Robert Covington’s Seamless Adaptation to the Houston Rockets’ Defensive Scheme

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.

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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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Why Don’t We Care About NBA-All Star Weekend?

There is something fundamentally wrong with the NBA All-Star game and All-Star Weekend at large. The new NBA Jam-like rule change, while interesting, does not fix what plagues the NBA’s premier regular season event.

So What Is the Problem?

Give the NBA credit, they have shoehorned the All-Star event into the perfect time period. Those feeling hungover from the Super Bowl have something to watch, and those who are waiting for prime time NCAA basketball to come during March have an event to hold themselves together in the meantime. If you are an NBA fan, chances are you were already going to watch the All-Star activities. By picking mid-February, the league has found a period in the sports calendar where the NBA has center stage.

In spite of the logistical victory, fans do not care and players do not care. The NBA has seen a sharp decline in viewership numbers for the All-Star game from the ’90s leading into the 2000s, and then almost no growth since 2005 (Please note the Lockout of 1999 had the All-Star Game cancelled, all data from https://en.wikipedia.org/wiki/National_Basketball_Association_on_television). Viewership is not the best metric for fan engagement, but it does give a decent look at where fans stand with respect to watching the actual game (admittedly, it does not measure interest in the other festivities during All-Star Weekend). I also examined the voter numbers to assess engagement, but worried that the results might be unreliable. Due the constant changes in how and where fans could vote, any correlations might not be instructive.

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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.

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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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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!