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

This project was produced by Pragmatic AI Labs. You can continue learning about these topics by:

• Buying a copy of Pragmatic AI: An Introduction to Cloud-Based Machine Learning
• Viewing more content at noahgift.com
• Viewing more content at Pragmatic AI Labs
• Hear more about the some of the topics covered in TWIML podcast

• What is the relationship between social influence and the NBA

• Slides on What is the relationship between social influence and the NBA

• Video of Strata Talk on Safari

• 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/

You can also see Kaggle Notebooks here:

• Kaggle Kernel NBA Player Influence, Salary and Performance

• Kaggle Kernel Team Valuation Performance

This notebook has the following data legend:

• 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)

• 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 in the NBA (Comparing on the court performance with Social Influence)

Juypter Noteboooks Social Power

• NBA 2016-2017 REAL PLUS MINUS Wins, POINTS, SALARY, Wikipedia, Twitter

  • 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)
• Python for DevOps (O'Reilly, 2020). 

His most recent video courses are:

• Essential Machine Learning and A.I. with Python and Jupyter Notebook LiveLessons (Pearson, 2018)
• AWS Certified Machine Learning-Specialty (ML-S) (Pearson, 2019)
• Python for Data Science Complete Video Course Video Training (Pearson, 2019)
• AWS Certified Big Data - Specialty Complete Video Course and Practice Test Video Training (Pearson, 2019)
• Building A.I. Applications on Google Cloud Platform (Pearson, 2019)
• Pragmatic AI and Machine Learning Core Principles (Pearson, 2019)
• Data Engineering with Python and AWS Lambda (Pearson, 2019)

His most recent online courses are:

• Microservices with this Udacity DevOps Nanodegree (Udacity, 2019)
• Command Line Automation in Python (DataCamp, 2019)