Dribble Analytics is a fully open-source basketball analytics blog. All data, code, and graphs are available on my GitHub here.
Click on each link to view the Github repo of that post’s data and Python code.
Is wingspan or height a better predictor of NBA defense?
Using machine learning to predict the top shooters in the 2018 draft
Using machine learning to predict the best defenders in the 2018 draft
Using machine learning to predict the best distributors in the 2018 draft
Finding the least and most deserving MVPs of the last decade
Using machine learning to predict hall of famers and all stars from the 2017 draft class
Visualizing how much the top 10 NBA players would earn without max contracts
Using machine learning to predict the 2019 MVP: mid-season predictions
Using machine learning to predict the 2019 MVP: All-Star break predictions
Predicting the 2019 All-NBA teams with machine learning
The code for “Using machine learning to predict the 2019 MVP and All-NBA teams: end of season predictions” is in both the MVP repository and the All-NBA repository.
Defining NBA players by role with k-means clustering
A new approach to analyzing the value of tanking: Markov chains
All 2019 draft-related projects are available under individual folders here.
Introducing the unicorn index: defining player uniqueness
Introducing DAVIS: a holistic and transparent defensive stat
Determining the 2010s NBA All-Decade team with machine learning
Introducing true win shares: estimating team win probability given player stats
Using machine learning to find the best and worst value contracts
Predicting the 2020 MVP with linear models
Predicting the 2020 All-NBA teams with a deep neural network
Generating fake Woj and Shams tweets with AI
Introducing LEBRON: Longevity Estimate Based on Recurrent Optimized Network