Introduction

Welcome to the fourth post in this series! In the first one, I introduced my plan for conducting methodological reviews of basketball statistical models. In the second, I reviewed the Wins Produced model. Then, in the last post, I took a look at Win Shares. Today, I will analyze the Wins Above Replacement Player (WARP) model developed by Kevin Pelton.

The Basics of WARP

As Pelton outlines at the beginning of his write-up on WARP, he based much of his work on Dr. Dean Oliver’s. Since I already wrote about Dr. Oliver’s model, I will try not to delve too much into the parts of his writing used by Pelton. Doing so would make this series too repetitive to be ideal as a review.

My opinion is that there are two pivotal frameworks in use by Pelton: Dr. Oliver’s work is the first, while the second is related to his use of the concept of a “replacement player”. Having previously described Oliver’s impact on modern basketball analytics, let’s focus on the effect of the latter framework.

Continue reading “Methodological Reviews of Basketball Models – Part 4 – WARP” →

Introduction

In the first post of this series, I introduced my plan for conducting methodological reviews of major basketball statistical models. In the second installment, I looked at the Wins Produced model. In keeping with the theme of “Wins-named” metrics, we turn to Win Shares.

Win Shares is a basketball adaptation of Bill James’ work with baseball statistics spanning decades. Justin Kubatko adapted James’ methodology to basketball. Kubatko’s explanation shows how to calculate the number of Win Shares for a player, so my review will rely on his exposition.

Continue reading “Methodological Reviews of Basketball Models – Part 3” →

By Alan Moghaddam

I’ve been obsessed with Machine Learning lately. Like really obsessed. The problem is that I do not care about predicting how many passengers would have survived the Titanic sinking, or if a computer can tell whether an image is a dog or cat (all cats or you need not apply), or performing handwriting analysis to see if someone drew a 0 or a 6. Fret no more, I’ve done it – I have made a machine learning algorithm that scrapes data from basketballreference.com and can predict who will and will not make the playoffs!

Continue reading “Hey Siri, will the Rockets make the playoffs this year?” →

by Andre Vizzoni

Introduction

As outlined in the first post of this series, I will be reviewing several models, beginning with Wins Produced. The creators of Wins Produced are David Berri, Martin Schmidt, and Stacey Brook, three sports economists. Their work on the subject of basketball analysis spans many academic papers, specially Berri’s work, and has also spawned two books and two blogs.

Continue reading “Methodological Reviews of Basketball Models – Part 2” →

After the Houston Rockets pulled off the deal for Robert Covington at this year’s trade deadline many wondered what the Rockets were doing. How would their team look, especially on the defensive end? In some respects, the Rockets performed as well after the trade as before, and one of the biggest factors in their defense has been the newest factor: Robert Covington.

Where did Covington come from? How did he land here, as a highly-prized trade target First, let’s go back to the beginning of Robert Covington’s career.

Continue reading “Robert Covington’s Seamless Adaptation to the Houston Rockets’ Defensive Scheme” →

Introduction

The first three parts of this series of articles were focused on a static model for predicting match results. With that model, our measures of team quality (offensive quality, defensive quality, and home field advantage) were static, meaning that they were not allowed to change as time progresses. In other words, a team was considered as having the same quality whether you were trying to predict the team’s results in week 1 or its results in the last week of the season.

Continue reading “A Prediction Model for Sports Match Results – Part 4” →