Drip

The GOAT Ladder: Offensive Greatness

 

Analyzing Offensive Value in Context

Evaluating a player’s offensive output relative to his background has been one of the areas in which statistical analysis of the NBA has enjoyed the most success, with analysts since the 1980’s having endeavored to calculate the offensive value of players from various eras. To this day, the term “analytics” is synonymous with shooting efficiency in some people’s minds. Previous models have calculated players’ offensive volume (Oliver’s Points Produced, O-BPM, and the lions’ share of bygone metrics PER and WORP), offensive efficiency (Oliver’s Floor Rating, the offensive section of Wins Produced), offensive production rate (The NBA’s ORtg, OWS/48), and offensive impact (O-PIPM and the offensive components of RPM and RAPM). I have thoroughly studied individual player volume, efficiency, and production rate in The Basketball Bible. My study focused solely on the 2015-16 through 2018-19 seasons (the seasons for which full tracking data was available at the time).

Offensive overview of the 2019 Toronto Raptors from The Basketball Bible

There been no significant efforts to ascertain offensive impact for players prior to the play-by-play era (1996-97 through the present). The only metrics that have claimed to measure offensive impact are plus-minus models. Such models rely on changes in point margin while a player is on the floor to infer offensive impact. (sometimes the change in point margin is augmented by the player’s box score statistics and/or prior plus-minus).

It is possible, and indeed valuable, to measure a player’s offensive impact by comparing his performance against league average in a given season. We begin by determining each player’s volume (total points created), production rate (points created per possession), and efficiency (the ratio of points created to points created and lost). After calculating these values, we move to the comparison stage. Some easy-to-understand statistical methods can reveal powerful insights about a player’s relationship to his context using only these numbers. More to the point, I believe we can determine a player’s offensive greatness and how it affects his historical standing.

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The GOAT Ladder: Intro

 

No matter how strong the pull of the moment is on our attention, the history of the game calls to us. The urge to crown a king and place him in succession to the kings of the past is an echo of the call. Ranking the top 10 or top 20 or top 50 or top 100 players of all time is another echo. The call rings out clearest of all, however, in the eternally debated question “Who is the greatest player of all time?” The GOAT debate is nothing more and nothing less than our predisposition to measure the immediacy of current players against the historical stature of legends from the past.

No matter how one describes greatness, or measures it, the desire is the same. We all want to know who is and was the absolute best. Ring-counters want to know the truth of the matter. So do points-praisers, eye-testers, and analytics afficionados. Coaches want to know who is the best, and so do fans. Everyone involved in the game has the same need to compare, to evaluate players in relation to one another. It’s a competition, after all! If we want to understand what makes teams win, it is crucial to be able to determine which players have a stronger or weaker influence on winning. The goal is not to field an objectively talented team. The goal is to field a team which can defeat opponents as frequently as possible.

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

 

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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