Drip

Identifying the Best Defenders in the NBA Using Matchup-Based Defense

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Defense is half of the game of basketball, but it has been difficult to gather information about the defensive capabilities of all the players in the league until very recent seasons. Due to the paucity of data about defensive performance and the limitations imposed by television broadcasting contracts, it was practically impossible to truly know who the best defenders were, night in and night out.

Offense, by contrast, is well-documented. The box score provides a good deal of useful information on individual offensive output, and even more granular data has been available throughout the 21st century by virtue of play-by-play data.

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Matchup-Based Defense

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Defense is the unsolvable puzzle in NBA analytics. No matter how advanced the advanced stats get, defensive metrics continue to crash against the same conundrums. Better data often leads to better models, and recent years have seen a dramatic improvement in the quality of defensive data available for analysis. Tracking data, opponent shooting data, play-by-play data, and more have all played a hand in modern defensive analysis. In spite of the improvements, or perhaps in part because of the improvements, it is clear that defensive analysis is still not highly accurate.

Most defensive metrics which are currently extant are based on one of two schools of thought. In order to take stock of why defensive analysis is still frequently inaccurate, it will help to investigate the underlying assumptions behind most current models.

The Plus/Minus School of Thought

The most popular method by far is The Plus/Minus School, which counts BPM, RPM, RAPM, PIPM, and more among its adherents. The distinguishing precept of the Plus/Minus School is the belief that we can ascertain a player’s defensive value by evaluating the team’s performance with him on the court, if only we properly adjust for strength of opponent, the team’s talent level, the team’s performance with the player off the court, and the player’s performance level in seasons past. The adjustments made to raw plus/minus are attempts to extract reliable data by excising confounding variables.

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Methodological Reviews of Basketball Models – Part 3

 

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.

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Hey Siri, will the Rockets make the playoffs this year?

 

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!

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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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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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The Best Defenders in the NBA this Season

 

What Have You Done for Me Lately?

The NBA is a right now league. Each game can change in a moment. Every season’s trade deadline brings about substantial team restructuring, and the offseason free agency and trade market has become an event unto itself. Change happens quickly, and the team that wins the championship is often the team that becomes the best version of itself at just the right moment.

Many of the most important questions deal with which player or team is the best right now. “What have you done for me lately?” is the unspoken question on the minds of everyone in and around the league. In previous posts, I’ve used the defensive matchup data at stats.nba.com to create a model for defensive performance since 2013-14. The entire dataset is now features prominently on the homepage, and you can pull any player card for any season from the Google Sheets tool.

But what about this season?

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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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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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