  "gp_games_played" BIGINT,
  "gs_games_started" BIGINT,
  "ast_total_assists" DOUBLE,
  "apg_assists_per_game" DOUBLE,
  "to_turnovers" DOUBLE,
  "topg_turnovers_per_game" DOUBLE,
  "a_to_assists_per_turnover" DOUBLE,
  "stl_total_steals" DOUBLE,
  "spg_steals_per_game" DOUBLE,
  "player" VARCHAR,
  "position" VARCHAR,
  "team" VARCHAR
);