There is a reason NBA fans call the past 15 years or so The Steph Curry Era. Steph and others changed the game through shot selection, spacing, and the value teams placed on the three-point shot. That impact is not going away.

The question now is whether the advantage, or premium, of the three-point shot has finally crossed the Rubicon. Teams take more threes than ever, defenses have adapted, and the league may have reached the point of diminishing returns. When you look at the numbers, it seems like volume has started to catch up to the advantage.

That does not mean the three-point premium is gone. It means the answer is more complicated than more threes equals better offense. This project uses cluster modeling to test that idea, starting with a simple model that groups teams by three-point attempt rate and true shooting percentage to see whether higher volume still connects to better scoring efficiency.

The second model adds free throw rate and offensive rating because offense is not only about where the shot comes from. Good threes create spacing, rim pressure creates fouls, and shot quality still matters. The goal is to see whether the three-point premium still shows up by itself, or whether it now depends more on the offense around it.

Why K-Means Clustering Fits This Question

K-means clustering is useful here because it lets the data sort teams by style instead of forcing an answer before the analysis starts. I am not trying to predict wins, crown the best offense, or say every good team has to play the same way. The goal is simpler than that: group teams that look similar offensively and then see what those groups tell us about the three-point premium.

That matters because the modern NBA is not just high-volume threes versus low-volume threes. Some teams take a ton of threes and score efficiently. Some take a ton and do not. Some teams create efficient offense through foul pressure, rim pressure, or balance instead of pure three-point volume. Before building the clusters, the first step is to lay out the team offensive profiles that feed the model.

2025-26 NBA Team Offensive Profile

2025-26 NBA Team Offensive Profile
Sorted by offensive rating rank.
ORtg Rk Team
Record
Team Strength
Shot Profile
W L Win% ORtg NRtg 3PAr TS% FTr
1 DEN 54 28 65.9% 122.6 5.2 40.8% 61.6% 29.4%
2 BOS 56 26 68.3% 120.8 8.1 46.7% 58.3% 20.7%
3 NYK 53 29 64.6% 119.8 6.5 42.8% 59.0% 23.8%
4 SAS 62 20 75.6% 119.6 8.3 42.2% 59.5% 27.4%
5 CHO 44 38 53.7% 119.4 5.0 48.7% 58.9% 24.4%
6 CLE 52 30 63.4% 119.2 4.1 44.2% 59.5% 26.5%
7 OKC 64 18 78.0% 118.9 11.2 42.6% 59.9% 26.1%
8 HOU 52 30 63.4% 118.6 5.4 35.0% 57.6% 26.0%
9 LAL 53 29 64.6% 118.2 1.8 39.4% 60.9% 32.0%
10 DET 60 22 73.2% 117.9 8.2 34.5% 58.3% 29.2%
11 LAC 42 40 51.2% 117.3 1.2 40.4% 60.2% 29.5%
12 MIN 49 33 59.8% 116.8 3.3 42.0% 59.2% 28.5%
13 MIA 43 39 52.4% 116.7 2.2 40.6% 58.0% 26.8%
14 ATL 46 36 56.1% 116.1 2.4 42.9% 58.4% 23.4%
15 TOR 46 36 56.1% 115.9 2.9 36.3% 58.1% 26.5%
16 PHO 45 37 54.9% 115.4 1.5 45.3% 56.8% 22.5%
16 PHI 45 37 54.9% 115.4 −0.1 39.1% 57.2% 27.5%
18 GSW 37 45 45.1% 115.0 −0.6 49.7% 58.4% 23.8%
19 ORL 45 37 54.9% 114.9 0.6 38.6% 57.6% 31.1%
20 POR 42 40 51.2% 114.4 −0.3 46.9% 57.0% 28.0%
20 NOP 26 56 31.7% 114.4 −4.5 35.5% 56.8% 28.4%
22 UTA 22 60 26.8% 114.1 −8.2 40.2% 57.5% 27.7%
23 CHI 31 51 37.8% 113.0 −5.1 44.3% 58.0% 24.6%
24 MEM 25 57 30.5% 112.9 −5.9 43.6% 57.0% 25.1%
24 MIL 32 50 39.0% 112.9 −6.4 45.7% 58.9% 22.3%
26 SAC 22 60 26.8% 111.4 −10.1 33.9% 56.0% 25.6%
27 DAL 26 56 31.7% 111.2 −5.3 35.5% 56.4% 28.7%
28 WAS 17 65 20.7% 111.0 −11.7 40.3% 56.6% 23.5%
29 IND 19 63 23.2% 110.9 −7.9 42.2% 56.8% 25.2%
30 BRK 20 62 24.4% 108.7 −10.3 45.5% 55.9% 27.2%
Green indicates higher values within each colored column. Red indicates lower values. For 3PAr and FTr, color shows offensive profile, not automatic shot quality.

