# Approximating Curves I: Mechanical Process

Now that the 2019-2020 season has ended, let’s take a quick look at something almost every data scientist knows: polynomial projection. Now, if you’re a data scientist and find yourself mumbling, “I’ve never heard of that,” don’t worry: You have. Over the next few posts, we are going to discuss a larger problem of approximating…

# Usage and Efficiency

The usage of an NBA player consists of the number of chances a player takes out of the possible chances a team has when that player is on the court. A chance being the number of possessions that can result in a scoring possession. The higher the usage for a particular player, the more likely…

# Bradley-Terry Rankings: Introduction to Logistic Regression

In a recent post, we identified the Colley Matrix methodology for ranking NBA teams. The methodology provided insight but abused the originating statistical construct in an effort to enforce a correlated, solvable, set of equations to identify a “probability” of winning. Unfortunately, we witnessed that not only were those statistical assumptions violated, but the resulting…

# 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…

# 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…

# Basics in Negative Binomial Regression: Predicting Three Point Field Goal Percentages

With the highest return for a field goal attempt per possession being a three point field goal, teams have caught on that three point field goals are the optimal way to go on offense provided they have consistently accurate shooters from that range. This qualifier has been the difficult part of the three point equation.…