NBA Shot Fine-Tuning Simulator
Published model · Gradual Distributional Shifts · ← All-or-Nothing Simulator
33,362 three-pointers from the 2014-15 season. League average: about 35%. What if the mix of shot conditions had been slightly different — a few more open looks, a few fewer deep pull-ups?
Same published model as the All-or-Nothing Simulator — Electric Insights' release-day explanatory account of NBA three-point make rates — with redistribution rather than pinning.
Shift the mix of shot conditions gradually — moving 20% of tight-defense shots to open rather than moving every shot at once.
How to Use This Tool
Shift shot-condition distributions gradually and see how the make rate would change
How to Use This Tool
Shift shot-condition distributions gradually and see how the make rate would change
1. Choose league or player
Start with All players to use the league-wide published model, or switch to a player such as Stephen Curry to re-fit the six-variable model on that player's shots only. The sliders reset to that player's observed baseline when you switch.
2. Try a preset or drag sliders
Use the preset buttons for a quick start, or drag sliders to change the mix of shot conditions for any variable. The colored bar under each variable's header shows its model leverage. Each variable's percentages must sum to 100.
3. Run the scenario
Click Run scenario to send your slider settings through the fitted model. The result shows the projected make rate and a 95% confidence interval. The How certain is this result? chart shows 10,000 simulated outcomes so you can see how much the baseline and your scenario overlap.
4. Explore each factor
Click Explore Each Factor after a run to see every level of every variable, holding your other sliders fixed. This shows whether defender distance, shot clock, or shot distance is doing most of the work in your current scenario.
5. Shift distributions gradually
Unlike the All-or-Nothing simulator, sliders let you move just some shots from one condition to another — for example, shifting 20% of "Tight" defense to "Open." This tests realistic, incremental shifts in shot selection rather than all-or-nothing scenarios.
6. Reset and iterate
Click Reset to baseline to return all sliders to the current player's observed baseline mix. Switching players also resets the scenario so the new model starts fresh. Past runs are saved in the Saved Scenarios drawer at the bottom.
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Set the distribution
Predicted outcome
How did you change the survey?
Each pair of bars compares the actual distribution (green) to the distribution under your scenario (red where you changed it). This is what you changed — not the predicted impact, just the input.
How certain is this result?
10,000 simulated outcomes drawn from the model's coefficient uncertainty, for both the baseline (green) and your scenario (red). Where the distributions overlap, the model isn't certain the two would produce different headlines.
Calibration: predicted vs. observed
How closely the model's predicted probabilities track the observed outcome rates, binned by predicted decile. Bubble size shows respondents per bin. The dashed diagonal is perfect calibration; the blue scenario line is your run; the gray line (when present) is the unmodified base model for comparison.
Explore each factor
For each variable, see how the predicted outcome would change if you switched that one distribution to 100% at each level — holding all your other selections fixed. Reveals which individual levels have the most leverage given your current scenario.