This is really cool! As someone who’s been doing these calculations in a somewhat haphazard way using a mix of pen and paper, spreadsheets, and Python scripts for years, it’s nice to see someone put in the work to create a polished product that others can use.
Something that I’ve been meaning to incorporate to my estimates and that would be a killer feature for an app like this is a reasonable projection of future earnings, under the assumption that you’ll get promoted / switch career paths at the average rate for someone in your current position. Sprinkle a bit of uncertainty on top, and you can get out a nice probability distribution over “time at FI” and “total money donated”.
Great analysis!
I wonder what would happen if you were to do the same exercise with the fixed-year predictions under a ‘constant risk’ model, i.e.
P(t) = 1 - exp(-l*t)
withl = - year / log(1 - P(year))
, to get around the problem that we’re still 3 years away from 2026. Given that timelines are systematically longer with a fixed-year framing, I would expect the Brier score of those predictions would be worse. OTOH, the constant risk model doesn’t seem very reasonable here, so the results wouldn’t have a straightforward interpretation.