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About Me

I’m a college freshman and lifelong Celtics fan. I’ve been interested in statistics for a long time, and am starting Dribble Analytics to explore my interest in basketball analytics while learning Python and data science. To read more about the blog and my story, check out the “About” page.

Contact me at: the name of this blog [at] gmail [dot] com

Dribble Analytics

open-source basketball analytics

June 11, 2020June 11, 2020

Introducing LEBRON: Longevity Estimate Based on Recurrent Optimized Network

Projecting player longevity and progression is extremely hard. Often, people follow conventional time frames. Here, we present a new metric that uses a deep learning sequence model to project players’ future All-NBA probabilities.

Posted in Uncategorized. Leave a comment
February 24, 2020February 27, 2020

Generating fake Woj and Shams tweets with AI

By training a machine learning model on historical tweets from Adrian Wojnarowski and Shams Charania, we can generate fake NBA news tweets.

Posted in Uncategorized. 1 Comment
February 4, 2020February 4, 2020

Predicting the 2020 All-NBA teams with a deep neural network

Last year, we created machine learning models that nailed the All-NBA teams. This year, we’re using even better methods to predict the All-NBA teams.

Posted in Classification, NBA Awards. Leave a comment
January 17, 2020January 17, 2020

Predicting the 2020 MVP with linear models

Last season, we correctly predicted the MVP with four machine learning models. This year, we’ll take a better approach to predict the 2020 MVP.

Posted in Classification, NBA Awards. Leave a comment
January 3, 2020January 3, 2020

Using machine learning to find the best and worst value contracts

Let’s evaluate contracts in the NBA by applying machine learning to compare how much a player earns versus how much the models expect them to earn.

Posted in Regression. Leave a comment
December 20, 2019December 20, 2019

Introducing true win shares: estimating team win probability given player stats

In evaluating players, teams look for who will give them the highest chance to win. Let’s create a metric based on the win probability given a player’s stat line.

Posted in Classification. Leave a comment
December 9, 2019December 9, 2019

Determining the 2010s NBA All-Decade team with machine learning

Following the end of the 2018-19 NBA season, many released their All-Decade teams for the 2010s. Different authors weight longevity and peak differently, creating some subjectivity. Let’s use machine learning to approach this objectively.

Posted in Classification, NBA Awards. Leave a comment
November 25, 2019November 25, 2019

Introducing DAVIS: a holistic and transparent defensive stat

Publicly available defensive metrics either fail to quantify defense or are hard to understand. Let’s create a better, more transparent public defensive metric, DAVIS.

Posted in Defense. 1 Comment
November 11, 2019November 11, 2019

Introducing the unicorn index: defining player uniqueness

Each year, more and more “unicorns” enter the league. Let’s define the uniqueness of these unicorns in a comprehensive metric.

Posted in Similarity. Leave a comment
July 26, 2019July 26, 2019

Using machine learning to predict All-Stars from the 2019 draft

Let’s use college stats to predict All-Star probabilities for the top-10 picks in the 2019 draft.

Posted in The Draft. Leave a comment

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