{"id":14977171,"url":"https://github.com/noahgift/socialpowernba","last_synced_at":"2025-10-28T01:30:48.214Z","repository":{"id":54260356,"uuid":"96794900","full_name":"noahgift/socialpowernba","owner":"noahgift","description":"Social Power in the NBA (Comparing on the court performance with Social Influence in R and Python)","archived":false,"fork":false,"pushed_at":"2021-02-27T04:35:40.000Z","size":8944,"stargazers_count":27,"open_issues_count":1,"forks_count":42,"subscribers_count":7,"default_branch":"master","last_synced_at":"2024-09-28T23:23:51.673Z","etag":null,"topics":["court-performance","ggplot2","influence","ipython-notebook","jupyter-notebook","kaggle","kaggle-dataset","machine-learning","machine-learning-algorithms","ml","nba","pie","prediction","python","r","salary","social","social-media","social-network","social-networks"],"latest_commit_sha":null,"homepage":"https://www.amazon.com/Pragmatic-AI-Introduction-Cloud-based-Learning/dp/0134863860","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/noahgift.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"license.md","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2017-07-10T15:48:16.000Z","updated_at":"2024-01-12T10:35:35.000Z","dependencies_parsed_at":"2022-08-13T10:20:17.018Z","dependency_job_id":null,"html_url":"https://github.com/noahgift/socialpowernba","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/noahgift%2Fsocialpowernba","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/noahgift%2Fsocialpowernba/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/noahgift%2Fsocialpowernba/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/noahgift%2Fsocialpowernba/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/noahgift","download_url":"https://codeload.github.com/noahgift/socialpowernba/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":219860278,"owners_count":16556020,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["court-performance","ggplot2","influence","ipython-notebook","jupyter-notebook","kaggle","kaggle-dataset","machine-learning","machine-learning-algorithms","ml","nba","pie","prediction","python","r","salary","social","social-media","social-network","social-networks"],"created_at":"2024-09-24T13:55:14.299Z","updated_at":"2025-10-28T01:30:40.819Z","avatar_url":"https://github.com/noahgift.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Introduction\n\nThis project and the data explores the relationship between Social Media, Salary, Influence, Performance and Team Valuation in the NBA.  This is covered in [Chapter 6 of Pragmatic AI](https://amzn.to/2LFLVEg)\n\n## Pragmatic AI Labs\n![alt text](https://paiml.com/images/logo_with_slogan_white_background.png)\n\nThis project was produced by [Pragmatic AI Labs](https://paiml.com/).  You can continue learning about these topics by:\n\n*   Buying a copy of [Pragmatic AI: An Introduction to Cloud-Based Machine Learning](https://amzn.to/2LFLVEg)\n*   Viewing more content at [noahgift.com](https://noahgift.com/)\n*   Viewing more content at [Pragmatic AI Labs](https://paiml.com/)\n*   Hear more about the some of the topics covered in [TWIML podcast](https://twimlai.com/twiml-talk-158-growth-hacking-sports-w-machine-learning-with-noah-gift/)\n\n\n## Strata 2018 Talk\n\n* [What is the relationship between social influence and the NBA](https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/63606)\n\n* [Slides on What is the relationship between social influence and the NBA](https://cdn.oreillystatic.com/en/assets/1/event/269/What%20is%20the%20relationship%20between%20social%20influence%20and%20the%20NBA_%20Presentation.pdf)\n\n* [Video of Strata Talk on Safari](https://www.safaribooksonline.com/library/view/strata-data-conference/9781492025955/video319171.html)\n\n## IBM Developerworks Articles on Project\n\n* *Explore valuation and