Sports Analytics

Dive into the world of sports analytics through a comprehensive series of videos covering various aspects of data science in sports. Learn how to land a job in sports analytics, explore different types of analytics projects, and analyze controversial topics like the optimal placement of the NBA 3-point line. Master techniques for scraping NBA data using Python, understand the fundamentals of sports analytics, and explore the intersection of sports betting and data science. Discover recommended sports analytics books, examine golf statistics including driving distance and accuracy, and learn about strokes gained analysis. Apply data science concepts to predict NBA player minutes and simulate games, use multiple linear regression to forecast season-long NBA wins, and investigate the impact of cheating in baseball. Gain insights from industry professionals, including a talk for MBA students and a discussion on streaming data science. Finally, put your skills to the test by creating a machine learning model for March Madness predictions.

How YOU Can Land a Sports Analytics Job.
The 4 Types of Sports Analytics Projects.
How Far Should the NBA 3-Point Line Actually Be?.
By The Numbers: Where Should The NBA Put a 4 Point Line?.
How to Scrape NBA Data Using the nba_api Python Module.
What is Sports Analytics Really?.
Collision Course: Sports Betting + Data Science.
5 Sports Analytics Books to Get You Started.
Golf: Would You Rather Be the LONGEST or STRAIGHTEST Driver on the PGA Tour?.
Data Science in Golf: PGA Merchandise Show 2020.
Golf STATS: Strokes Gained Explained.
The Best Way to Predict NBA Minutes Played.
How to Simulate NBA Games in Python.
Predicting Season Long NBA Wins Using Multiple Linear Regression.
How Much Did Cheating Help the Astros Win? (What the Numbers Say).
Data Science in Sports - Talk for Northwestern (Kellogg) MBA Students.
Sports Analytics & Streaming Data Science on Twitch (Nick Wan) - KNN EP. 08.
MARCH MADNESS - Will My Machine Learning Model Beat Your Bracket?.