NBA 3-Point Case Study
In the 2014-15 season, NBA players made about 35% of their three-point attempts. What shot conditions drove that number — and what could have changed it? Four tools let you explore the data yourself.
Four Tools, One Dataset
Each tool asks a different question of the same 32,511 three-point shots. Start anywhere.
All-or-Nothing Simulator
Change the conditions of every shot at once — what if every shot were taken with the defender 9+ feet away? See how the overall make rate shifts under a published 6-variable model.
Fine-Tuning Simulator
Shift the mix of shot conditions gradually — move 20% of tight-defense shots to open, rather than moving every shot at once. Closer to how coaching adjustments actually play out.
Shot Log Explorer
See how shots were distributed across conditions, which conditions moved together, and how one group of shots compared to another. Frequencies, correlations, and crosstabs.
Model Builder
Build your own logistic regression. Add or remove shot-condition variables to see how model fit changes, or let Auto-Build find the best-performing combination.
About the 2014-15 NBA Season
A Pivotal Season for the 3-Point Shot
The rise of the three
Teams attempted a then-record 22.4 three-pointers per game, up from 18.0 just five seasons earlier.
Splash Brothers era
Golden State won its first title in 40 years behind MVP Stephen Curry's record-breaking 286 made threes.
SportVU tracking
The NBA's SportVU cameras recorded shot-level data — defender distance, shot clock, dribbles, touch time — for the first three-quarters of the season.
Defender Distance
How close was the nearest defender? (0-3, 3-6, 6-9, or 9+ ft.)
Shot Distance
From the center of the basket (22-24 up to 26+ ft.)
Shot Clock
Late (under 4s) or open, plus touch time before the shot
Catch vs. Dribble
Did the shooter catch and shoot, or dribble first?
The Original Data
The NBA's SportVU optical tracking system (later Second Spectrum) recorded every shot in the first three-quarters of the 2014-15 regular season, producing the most granular public shot-level dataset the league has ever released.
Variables in the Published Model:
Ready to explore what drove the 35%?
Simulate scenarios, compare distributions, or build your own model from scratch.