This table gives the baseline before the clustering starts. 3PAr shows how much each team leans into the three-point shot, while TS% shows whether that shot profile is turning into efficient scoring. If the three-point premium were automatic, the high-3PAr teams would also separate cleanly near the top in efficiency.

That is why the other variables matter. FTr captures foul pressure, ORtg captures total offensive production, and Win% adds basic team context. For the 2025-26 season, the league average 3PAr is 41.5%, the league average TS% is 58.1%, the league average FTr is 26.4%, and the league average ORtg is 115.8. Now the question becomes whether K-means sees the same patterns once the teams are grouped by style.

Determining the Cluster Groupings

Before running the model, we need to decide how many groups actually make sense. Too few clusters would lump different offensive styles together, while too many would make the results noisy and harder to explain. The elbow method helps with that by showing where adding more clusters stops making the team groupings meaningfully tighter. The goal is not to find a perfect number of groupings, but to pick a number that gives a clean basketball interpretation.

This matters for both versions of the model. The simple model uses 3PAr and TS% to test the cleanest version of the three-point premium. The advanced model adds FTr and ORtg, which gives the clusters more offensive context by including foul pressure and overall scoring strength.

I ended up going with four clusters because the elbow plot shows the biggest improvements come early, and after that the gains start to shrink. Four groups also give enough separation to compare different offensive styles without pretending every small difference needs its own category.

Four different groupings also passed the smell test to me when thinking about the game of basketball in generat. Teams can be high-volume and efficient, high-volume but less efficient, lower-volume but efficient, or lower-volume and struggling. That structure lines up with the three-point premium question because I am not just asking who shoots the most threes. I am asking whether three-point volume still separates efficient offenses, or whether the premium now depends on more than volume alone.

Model 1: Does More Three-Point Volume Mean Better Efficiency?

This first model keeps the question as clean as possible. It only uses 3PAr and TS%, so it is testing the basic version of the three-point premium. If taking more threes still creates a clear advantage by itself, the higher-volume teams should separate as the more efficient teams.

That is why this model comes first. Before adding free throws, offensive rating, or any other context, I want to see whether three-point volume and scoring efficiency still move together on their own.

Simple Cluster Summary
Four offensive profiles based only on three-point volume and true shooting.
Cluster Group
Shot Profile
Context
Cluster N Teams 3PAr TS% ORtg FTr
● High 3PA / High Efficiency 8 ATL · BOS · CHI · CHO · CLE · GSW · MIL ·
NYK		 45.6% 58.7% 117.0 23.7%
● High 3PA / Lower Efficiency 5 BRK · IND · MEM · PHO · POR 44.7% 56.7% 112.5 25.6%
● Lower 3PA / High Efficiency 6 DEN · LAC · LAL · MIN · OKC · SAS 41.2% 60.2% 118.9 28.8%
● Lower 3PA / Lower Efficiency 11 DAL · DET · HOU · MIA · NOP · ORL · PHI ·
SAC · TOR · UTA · WAS		 37.2% 57.3% 114.7 27.4%
Cluster colors match the plot. This simple model only uses 3PAr and TS%; ORtg and FTr are shown as context.

The results show why the three-point premium is not automatic anymore. The correlation between three-point attempt rate and true shooting percentage is only 0.17, so higher volume does not clearly equal better efficiency.

The highest-volume cluster is Cluster 1: High 3PA / High Efficiency, with an average 3PAr of 45.6% and an average TS% of 58.7%. The most efficient cluster is Cluster 3: Lower 3PA / High Efficiency, with an average TS% of 60.2%.

That is the point of this first model. Shooting more threes can still be part of a great offense, but it does not guarantee one. Volume has to come with shot quality, spacing, personnel, and enough pressure elsewhere on the floor to keep defenses honest.

Model 2: Adding Context to the Three-Point Premium

The simple model says three-point volume is not enough by itself. This expanded model tries to explain why. It still includes 3PAr and TS%, but it also adds FTr and ORtg so the model can account for foul pressure and overall offensive strength.

That matters because efficient offense is not created by shot location alone. A team can take a lot of threes and still struggle if those shots are rushed, contested, or disconnected from the rest of the offense. The better question is whether the three-point shot is part of a complete offensive profile.