attendance using data science and machine learning*:  https://www.ibm.com/developerworks/library/ba-social-influence-python-pandas-machine-learning-r-1/\n\n* *Exploring the individual NBA players*:  https://www.ibm.com/developerworks/analytics/library/ba-social-influence-python-pandas-machine-learning-r-2/\n\n## Kaggle Version of Project\n\nYou can also see Kaggle Notebooks here:  \n\n* [Kaggle Kernel NBA Player Influence, Salary and Performance ](https://www.kaggle.com/noahgift/nba-player-power-influence-and-performance)\n\n* [Kaggle Kernel Team Valuation Performance ](https://www.kaggle.com/noahgift/nba-team-valuation-exploration)\n\n## Data Legend\n\n### [Exploring Team Valuation Notebook](https://github.com/noahgift/socialpowernba/blob/master/notebooks/exploring_team_valuation_nba.ipynb)\n\nThis notebook has the following data legend:\n\n### [Exploring Team Valuation Dataset created](https://github.com/noahgift/socialpowernba/blob/master/data/nba_2017_att_val_elo_win_housing_cluster.csv)\n\n* TEAM:  Name of the NBA Team\n* GMS:  Games Played\n* PCT_ATTENDANCE:  Average % Attendance of capacity (note some teams were over capacity as an averag)\n* WINNING_SEASON:  If the team won over 50% of their games, it was 1, otherwise 0.\n* TOTAL_ATTENDANCE_MILLIONS:  Total season attendance in the millions.\n* VALUE_MILLIONS:  Valuation of the team in millions\n* ELO:  https://en.wikipedia.org/wiki/Elo_rating_system\n* CONF:  Eastern or Western Conference\n* COUNTY:  The county where the team is located\n* MEDIAN_HOME_PRICE_COUNTY_MILLIONS:  Median Home Price\n* COUNTY_POPULATION_MILLIONS:  The Population of the county in Millions\n* cluster:  A cluster created by KMeans clustering (shown in notebook)\n\n### [Exploring Team Valuation Notebook](https://github.com/noahgift/socialpowernba/blob/master/notebooks/nba_player_power_influence_performance.ipynb)\n\n### [Exploring Team Valuation Dataset created](https://github.com/noahgift/socialpowernba/blob/master/data/nba_2017_endorsement_full_stats.csv)\n\n* PLAYER:  NBA Player Name\n* TEAM:  NBA Team\n* SALARY_MILLIONS:  Salary paid to player in Millions\n* ENDORSEMENT_MILLIONS:  Endorsements paid to player in Millions\n* PCT_ATTENDANCE_STADIUM:  Average % attendance in stadium\n* ATTENDANCE_TOTAL_BY_10K\n* FRANCHISE_VALUE_100_MILLION\n* ELO_100X:  https://en.wikipedia.org/wiki/Elo_rating_system/100\n* CONF:  Eastern or Western Conference (Even split between all teams between both conferences)\n* POSITION:  Position of the player\n* AGE\n* MP:   Minutes/Games Average\n* GP:  Games played\n* MPG:  Minutes/Games Average\n* WINS_RPM:  Wins attributed to individual player performance.  One of the best metrics of overall impact on team.\n* PLAYER_TEAM_WINS:  Wins for the team the playes is on.  \n* WIKIPEDIA_PAGEVIEWS_10K:  Pageviews of player divided by 10 thousand\nTWITTER_FAVORITE_COUNT_1K:  Twitter favorites of player profile divided by 1 thousand.\n\n# Social Power, Influence and Performance in the NBA\nSocial Power in the NBA (Comparing on the court performance with Social Influence)\n\n![Social Power Data Sources](https://user-images.githubusercontent.com/58792/28694940-19e6e532-72e1-11e7-9b62-0796e8ea140b.png)\n\n# Data Exploration\n\n## Player Impact Estimation\n![NBA 2016-2017 Season PIE](https://user-images.githubusercontent.com/58792/28027382-bd7f5108-654d-11e7-8ed1-299a880714cd.png)\n\n## Social Power, Performance and Salary\n![NBA 2016-2017 Season Twitter, Salary and Performance](https://user-images.githubusercontent.com/58792/28044183-b873238c-658a-11e7-90b7-bd923aeb1e32.png)\n\n## Valuation vs Attendance\n\n![NBA 2016-2017 Season Valuation Vs Attendance](https://user-images.githubusercontent.com/58792/28756721-c213f670-7528-11e7-8988-366b461e8992.png)\n\n## ELO vs Attendance\n\n![NBA 2016-2017 ELO Score Vs