Visualizing NBA Analytics through the Past Decade’s Superstars


It’s no secret that the NBA has been undergoing an analytics revolution for several years, and it’s not just Daryl Morey’s Houston front office, though they might have the most organizational buy-in from top to bottom. I can count only three teams (all three of which are in the Pacific Division) that don’t employ or contract with at least one “stats guy” or “analytics guy,” whatever his official job title may be. With SportVU rightfully attracting massive amounts of attention after the NBA agreed to supply the remaining camera-less teams before this season, front offices will continue to discover new insights on player evaluation (especially defense), in-game strategy, salary cap management, draft prospect prediction, player development, coaching evaluation and player synergies.

So let’s get ourselves caught up with some of the basketball research that has been done publicly through visualizing superstar players’ careers.

I used the xRAPM (Expected Regularized Adjusted Plus/Minus) ratings from Jeremias Engelmann’s to take a look at some of the career trends for NBA players through superstars. It uses play-by-play data, box score data, a height adjustment, and some advanced statistical techniques to evaluate all parts of a player’s game, from simpler things like scoring and rebounding to the more complicated like spacing, pick setting, and defensive rotations. It then puts a numerical value on how the player affects a team’s points per 100 possessions and points allowed per 100 possessions, relative to the league average. For more detail on plus/minus metrics see Daniel M’s explanatory post.

xRAPM is by no means perfect, particularly for small sample sizes, and does not account for offensive and defensive scheme. It features heavy regression to the mean, integrating multiple seasons of data, and assigning prior values that are negative for rookies, more offensively-based for perimeter players and more defensively based for big men.

Even so, it is essentially the “gold standard” NBA catchall advanced metric out of those that are publicly available, usually agreeing with the educated NBA viewer about which players help their team win and which players do not. Now, let’s get to some of the trends in the data.

Defense is important (half the game!), and big men are essential to a strong defense.


Carmelo Anthony is a great NBA player, but his offensive gifts say nothing about how he plays on the other end of the court. Whether it’s due to a lack of defensive skill or effort, Melo has been a consistently below average defender for his entire career, something that separates him from the real superstars of the past decade.


On the other hand, as should be pretty obvious to anyone who watches the NBA, Dwight Howard has been a terrific defender his entire career and is far and away the main reason why Orlando was a contender in the East for several years. Howard helps his team mainly from being able to turn a bad defense into a good one while also being the best offensive center in the league since Shaquille O’Neal. In fact, his injury, combined with getting familiar with a new team, coach, and role last year with the Lakers, resulted in a league average at best contribution on offense, which Laker fans would agree with despite Dwight’s 17 points per game on a 57.3% True Shooting Percentage.

Offense peaks earlier than defense since part of defensive skill comes from effort and experience with smart defensive rotations.



As Mike Zarren, Assistant GM of the Celtics used to say, I don’t care if Duncan is better than Garnett or Garnett is better than Duncan. But both the Celtics (before this summer) and the Spurs care about evaluating the two great defenders and figuring out optimal rotations in a Doc Rivers-led or Gregg Popovich-led defensive scheme. Great defensive big men are very important to a good defense and aging produces offensive decline at a much faster rate than defensive decline, at least for big men. This coincides with what I heard from a source in a front office: smart basketball people tend to think offense is mostly skill while defense is mostly effort. While age erodes athleticism and skill over time, effort seems to be consistent from the middle of a player’s career to later on.

It takes time to adjust to new teammates, a new head coach, and a new role. And because only one player can handle the ball at once, there are diminishing returns for having multiple scorers.



The Big Three provided a great quasi-experiment about diminishing returns for multiple offensive weapons that all need the ball in their hands to be most effective. They did not run the league, as many people had expected. In fact, the Heat teams of the past three years have been just about as good as LeBron’s 08-09 and 09-10 Cleveland teams. LeBron, Wade, and Bosh’s value is reduced when on the same team because they are all most effective offensively with the ball in their hands, but there is only one ball on the court.

We can see similar bumps up or down in offensive contribution for other primary scorers losing or gaining another scorer on their team. Kobe’s offensive contribution to the Lakers was maximized when he didn’t play with Shaq or Pau. KG’s was maximized when he played alongside nobody offensively effective with the Timberwolves. It is a difficult task for an NBA GM to continue to improve a team’s offensive efficiency past a certain level because primary scorers become less and less valuable as they gain more scoring help around them. That’s not to say that a great distributor or floor-spacer can’t increase a great scorer’s offensive contribution, but rather that there is only one ball to go around, and it is necessary to adjust players’ offensive value for the scoring ability of their teammates.

The graphs above also illustrate that it takes time to get familiar with a new coach and new teammates. While LeBron and Wade were much less effective right after joining forces, LeBron became more effective after getting familiar with coach Erik Spoelstra and his new teammates. We would expect Wade to decline from 10-11 to 11-12 because of age and injuries, but he stayed just as effective as those negative trends were canceled out by his increased familiarity and responsiveness to his teammates on the floor.

Injuries have a notable, lasting impact that is much greater than people think.


Unfortunately, major injuries have a lasting effect on players. While many members of the media are all set to proclaim Derrick Rose as MVP again, we should look back at Chris Paul’s knee injury, Dwyane Wade’s knee injuries, and the other major injuries of the past decade. As the graphs illustrate, an injury not only reduces a player’s effectiveness in that particular season, but it also usually has an adverse effect for at least the next season as well.

Don’t plan on the Riggin’ for Wiggins winner to contend from day one. Even the best rookies aren’t All-Stars.

This should be pretty obvious to most people, yet I have a sneaking suspicion that if Andrew Wiggins gets drafted number one next year he’ll have an ESPN #NBARank in the 30s, despite no rookie in the past decade being definitively All-Star caliber. LeBron didn’t even make the All-Star team as a rookie, and Blake Griffin’s case was helped by quite a few highlight dunks. Usually the best young players make a major leap from year one to year two or from year two to year three after showing flashes of promise but not necessarily being a great player in their first or second year in the league.


The most striking example for this phenomenon is probably Kevin Durant, who showed both massive offensive potential scoring the ball and defensive potential with his size during his rookie year. However, he was not actually a significantly above average player (check out his rookie year efficiency and defense). Of course, then came the huge leap in year three as he adjusted to NBA teammates and competition, taking a 23-win 08-09 Thunder team to 50 wins in 09-10.

I haven’t proven anything on my own by showing these graphs of a few players; rather, the people at the APBRmetrics forum (some of whom now work for NBA teams) have proven these concepts before me. But I hope I’ve helped communicate their conclusions to you by connecting their research to visualizations and familiar storylines so that you can see that analytics and a knowledge of the game can be combined to make us better informed about basketball, whether we consume it as a fan or have dreams of becoming the next Daryl Morey.

Data courtesy of

Nik Oza
Georgetown University Class of 2016

Follow Nik on Twitter: @NikOza2
Follow GSABR on Twitter: @GtownSports
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