The Heliocentric Metric
the 9.81 number✨
Thank You, Newton
I first heard this term ‘gravity,’ and my mind went where any normal person would.
Physics.
Falling objects, free fall. Gravity pulls just as hard at the start as it does at the end, yet early on, you barely notice it. Only moments before the ground, before impact—when speed has compounded—does the force feel overwhelming. It was always there; the effects just took time to register.
The scientific concept eerily coincides with basketball. For instance, when Wemby or Curry steps on the floor, you pay attention...and so do defenses. And these players don’t immediately manipulate defensive sets; rather, possession by possession, they distort the floor. Help defenders guard a bit closer. Rotations arrive a half-step earlier. Passing lanes widen. And by the time the ball moves, the floor no longer looks like it’s supposed to.
Earlier this month (late to the party), the NBA formalized this idea with a new metric called Gravity. Introduced in an official league release, gravity measures the amount of defensive pressure an offensive player draws (on or off-ball) relative to expected floor spacing. Using cutting-edge tech such as optical tracking data and machine-learning models, the league compares where defenders should be to where they actually do go. The difference is scored frame by frame on a normalized scale, turning defensive attention into a measurable signal on a -100 to 100 scale.
Let’s look at some light exploratory analysis of this concept: how gravity shows up, how it differs from scoring efficiency, and why some players reshape the game long before they ever take a shot.
Lightyears Away
Gravity scales with distance.
As a player’s average field goal attempt distance increases, so does the defensive attention they tend to command.
And this relationship tracks. Perimeter spacing taxes defenses, stretches rotations, and, ultimately, increases the cost of mistakes. The line of best fit affirms that distance matters.
Let’s observe the residuals, or the players who sit meaningfully above or below that expected relationship, the trendline. These deviations tell us a bit more than the slope itself.
Players above the trendline are garnering more defensive attention than their shot diet alone would predict. SGA has the most significant positive residual. Meaning, although Shai typically attempts field goals from 13 feet out, defenses guard him as if that shot were worth twice its value. And remember, the midrange is a pretty low-ROI shot, yet teams still overcommit to defending his middies; however, that speaks more to defenders responding to how difficult he is to stop than to where he shoots.
On the flip side, we have Nicolas Batum. Batum lives beyond the arc, but despite shooting above league average, he draws relatively little gravity. His low comparative defensive attention is not a statement about perimeter threat, but about leverage, as shooting ability alone does not guarantee defensive distortion. Gravity requires the defense to care in real time, not merely respect a percentage on a scouting report.
I varied the scatter points by size using field goal attempts per game (just because it showed more clearly than, say, USG% or TS%) to add an extra dimension. The volume exposes how players exploit their opportunities relative to the attention they receive.
Westbrook stands out because he’s a high-volume shooter (top 20% in the league) who draws relatively little defensive pressure compared to his average attempt distance; however, his efficiency is below average. Now, whether defenses sag due to inefficiency or to defensive neglect, I’ve yet to disentangle. But there is a signal there.
By contrast, Baylor Scheierman sits on the other extreme: low volume, minimal gravity, but strong (86th percentile) scoring efficiency. This suggests a role defined more by selectivity than by leverage.
Another thing worth noting is that players with above-average gravity (>0.608) also see higher usage (≈22% vs. ≈19%). Or about 2 more possessions per game. That relationship is not causal but indicative of a feedback loop: players who bend defenses are given the ball more often, and offensive roles boost that effect. So gravity is not just off-ball influence, but a property offenses are structured around.
Inner vs. Outer Planets
So far, we’ve only observed gravity as a single aggregate metric. Luckily, the NBA’s tracking data allows us to split gravity into interior and perimeter, as well as on-ball and off-ball. When observing through this lens, many positional trends arise.
Centers dominate rim attempts, guards dominate perimeter gravity, and most players cluster predictably around their positional norms.
Kevin Durant is in a world of his own. 🌎
When benchmarked against centers, and any position or player for that matter, KD’s off-ball interior gravity registers as a +5.9 standard deviation outlier. However, I must point out that the average center in the dataset sits well below zero. So, although one would think that centers would monopolize interior gravity, surprisingly, they command quite the opposite. Regardless, KD is not just a perimeter scorer matching paint-beast-type of interior gravity. We’re seeing a 3-level scorer generating interior defensive attention at a level that centers themselves, or anyone for that matter, do not approach.
On the perimeter side, the trends are more straightforward. Guards overwhelmingly drive perimeter gravity, centers lag, and Stephen Curry, like KD in the paint, also exists in a world of his own. Steph’s off-ball perimeter gravity is head and shoulders above the rest. His gravity value reflects his deadly shooting, but it’s also a testament to the defensive overcorrection his presence generates. Shai and KD appear once more as outliers, controlling disproportionate perimeter attention relative to their three-point attempt rates.
