The GOAT Ladder: What the Ladder Looks Like

Putting It All Together

How do we put it all together to arrive at a valid answer? There are different levels, and different data sources; how should we stitch them together? What I’ve done is to view a player’s credentials through eleven “windows” side by side, with players receiving a “grade” for each window. Here are the grades on every player’s Report Card:

Continue reading “The GOAT Ladder: What the Ladder Looks Like”
 

The GOAT Ladder: Greatness is in the Eye of the Beholder

Putting Offense and Defense Together

With D_Score and two forms of O_Score in hand, we can combine offense and defense into a “one number” statistic. I have labeled the two variants of this stat differently to help the reader easily apprehend the difference: Total_Score per Possession adds O_Score per Possession to D_Score, while Total_Score adds O_Score by Volume to D_Score. So if you see “Total_Score” without any qualification, I am citing a player’s volume on both ends. When I cite “Total_Score per Possession”, I am referring to a player’s production rate.

Continue reading “The GOAT Ladder: Greatness is in the Eye of the Beholder”
 

The GOAT Ladder: Defensive Greatness

Defense – The Final Frontier

The final frontier
Image created by Craig Wheeler (https://www.flickr.com/photos/28221085@N03/3379998439)

It has heretofore been impossible to discuss individual defensive performance with any type of common language. Unfortunately, that leaves us with no solid means to debate the defensive merits of the majority of players in NBA history. All previous attempts to analyze defensive value have relied on defensive rebounds, steals, and blocks. In their absence, some models have used rebounds and assists as proxies. Statistics built upon this foundation include Dean Oliver’s DRating, bball-reference’s Defensive Win Shares, and Myers’ D-BPM. Some all-in-one metrics have also used team Defensive Rating as a stand-in for a player’s defensive value. DRating, Wins Produced, and logically even Plus-Minus models (in so far as points allowed is half of a team’s point margin) all use team defensive strength in this way.

The result is a decided favoritism toward big men (who gather rebounds) and players who are on good defensive teams. Many have noticed the result, but have not been able to redress the deficit without the use of superior data. Analyzing defense in this way also substantially misrepresents the merits of perimeter defenders. Such methods reward players with a lot of steals (even if they are poor defenders who gamble a lot) and punish players who don’t get many steals (even if they spend a lot of their time guarding the opponents’ best player.

Continue reading “The GOAT Ladder: Defensive Greatness”
 

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.

Continue reading “The GOAT Ladder: Offensive Greatness”
 

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.

Continue reading “The GOAT Ladder: Intro”
 

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?

Continue reading “The Best Defenders in the NBA this Season”
 

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

Featured

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.

Continue reading “Identifying the Best Defenders in the NBA Using Matchup-Based Defense”
 

Beyond the Box Score

It is time to stop evaluating players based on statistical feats. The ubiquity of statistical data has brought with it a ready supply of historical comparisons. In today’s NBA, one can hardly turn around without running into a stat trumpeting a player as “the first player since MJ to have 18 consecutive games of 20-5-5”. Who can ignore the box score aggregators, with their perpetual lists of “top performances of the night” that are oriented mainly around points, rebounds, and assists? The basketball community is awash in counting stats-based evaluations, but it’s time to cut it out.

The comeuppance has been a long time coming, to be perfectly honest. Fans have long prized points, rebounds, and assists as basketball’s version of the “Triple Crown” – a trio of statistics that can combine to depict a player’s value. Such an outcome is to be expected, since those three statistics are usually the largest numbers in any box score; our eyes are drawn to big numbers. In The Book of Basketball, Bill Simmons concretized the method of measuring players by Points+Rebounds+Assists in constructing his all-time rankings, which certainly did nothing to cool the ardor of counting stat acolytes.

It Was Never a Good Idea

Adding together counting stats was a bad idea to begin with. Taking values that describe discrete events and adding them without modification is among the poorest methods of measuring a player’s impact. Every box score statistic (every statistic period, as a matter of fact) exists on a certain scale. While it is quite common for a player to score 10 points in a game, and somewhat common for a player to grab 10 rebounds in a game, it is decidedly uncommon for a player to register 10 steals in a game. The scale – or range of normal values – for steals is much smaller than the scale for points. Since points, rebounds, and assists each have their own scale, adding them is always going to privilege points above the other components.

Continue reading “Beyond the Box Score”
 

Can You Give Me Some Stats to Prove that Russell Westbrook Steals Rebounds?

          Properly valuing and evaluating rebounds has become something of a hot topic in recent years with Russell Westbrook averaging a triple double for three consecutive seasons despite being one of the smallest players in the history of the league to average 10 rebounds per game. Much of the discussion about his incredible feat has centered on whether or not it is the case that Westbrook’s rebounding numbers are inflated due to Westbrook taking a disproportionate amount of defensive rebounds which could be collected by other members of his team.

          Naturally, a lot of people are looking for stats to support the conclusion which they’ve already reached (thus the tongue-in-cheek title). I think we can analyze this question, within the context of a useful estimation of rebounding value for the entire league.

Continue reading “Can You Give Me Some Stats to Prove that Russell Westbrook Steals Rebounds?”
 

Who is Really the Best Defensive Center in the League?

As with the previous posts in this series which detailed the best defenders at each position, I start with the question “Who are the best defensive centers in the league?” In order to address the question properly when evaluating centers, however, it is necessary to answer a prior question. What is a center’s defensive role in the modern NBA? During some prior eras, the center could remain near the basket either defending a fellow behemoth on the low block or walling off the path of opposing drives. Rule changes constricted the scope of zone defense. Then, the 3-point shooting revolution caught even big men in its tantalizing web. For the first time in basketball history, forces conspired to pull centers away from the basket for good.

Continue reading “Who is Really the Best Defensive Center in the League?”