Applied Efficiency: A New NBA Scoring Efficiency Stat

by Baltej Parmar

Comparing players is the stock in trade of fandom. Whatever your team, everyone wants to know which player is better than his peers. Statistics allow us to compare various aspects of player performance – perhaps most notably efficiency. “Russell Westbrook has a career FG% of .434, while Stephen Curry has a FG% of .476. The difference isn’t much.” Are we sure that a difference of .042 is not much, though? The scale of statistics can make it difficult to use them responsibly in comparisons. In the example above, Curry makes 42 shots more than Westbrook out of 1,000. Since a player doesn’t take 1,000 shots in a game, it is not directly obvious how much more likely Curry’s team is to win a game based on the difference in FG%. What if we had a better way to measure how much a player’s shooting impacts the score on a game-by-game basis? That’s why I’m introducing Applied Efficiency, a new NBA stat which does just that.

Continue reading “Applied Efficiency: A New NBA Scoring Efficiency Stat”
 

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.

Continue reading “A Prediction Model for Sports Match Results – Part 2”
 

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.

Continue reading “A Prediction Model for Sports Match Results”
 

Recent Writing

Hey everybody! I haven’t posted anything here in the last couple of weeks, because I’ve been busy writing things on other sites. I will provide the links and brief summaries of those articles below.

At Off the Glass, I wrote about how to resolve the tensions under the current CBA in which players feel wronged because they are trapped on a team that they don’t get to choose and teams feel wronged because players are forcing trades one and two years before the end of their contracts.

The Solution for Trade Demands

I also wrote a piece for Off the Glass on Caris LeVert, who had a breakout season disrupted by injury last season. I break down his performance, and offer some speculation on what he might do this year.

Hot Take Marathon: Caris LeVert Will Be an All-Star

At Bellyup Sports, I published a data dive on the Orlando Magic’s Jonathan Isaac:

Is Jonathan Isaac the Future for Orlando?

Also at Bellyup Sports, I wrote a short piece on the top five most effective passers in the NBA last year.

Who Are the NBA’s Top 5 Passers?

I will be posting an article here next week, but I will continue to provide links to my work on other sites as well. Thanks for reading!