As mentioned earlier, when discussing Shai’s general gravity and KD’s interior off-ball gravity, defenders respond less to volume and more to shot consequence.
Stars & Satellites
Let’s look at perimeter on-ball gravity versus the share of made threes that are assisted. This can be a signal for agency. Or rather, gravity can show us players through a lens of either self-generated or system-dependent perimeter scoring. Who creates their own looks versus who depends on getting set up.
For high gravity and low assisted 3P rate (bottom right), we’re looking at true self‑creators. These are stars whose perimeter gravity is born on the ball, not from teammates spoon‑feeding them. Their scoring pressure draws help before the pass is even an option. Shai, Luka, Kawhi, and Steph live in this part of the solar system. Big bubbles here signal that this self‑generated gravity directly shows up in offensive value.
The top‑right quadrant is a different archetype: high on‑ball gravity but heavily assisted threes. These guys thrive in structures that bend the defense first, then let them attack after the fact. Second‑Order Gravity. Lauri Markkanen, Chet, and KAT sit here. Their gravity is absolutely real, but it often activates when someone else tilts the floor.
Top‑left is classic system shooter territory: low on‑ball gravity, highly assisted threes. Catch‑Only Threats. They rarely force a reaction themselves but punish rotations on the back end. Think stretchy, play‑finishing bigs like Ke’lel Ware, Donovan Clingan, or Bam Adebayo. Their gravity is situational, not initiating.
Even within that top half (above-average assisted-3P), there’s a distinction. While they may all seem like stretch bigs, the second‑order group (KAT, etc.) does consist of floor‑stretching 4/5s but with real perimeter volume and some on‑ball usage. The catch‑only cohort is more emerging or role‑level stretch bigs whose spacing is still complementary rather than central.
Bottom‑left is the dead zone: low on‑ball gravity & low assisted rates. So they’re neither warping defenses nor benefitting from a system. The smaller bubbles here underline that:
limited gravity + modest efficiency = muted impact.
You get folks like Russ, Josh Hart, and Ajay Mitchell at the low‑OBPM end, with reliable contributors like Jimmy Butler, Keyonte George, and Collin Gillespie on the high end.
Ultimately, the chart reframes gravity as a spectrum of how advantage is generated. High gravity by itself isn’t the punchline; what matters is whether that attention is created, triggered, or borrowed.
And as we can see, the league’s most valuable offensive engines are the ones who don’t need the defense to bend first.
Pressure Compared To Production
This chart lines up players by how much defensive attention they draw relative to how well they convert their shots.
Steph, Harden, and SGA sit in the ‘nuclear threat’ zone. Their defensive attention far outpaces even elite efficiency, which tracks with how much help they pull even on off nights. Now, on the other end, players like Gobert or Ayton post exceptional scoring efficiency but low Gravity. The disparity here is likely indicative that defenses are opting to guard them more straight‑up. In between are some aforementioned stretch bigs, Alpy, KAT, and Chet, and creators whose dots nearly overlap. Defensive respect and scoring returns are more in balance. Also worth noting is Julius Randle, the median point, whose Gravity and scoring efficiency are just average.
A Force We Can Now See
As with anything eye-test-y, Gravity has pretty much backed up what seems to be a proxy for “attention”. But now we’ve unpacked the different flavors of threat.
We learned that a positive relationship exists between average shot distance and defensive pressure, where a steady 3‑point diet explains a lot of who bends the floor.
However, that’s not to dismiss midrange beasts and paint magnets that can still pop as primary gravity sources even without living behind the line. Self‑creation vs assisted scoring allowed us to split offensive threats into different categories:
Primary Engines
Pressure Amplifiers
Knockdown Shooters
Low-Leverage Scorers
In essence, we found that some players create Gravity, while others inherit it. Ultimately, the last visual made clear that Gravity is independent of scoring efficiency. Some stars with high‑degree‑of‑difficulty shot diets draw far more attention than their TS% alone would suggest. Even so, efficient finishers can quietly rack up points without ever becoming the center of the scouting report.
So this Gravity metric has been out for around a month now, and a lot of folks on NBA Twitter have been raving about it. I think it’s super cool to take a metric that’s lived mainly in the eye-test realm and been brought to life! The use of FAANG-like tech in the league, like trajectory data & AI/ML, has turned an abstract concept into something rich.
But with all tracking-intensive endeavors, the limitations are stark. Presumptively, players have a right to opt out, which makes sense given that players like Jokic and Giannis weren’t available in this dataset. Also, being new, we can only observe this during the in-session season—so it’ll be informative to see in years to come and finally be able to track trends in defensive warping.