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https://github.com/noahgift/socialpowernba
Social Power in the NBA (Comparing on the court performance with Social Influence in R and Python)
https://github.com/noahgift/socialpowernba
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
Last synced: 6 days ago
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Social Power in the NBA (Comparing on the court performance with Social Influence in R and Python)
- Host: GitHub
- URL: https://github.com/noahgift/socialpowernba
- Owner: noahgift
- License: other
- Created: 2017-07-10T15:48:16.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2021-02-27T04:35:40.000Z (over 3 years ago)
- Last Synced: 2024-09-24T21:33:18.563Z (9 days ago)
- 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
- Language: Jupyter Notebook
- Homepage: https://www.amazon.com/Pragmatic-AI-Introduction-Cloud-based-Learning/dp/0134863860
- Size: 8.53 MB
- Stars: 27
- Watchers: 7
- Forks: 42
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: license.md
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# Introduction
This 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)
## Pragmatic AI Labs
This project was produced by [Pragmatic AI Labs](https://paiml.com/). You can continue learning about these topics by:
* Buying a copy of [Pragmatic AI: An Introduction to Cloud-Based Machine Learning](https://amzn.to/2LFLVEg)
* Viewing more content at [noahgift.com](https://noahgift.com/)
* Viewing more content at [Pragmatic AI Labs](https://paiml.com/)
* 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/)
## Strata 2018 Talk
* [What is the relationship between social influence and the NBA](https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/63606)
* [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)
* [Video of Strata Talk on Safari](https://www.safaribooksonline.com/library/view/strata-data-conference/9781492025955/video319171.html)
## IBM Developerworks Articles on Project
* *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/
* *Exploring the individual NBA players*: https://www.ibm.com/developerworks/analytics/library/ba-social-influence-python-pandas-machine-learning-r-2/
## Kaggle Version of Project
You can also see Kaggle Notebooks here:
* [Kaggle Kernel NBA Player Influence, Salary and Performance ](https://www.kaggle.com/noahgift/nba-player-power-influence-and-performance)
* [Kaggle Kernel Team Valuation Performance ](https://www.kaggle.com/noahgift/nba-team-valuation-exploration)
## Data Legend
### [Exploring Team Valuation Notebook](https://github.com/noahgift/socialpowernba/blob/master/notebooks/exploring_team_valuation_nba.ipynb)
This notebook has the following data legend:
### [Exploring Team Valuation Dataset created](https://github.com/noahgift/socialpowernba/blob/master/data/nba_2017_att_val_elo_win_housing_cluster.csv)
* TEAM: Name of the NBA Team
* GMS: Games Played
* PCT_ATTENDANCE: Average % Attendance of capacity (note some teams were over capacity as an averag)
* WINNING_SEASON: If the team won over 50% of their games, it was 1, otherwise 0.
* TOTAL_ATTENDANCE_MILLIONS: Total season attendance in the millions.
* VALUE_MILLIONS: Valuation of the team in millions
* ELO: https://en.wikipedia.org/wiki/Elo_rating_system
* CONF: Eastern or Western Conference
* COUNTY: The county where the team is located
* MEDIAN_HOME_PRICE_COUNTY_MILLIONS: Median Home Price
* COUNTY_POPULATION_MILLIONS: The Population of the county in Millions
* cluster: A cluster created by KMeans clustering (shown in notebook)
### [Exploring Team Valuation Notebook](https://github.com/noahgift/socialpowernba/blob/master/notebooks/nba_player_power_influence_performance.ipynb)
### [Exploring Team Valuation Dataset created](https://github.com/noahgift/socialpowernba/blob/master/data/nba_2017_endorsement_full_stats.csv)
* PLAYER: NBA Player Name
* TEAM: NBA Team
* SALARY_MILLIONS: Salary paid to player in Millions
* ENDORSEMENT_MILLIONS: Endorsements paid to player in Millions
* PCT_ATTENDANCE_STADIUM: Average % attendance in stadium
* ATTENDANCE_TOTAL_BY_10K
* FRANCHISE_VALUE_100_MILLION
* ELO_100X: https://en.wikipedia.org/wiki/Elo_rating_system/100
* CONF: Eastern or Western Conference (Even split between all teams between both conferences)
* POSITION: Position of the player
* AGE
* MP: Minutes/Games Average
* GP: Games played
* MPG: Minutes/Games Average
* WINS_RPM: Wins attributed to individual player performance. One of the best metrics of overall impact on team.
* PLAYER_TEAM_WINS: Wins for the team the playes is on.
* WIKIPEDIA_PAGEVIEWS_10K: Pageviews of player divided by 10 thousand
TWITTER_FAVORITE_COUNT_1K: Twitter favorites of player profile divided by 1 thousand.
# Social Power, Influence and Performance in the NBA
Social Power in the NBA (Comparing on the court performance with Social Influence)
# Data Exploration
## Player Impact Estimation
## Social Power, Performance and Salary
## Valuation vs Attendance
## ELO vs Attendance
## ELO Correlation Heatmap
## REAL PLUS MINUS Wins, POINTS and SALARY
## 3D Plot
## ALL Data Correlation Heatmap
## Explore Juypter Notebooks
[Juypter Noteboooks Social Power](https://github.com/noahgift/socialpowernba/tree/master/notebooks)
## Social Money
## Social Power and Performance
## Explore Raw Data Here
* [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)
- This data was collected from multiple sources: ESPN, Basketball-Reference, Twitter, Wikipedia, and Forbes
### Additional Related Topics from Noah Gift
His most recent books are:
* [Pragmatic A.I.: An introduction to Cloud-Based Machine Learning (Pearson, 2018)](https://www.amazon.com/Pragmatic-AI-Introduction-Cloud-Based-Analytics/dp/0134863860)
* [Python for DevOps (O'Reilly, 2020)](https://www.amazon.com/Python-DevOps-Ruthlessly-Effective-Automation/dp/149205769X).
His most recent video courses are:
* [Essential Machine Learning and A.I. with Python and Jupyter Notebook LiveLessons (Pearson, 2018)](https://learning.oreilly.com/videos/essential-machine-learning/9780135261118)
* [AWS Certified Machine Learning-Specialty (ML-S) (Pearson, 2019)](https://learning.oreilly.com/videos/aws-certified-machine/9780135556597)
* [Python for Data Science Complete Video Course Video Training (Pearson, 2019)](https://learning.oreilly.com/videos/python-for-data/9780135687253)
* [AWS Certified Big Data - Specialty Complete Video Course and Practice Test Video Training (Pearson, 2019)](https://learning.oreilly.com/videos/aws-certified-big/9780135772324)
* [Building A.I. Applications on Google Cloud Platform (Pearson, 2019)](https://learning.oreilly.com/videos/building-ai-applications/9780135973462)
* [Pragmatic AI and Machine Learning Core Principles (Pearson, 2019)](https://learning.oreilly.com/videos/pragmatic-ai-and/9780136554714)
* [Data Engineering with Python and AWS Lambda (Pearson, 2019)](https://learning.oreilly.com/videos/data-engineering-with/9780135964330)
His most recent online courses are:
* [Microservices with this Udacity DevOps Nanodegree (Udacity, 2019)](https://www.udacity.com/course/cloud-dev-ops-nanodegree--nd9991)
* [Command Line Automation in Python (DataCamp, 2019)](https://www.datacamp.com/instructors/ndgift)