{"id":21293360,"url":"https://github.com/ejw-data/proj-nba-eda","last_synced_at":"2026-04-14T04:31:24.574Z","repository":{"id":106650295,"uuid":"545177184","full_name":"ejw-data/proj-nba-eda","owner":"ejw-data","description":"NBA exploratory data analysis project with Pandas and Tableau","archived":false,"fork":false,"pushed_at":"2023-03-06T01:49:56.000Z","size":463,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-07-18T09:46:10.254Z","etag":null,"topics":["nba","numpy","pandas","python","tableau"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ejw-data.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-10-03T23:15:30.000Z","updated_at":"2025-05-28T20:56:20.000Z","dependencies_parsed_at":null,"dependency_job_id":"e5c792a1-261c-4662-9b10-89605c3235c5","html_url":"https://github.com/ejw-data/proj-nba-eda","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ejw-data/proj-nba-eda","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ejw-data%2Fproj-nba-eda","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ejw-data%2Fproj-nba-eda/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ejw-data%2Fproj-nba-eda/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ejw-data%2Fproj-nba-eda/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ejw-data","download_url":"https://codeload.github.com/ejw-data/proj-nba-eda/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ejw-data%2Fproj-nba-eda/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31782736,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-14T02:24:21.117Z","status":"ssl_error","status_checked_at":"2026-04-14T02:24:20.627Z","response_time":153,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["nba","numpy","pandas","python","tableau"],"created_at":"2024-11-21T13:54:28.178Z","updated_at":"2026-04-14T04:31:24.559Z","avatar_url":"https://github.com/ejw-data.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# proj-nba-eda\n\nAuthor:  Erin James Wills, ejw.data@gmail.com  \n\n![Tableau Banner](./images/nba-tableau.png)  \n\u003ccite\u003ePhoto by \u003ca href=\"https://unsplash.com/@markusspiske?utm_source=unsplash\u0026utm_medium=referral\u0026utm_content=creditCopyText\"\u003eMarkus Spiske\u003c/a\u003e on \u003ca href=\"https://unsplash.com/s/photos/basketball?utm_source=unsplash\u0026utm_medium=referral\u0026utm_content=creditCopyText\"\u003eUnsplash\u003c/a\u003e\u003c/cite\u003e\n\n\u003cbr\u003e\n\n## Overview  \n\u003chr\u003e  \nNBA exploratory data analysis project with Pandas and Tableau \n\n\u003cbr\u003e\n\n## Technologies    \n*  Tableau\n\n\u003cbr\u003e\n\n## Data Source  \n\n*  NBA Revenue - https://runrepeat.com/nba-revenue-statistics  \n*  NBA Salaries - https://hoopshype.com/salaries/  \n*  NBA Salaries - https://data.world/datadavis/nba-salaries \n*  Alternate Pay - https://www.spotrac.com/nba/cash/   \n*  All Stars - https://data.world/gmoney/nba-all-stars-2000-2016 \n\u003cbr\u003e\n\n## Setup and Installation   \n1.  A copy of the tableau workbook is `xxxxxx_v2022_1.twbx`.\n1.  The dashboard can be viewed at https://ejw-data.github.io/xxxxxx/.  \n\n\u003cbr\u003e\n\n## Examples  \nTBD\n\n\n## References:\n\nSomething to think about:  https://www.si.com/nba/2018/09/21/nba-teams-revenue-spending-breakdown-small-large-market  \n\nDefinitions:  \n\n**Hard cap**: A team salary figure (set at the apron) that, once triggered, cannot be crossed for any reason. A hard cap can be triggered in one of three ways:  \n*  A team uses the non-taxpayer mid-level exception.  \n*  A team uses the bi-annual exception.  \n*  A team acquires a player through a sign-and-trade.  \n\n\nhttps://www.cbssports.com/nba/news/nba-salary-cap-explained-glossary-for-the-terms-you-need-to-know-ahead-of-basketball-free-agency/#:~:text=Hard%20cap%3A%20A%20team%20salary,uses%20the%20bi%2Dannual%20exception.\n\n## ETL\n\nTables\n* player\n    - player_id\n    - player_name\n    - birthdate\n    - first_game_date\n    - retirement_date\n\n\n* player-injury\n    - player_id\n    - injury\n    - start_date\n    - end_date\n\n\n* team\n    - team_id\n    - team_name\n    - team_abbrev\n    - team_city\n    - team_mascot\n    - start_date\n    - end_date\n\n* games (schedule??)\n    - game_id\n    - home_team\n    - away_team\n    - location\n    - arena\n    - start_date\n    - start_time_et\n    - start_time_local\n\n* player-team\n    - player_id\n    - team_id\n    - position\n    - start_date\n    - end_date\n\n* play-by-play\n    - game_id\n    - event_id\n    - event_description\n    - game_time\n    - player_id\n\n* players-substitution\n    - player_id\n    - game_id\n    - team_id\n    - quarter\n    - time\n    - type (sub-in or sub-out)\n\n* players-scored\n    - player_id\n    - type_score (foul_shot, layup, dunk, 3ptr, etc)\n    - location\n    - pts_scored\n    - quarter\n    - time\n    - shotclock\n\n* players-fouls\n    - player_id\n    - type \n    - player_fouled\n    - shooting_foul\n    - pts_scored\n\n* players-defense\n    * player_id\n    * type (steal, block, turnover)\n    * offense_player\n    * quarter\n    * time\n\n* players-assists\n    - player_id\n    - type (assist)\n    - player_scored\n\n* players-rebounds\n    - player_id\n    - type (offensive/defensive)\n    - quarter\n    - time\n    - next play\n\n* player-salary\n    - player_id\n    - season\n    - income_source\n    - conditions\n\n* team-caps\n\n\n*** What to record for p-b-p\n    - event log\n        - event_id\n        - raw_description\n        - table\n        - _id\n        - quarter\n        - time","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fejw-data%2Fproj-nba-eda","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fejw-data%2Fproj-nba-eda","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fejw-data%2Fproj-nba-eda/lists"}