Expanded Cluster Summary
Four offensive profiles based on volume, efficiency, foul pressure, and offensive rating.
Cluster Group
Shot Profile
Pressure / Results
Cluster N Teams 3PAr TS% FTr ORtg
● Rim and Free Throw Pressure 7 CLE · DEN · LAC · LAL · MIN · OKC · SAS 41.7% 60.1% 28.5% 118.9
● Efficient Perimeter Offense 7 ATL · BOS · CHO · GSW · MIL · NYK · PHO 46.0% 58.4% 23.0% 117.1
● Rim and Free Throw Pressure 10 DAL · DET · HOU · MIA · NOP · ORL · PHI ·
SAC · TOR · UTA		 36.9% 57.4% 27.8% 115.0
● Three-Heavy Without Premium 6 BRK · CHI · IND · MEM · POR · WAS 43.8% 56.9% 25.6% 111.8
Cluster colors match the plot. The expanded model uses 3PAr, TS%, FTr, and ORtg.

The expanded model gives the simple model a better explanation. The chart still shows 3PAr and TS%, but the clusters are now built with FTr and ORtg included. That means teams can move into different offensive groups even if they look similar on the basic shot-profile chart.

The best offensive-rating cluster is Cluster 2: Rim and Free Throw Pressure, with an average ORtg of 118.9, an average TS% of 60.1%, and an average 3PAr of 41.7%. The highest three-point-volume cluster is Cluster 4: Efficient Perimeter Offense, with an average 3PAr of 46.0% and an average ORtg of 117.1.

The foul-pressure piece matters too. The strongest FTr cluster is Cluster 2: Rim and Free Throw Pressure, with an average FTr of 28.5%. That helps explain why the three-point premium cannot be judged by volume alone. A team can create value by spacing the floor, but it can also create value by pressuring the rim, getting to the line, and forcing defenses to guard more than one thing.

That is the main difference between the two models. The simple model says volume is not enough. The expanded model shows why. The three still matters, but it works best when it is part of a fuller offensive profile.

What the Models Actually Say

The clusters make the three-point question less clean, but a lot more interesting. If the premium were automatic, the answer would be simple: shoot more threes and become more efficient. That is not really what the data shows.

In the simple model, the correlation between 3PAr and TS% is only 0.17. That does not mean threes are bad. It means three-point volume by itself is not enough to explain team scoring efficiency. Some teams can take a lot of threes and make the math work. Others take a lot of threes and do not separate from the league.

The expanded model helps explain why. The correlation between 3PAr and ORtg is 0.085, while the relationship between FTr and ORtg is 0.082. That matters because free throws are part of shot value too. A team that pressures the rim and gets to the line can create efficient offense even if it is not built around the highest three-point volume.

That is the biggest takeaway from the clusters. A high-volume, high-efficiency team is not the same thing as a high-volume, average-efficiency team. Both may shoot a lot of threes, but only one is really capturing the premium.

So the answer is not that the three-point premium is gone. It is that the premium is more conditional now. It belongs to the teams that create good threes without giving up rim pressure, foul pressure, spacing, or overall offensive balance.

The Premium Did Not Disappear. It Evolved.

The answer is yes, the three-point premium still exists. It just does not exist in the lazy version anymore.

A good three is still one of the best shots in basketball. An elite shooter taking a clean three with spacing around him is still a win for the offense. Steph Curry is the easiest example because his range changes the defense before the shot even happens. That kind of three creates value even when he does not shoot, because the defense has to stretch itself around the threat.

The cluster results show why the answer is more complicated now. In the simple model, the correlation between 3PAr and TS% is only 0.17, which means three-point volume by itself does not clearly explain scoring efficiency. The expanded model adds the missing context. The correlation between 3PAr and ORtg is 0.085, while the relationship between FTr and ORtg is 0.082.

That matters because a two-point attack can still create efficient offense if it gets to the rim, creates fouls, or forces defensive rotations. At the same time, a three-heavy offense can lose value if too many of those shots are rushed, contested, or taken by the wrong players. More threes are not always better threes.

That is why the old version of the three-point premium is mostly gone. The league already knows the math. The easy edge from simply taking more threes has been squeezed out. What remains is more specific. The premium belongs to the right shooters, taking the right shots, in offenses that still pressure the defense in other ways.

So the final answer is not that the NBA should stop shooting threes. That would be ridiculous. The answer is that the premium is still real, but it is conditional. The edge now is not just living by the three. It is knowing which threes are actually worth living with.