The buckets we’re given here are pretty narrow: Pennsylvania alone for example has 19 votes. So either of the middle bucket’s widths is less than two Pennsylvanias. And of course, we should expect the results of individual states to be correlated positively with one another most of the time, which makes the distribution for electoral college votes wider.
For a numerical estimate I looked at some other electoral college forecasts around the web, including FiveThirtyEight (still their estimate for Biden, which they haven’t updated yet), Polymarket and Metaculus. Here are their cumulative distribution functions on the same scale compared to us:
As you can see, the range of the buckets we’re given here is comparatively small. Maybe that anchors people? Anyway, the other forecasts have much heavier-tailed distributions. Here is the code for the graphic. Anyone interested should also be able to run it themselves right on there to get current data from the other three sites: https://colab.research.google.com/drive/1Sq473ytB8KYJtoXp9qjl0nb_zRkW45BU
The script also converts the three other estimates into Good Judgment Open's scale. Here is what they would predict on this question:
To make the numbers comparable among the different sites I’m assuming Trump has a 100% chance of remaining the candidate. So that’s maybe another percent or two for the lowest bucket. Also in aligning buckets for different sources, I’ve made some off-by-one errors in the number of electoral votes that I was too lazy to fix.
I think the Polymarket probably overestimates unlikely outcomes due to liquidity concerns and the favourite-longshot bias. Metaculus has a tool to enter distributions, but by design it’s hard to make nice heavy-tailed distributions with it.
Why do you think you're right? (optional)
The buckets we’re given here are pretty narrow: Pennsylvania alone for example has 19 votes. So either of the middle bucket’s widths is less than two Pennsylvanias. And of course, we should expect the results of individual states to be correlated positively with one another most of the time, which makes the distribution for electoral college votes wider.
For a numerical estimate I looked at some other electoral college forecasts around the web, including FiveThirtyEight (still their estimate for Biden, which they haven’t updated yet), Polymarket and Metaculus. Here are their cumulative distribution functions on the same scale compared to us:
As you can see, the range of the buckets we’re given here is comparatively small. Maybe that anchors people? Anyway, the other forecasts have much heavier-tailed distributions. Here is the code for the graphic. Anyone interested should also be able to run it themselves right on there to get current data from the other three sites: https://colab.research.google.com/drive/1Sq473ytB8KYJtoXp9qjl0nb_zRkW45BU
The script also converts the three other estimates into Good Judgment Open's scale. Here is what they would predict on this question:
538's (still Biden) forecast with GJOpen's bucket:
-235: 34.9%
235-269: 14.4%
269-307: 17.5%
307-: 33.2%
Polymarket's forecast with GJOpen's bucket:
-235: 41.8%
235-269: 16.3%
269-307: 12.5%
307-: 29.4%
Metaculus' crowd forecast with GJOpen's bucket:
-235: 23.8%
235-269: 31.3%
269-307: 26.9%
307-: 17.9%
Why might you be wrong?
To make the numbers comparable among the different sites I’m assuming Trump has a 100% chance of remaining the candidate. So that’s maybe another percent or two for the lowest bucket. Also in aligning buckets for different sources, I’ve made some off-by-one errors in the number of electoral votes that I was too lazy to fix.
I think the Polymarket probably overestimates unlikely outcomes due to liquidity concerns and the favourite-longshot bias. Metaculus has a tool to enter distributions, but by design it’s hard to make nice heavy-tailed distributions with it.
Comment deleted on Nov 12, 2025 02:22PM UTC