# How NBA Draft Lottery Probabilities Are Constructed

On September 28th, the NBA Board of Governors approved changes to the NBA draft lottery system. These changes were construed in an attempt to help avoid tanking in the league in an effort to maximize a respective team’s probability of obtaining a high draft pick. In part, this is not a bad effort as we…

# Using Random Forests to Forecast NBA Careers

Consider, for a moment, being a General Manager for an NBA team that is faced with determining the number of years for a player contract. The problem seems simple: a team requires a certain skill set that a player possesses and they would like to know for how long a player would be able to…

# Building a Simple Spatial Analytic: Passing Lane Coverage

In a recent blog post on defending the Hammer Offense, I showed that the quantification of distance to passing lane helps identify the coverage a defender has on an opposing player.In that very post, I showed only a graphic and did not give insight into how to compute this quantity. Today, we will walk through…

# 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…

# Deep Dive on Regularized Adjusted Plus Minus II: Basic Application to 2017 NBA Data with R

In our previous post, we introduced the theory associated with Regularized Adjusted Plus Minus (RAPM) through an illustrative example. In this post, we walk through a vanilla-flavored methodology for building a RAPM model for NBA data. In this article, we focus on the data necessary, the required data manipulation process, and methodology for determining required…

# Deep Dive on Regularized Adjusted Plus-Minus I: Introductory Example

Let’s start with a simple exercise. Suppose we have a three-on-three game, where there are five players on each team. If the game results in Team A defeating Team B by a score of 54 – 53; how can we determine each player’s contribution? We will identify the players as A1, A2, A3, A4, and…

# Hammer Offense: Mechanics and Quantification

If there was ever an evolution of basketball that led to present day NBA, it would be the transformation of the misdirection offense starting in the early 2000’s under Spurs’ head coach Greg Popovich. Leading the charge in reducing the number of low percentage shots, spreading the court through a series of flare screens, drags,…

# BLUE Defense: Introduction and Analysis

One of the simplest offensive plays in basketball is the pick and roll. The philosophy is relatively straightforward, a ball handler waits to receive a screen from a teammate and reacts accordingly. If the teammate establishes position, gets low and wide, and makes proper contact with the ball handler’s defender, then the defense is forced…

# Introduction to Oliver’s Four Factors

In 2004, Dean Oliver expanded upon his “Four Factors” philosophy from his 2002 book, Basketball on Paper, in an attempt to identify how four important strategies relate to success in basketball. These strategies are nothing new, as these were drilled into my head from coaches dating back to the early 90’s. The novelty of understanding…

# Understanding FG% and Rebounding in Player Efficiency Ratings

In our previous post we tackled, at length, how John Hollinger’s Player Efficiency Rating (PER) is calculated and identified how different aspects of a player’s game is weighted. This leads us to question certain components of PER. Should I prefer a scorer or a rebounder? How do these terms interact? What about team affiliation? In…