Evaluating Assists with Python: Community Detection and the Brooklyn Nets

A common question about identifying player tendencies on offense is to ask “how likely is this player to receive the ball during a possession?” This methodology can be aided by the quantity touches. However, a player can touch the ball with what I like to term as an empty touch. These are touches that have…

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Deep Dive with Python: Offensive Ratings

The calculation for Offensive Rating, another fruitful Dean Oliver metric, is simple: compute the number of points produced when a player is in the game per 100 possessions that the player is in the game. The computation is performed at a “per possession” rate and scaled out to 100. The challenge lies at being restricted…

Measuring Attack Vectors of Ball-Handlers

As a point guard growing up, I found that driving with my dominant shooting hand would typically put my shooting hand away from the basket. And being undersized at the position (5’4″, 95 pound Sophomore) made life more difficult to shoot off the dribble. Instead, I developed my non-dominant hand, which gave me two options…

Game Score: Focus on Scoring

While I’m on a flight between Albuquerque to Oakland, let’s take a quick glance at another advanced analytic: Game Score. Game score is a metric that was developed by John Hollinger (one of the Godfathers of basketball analytics) to quickly give a rough estimate of a player’s contribution to a game. If a player scores…