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…

Breaking Down Player Efficiency Rating

Warning: Lots of Math Ahead… With the introduction of Player Efficiency Rating (PER), John Hollinger constructed a methodology for comparing the relative accomplishments of players across leagues, as well as across years. While being commonly viewed as complex and unidentifiable, the idea is relatively simple: produce a value for each player such that it captures their personal…

Markov Simulation: NBA Playoffs Round 2

In continuation of our Markov simulation of the NBA Playoffs, we take a look at the updated probabilities for each team remaining. In this article, we take a cursory look at each second round match up and see how the first round panned out compared to the probabilistic predictions. So Far So Good: All rounds…

Markov Simulation: NBA Playoffs

With the NBA Playoffs set to get underway, we take a quick look at the probabilities for each team becoming the NBA Champions. Common consensus would place any combination of the five strongest teams: Golden State – Houston – San Antonio versus Cleveland – Boston. But the question is how likely? To answer that, we…