Attendance](https://user-images.githubusercontent.com/58792/28759207-2b139d9e-754f-11e7-9695-9a9083e1fb9c.png)\n\n## ELO Correlation Heatmap \n\n![NBA 2016-2017 ELO, Attendance, Valuation Heatmap](https://user-images.githubusercontent.com/58792/28759996-25da9680-7558-11e7-9168-85989b7d63c9.png)\n\n## REAL PLUS MINUS Wins, POINTS and SALARY\n![NBA 2016-2017 REAL PLUS MINUS Wins, POINTS and SALARY](https://user-images.githubusercontent.com/58792/28798971-5bf50158-75fb-11e7-9090-290b0703b2aa.png)\n\n## 3D Plot\n![3D Plot](https://user-images.githubusercontent.com/58792/36056809-7f87a266-0dbc-11e8-8877-9bb87905adbd.png)\n\n## ALL Data Correlation Heatmap\n\n![NBA 2016-2017 Correlation Heatmap REAL PLUS MINUS Wins, POINTS, SALARY, Wikipedia, Twitter](https://user-images.githubusercontent.com/58792/28804798-423a049c-761a-11e7-92ca-bc60bec6c147.png)\n\n## Explore Juypter Notebooks\n\n[Juypter Noteboooks Social Power](https://github.com/noahgift/socialpowernba/tree/master/notebooks)\n\n## Social Money\n\n![NBA 2016-2017 Social Power, Influence and Performance Heatmap](https://user-images.githubusercontent.com/58792/28856405-adc4dd8a-76f7-11e7-8d9a-08d3b04369de.png)\n\n## Social Power and Performance\n![NBA 2016-2017 Social Power and Performance Heatmap](https://user-images.githubusercontent.com/58792/28851989-e80b35f6-76da-11e7-9497-1f69dc3c1134.png)\n\n![NBA 2016-2017 Social Power and Performance Correlation Heatmap](https://user-images.githubusercontent.com/58792/28857433-4f640d04-76fe-11e7-9b44-808df9f32c5a.png)\n\n## Explore Raw Data Here\n\n* [NBA 2016-2017 REAL PLUS MINUS Wins, POINTS, SALARY, Wikipedia, Twitter](https://github.com/noahgift/socialpowernba/blob/master/data/nba_2017_players_with_salary_wiki_twitter.csv) \n  - This data was collected from multiple sources:  ESPN, Basketball-Reference, Twitter, Wikipedia, and Forbes\n  \n  ### Additional Related Topics from Noah Gift\n\nHis most recent books are:\n\n*   [Pragmatic A.I.:   An introduction to Cloud-Based Machine Learning (Pearson, 2018)](https://www.amazon.com/Pragmatic-AI-Introduction-Cloud-Based-Analytics/dp/0134863860)\n*   [Python for DevOps (O'Reilly, 2020)](https://www.amazon.com/Python-DevOps-Ruthlessly-Effective-Automation/dp/149205769X). \n\nHis most recent video courses are:\n\n*   [Essential Machine Learning and A.I. with Python and Jupyter Notebook LiveLessons (Pearson, 2018)](https://learning.oreilly.com/videos/essential-machine-learning/9780135261118)\n*   [AWS Certified Machine Learning-Specialty (ML-S) (Pearson, 2019)](https://learning.oreilly.com/videos/aws-certified-machine/9780135556597)\n*   [Python for Data Science Complete Video Course Video Training (Pearson, 2019)](https://learning.oreilly.com/videos/python-for-data/9780135687253)\n*   [AWS Certified Big Data - Specialty Complete Video Course and Practice Test Video Training (Pearson, 2019)](https://learning.oreilly.com/videos/aws-certified-big/9780135772324)\n*   [Building A.I. Applications on Google Cloud Platform (Pearson, 2019)](https://learning.oreilly.com/videos/building-ai-applications/9780135973462)\n*   [Pragmatic AI and Machine Learning Core Principles (Pearson, 2019)](https://learning.oreilly.com/videos/pragmatic-ai-and/9780136554714)\n*   [Data Engineering with Python and AWS Lambda (Pearson, 2019)](https://learning.oreilly.com/videos/data-engineering-with/9780135964330)\n\nHis most recent online courses are:\n\n*   [Microservices with this Udacity DevOps Nanodegree (Udacity, 2019)](https://www.udacity.com/course/cloud-dev-ops-nanodegree--nd9991)\n*   [Command Line Automation in Python (DataCamp, 2019)](https://www.datacamp.com/instructors/ndgift)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnoahgift%2Fsocialpowernba","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnoahgift%2Fsocialpowernba","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnoahgift%2Fsocialpowernba/lists"}