Understanding ProbCalc
The original guidebook behind DDTSR's BaseCalc & ProbCalc — how PickleArtGeek builds a polling average and a Monte Carlo win-probability model from a plain Wikipedia polling table, in Google Sheets.
Synopsis
ProbCalc started as a one-off Google Sheets election predictor in November 2025 and has since grown to cover sports, First-Past-the-Post constituencies, and a more structured winner-identification system. The guide walks through it in three parts.
Part I introduces the inverse gamma distribution ProbCalc is built on —
its shape (α), scale (β), and position (p) parameters — and how a bigger α tightens the
distribution around a result while β simply rescales the axis.
Part II covers "Simple ProbCalc": BaseCalc first, where polls are
copied from Wikipedia, cleaned up, and combined into a single α per candidate via
(poll result × sample size) ÷ (days until election) — so bigger, more recent
polls count for more — then normalized into a percentage; then ProbCalc itself,
which feeds those same α values into GAMMA.INV(RAND(), α, β) to simulate
an election outcome, repeated hundreds of times and tallied with
INDEX/MATCH/COUNTIF to get a win probability per
candidate.
Part III gets more advanced: classifying a simulated win as a
plurality, majority, or supermajority; extrapolating results for a subgroup (like a
demographic or constituency) that lacks its own local polling by adjusting the topline
α by that group's historical lean; and converting simulated percentages back into a
feasible raw vote count.
This is the same core method DDTSR's BaseCalc & ProbCalc tool runs on this site — α is computed directly from polling data this way, and β is fixed at 1.
Read it here
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