Methodological Reviews of Basketball Models – Part 2

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.

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Methodological Review of Basketball Models – Part 1

by André Vizzoni

Introduction

The series beginning here will be my first foray into topics beyond my model for predicting sports results. Specifically, I plan to undertake a methodological review of a few well-known basketball models which have been influential in the analytics community. The review will examine the “how” and “why” of those models, and may level criticism at parts of them. As there are a number of subjects that must be broached prior to the review proper, this introductory post will be shorter than its successors.

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A Prediction Model for Sports Match Results – Part 5

Introduction

I am quite happy to have arrived at the final post of the series – not because I disliked writing the previous posts, but because I have worked for four years with the models presented in this series. I have become accustomed to their weaknesses and strengths, as well as those of the methods used for comparison. This post, however, is new and fresh. Finally, I have the change to put on paper (sort of) all the ideas I have had over the years on how to improve the model.

I don’t think it will surprise anyone reading this to know that I love modeling data. One of the reasons I love it is that modeling is not an exact science, as one might expect it to be. Modeling is just as much an art as a science; there are no certainties when we work with Probability Theory. Dealing with probabilities means making your peace with the fact that you will always be uncertain about your conclusions.

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A Prediction Model for Sports Match Results – Part 4

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.

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A Prediction Model for Sports Match Results – Part 3

by André Vizzoni

Introduction

Parts one and two of this series of posts were quite heavy with probabilistic jargon (even with me trying to lighten it), and I hope this third one won’t be like that. The main objective of this article is to show the readers how the outputs of this model fit within the current basketball analytics framework. With that in mind, I will be looking at the relationship between the parameters of every team and the following stats:

  • Team 2-Point Field Goal Percentage (T2FG%)
  • Opponent 2-Point Field Goal Percentage (O2FG%)
  • Team 3-Point Field Goal Percentage (T3FG%)
  • Opponent 3-Point Field Goal Percentage (O3FG%)
  • Team Effective Field Goal Percentage (TEFG%)
  • Opponent Effective Field Goal Percentage (OEFG%)
  • Team True Shooting Percentage (TTS%)
  • Opponent True Shooting Percentage (OTS%)
  • Team Offensive Rebounding Percentage (TOREB%)
  • Opponent Offensive Rebounding Percentage (OOREB%)
  • Team Defensive Rebounding Percentage (TDREB%)
  • Opponent Defensive Rebounding Percentage (ODREB%)
  • Team Free Throw Rate (TFT%)
  • Opponent Free Throw Rate (OFT%)
  • Team Turnover Percentage (TTO%)
  • Opponent Turnover Percentage (OTO%)

Since I’m using this Justin Jacobs’ article as a guide, it also makes sense to look at the Four Factors Adjusted Ratings, hosted on this website. First, I will lay out the Methodology for my analysis. Next, I will analyze the scatter plots of the model parameters versus the assorted stats in the Analysis section. On the basis of those results, I will use the Discussion section to make sense of all that the analysis unearths.

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A Prediction Model for Sports Match Results – Part 2

Introduction

As outlined in the first post, this installment of the series will be about the model’s estimates and predictions. I will also touch on how to compare predictions between models. For all that to work, I will have to introduce a few mathematical definitions. In a similar way to the first post, I will give both an intuitive explanation of every concept and a mathematical explanation.


The Estimates section will begin with an explanation of how the model fits the data (with a few graphs to show the process of finding the estimates for last season’s NBA), followed by a brief introduction on Bayesian inference. Next, the Model Comparisons section will define the predictive likelihood of a model, and how to calculate it. I will also discuss a few different ways to compare predictions. Finally, in the Discussion section, I will offer critiques of the work presented in this post.

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A Prediction Model for Sports Match Results

by André Vizzoni

Introduction

In this post, I will introduce a prediction model that was the product of a research project that spanned four years (winning a few awards) and was the final project for my degree in Statistics. For those interested in the final project, here it is – though I warn everyone in advance that it is in Portuguese. The objective of this post, then, is to translate the most central parts of that project to English while, at the same time, talking about applications of the model to basketball data, since the original project used soccer data.

First, I will give an intuitive explanation of the model, with no equations or mathematical concepts introduced. Next will come the methodology section, where there will be a lot more maths and formal definitions. As such, people who are interested only on the intuitive definitions might wish to skip the methodology section). The idea behind this structuring of the post is for it to be understandable, both by laypeople and by people well versed in statistics. Finally, in the discussion section there will be a few summary comments, as well as a preview of things to come on this site.

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