Political Simulator

1 · The factors that go in

• • Each state's partisan lean — from the certified 2024 presidential results, expressed relative to the nation. Pennsylvania leaned R+0.2, California D+22.3. That relative lean is the stable structure that carries between elections.
• • The national environment (now R+1.5) — the assumed nationwide two-party margin. The baseline is the real 2024 result, R+1.5. Moving it shifts every state by the same amount.
• • Uncertainty — no forecast is exact, so each run adds random error sized to how far real forecasts have historically missed: a national error (~2.5 pts, the whole map can tilt) and a per-state error (~5 pts, each state wobbles on its own).

2 · The process — 10,000 simulated elections

Each simulated election:

1. Draw a national environment — R+1.5 plus a random national error.
2. For every state: margin = national + state lean + state error.
3. Whoever's ahead takes the state's electors, except Maine and Nebraska: two go to the statewide winner and one to each congressional-district winner.
4. Add up the votes; first to 270 wins.

Then we count the results: the Republican won in 75% of those 10,000 runs — that's the implied probability. The electoral-vote range 239–336 is the middle 90% of outcomes.

3 · Why it's not 51 coin flips

The national error is shared across every state, so they move together — a good night for one party is a good night across the map. Modeling that correlation avoids treating statewide and district outcomes as independent coin flips.

4 · What it is — and isn't

It is: the partisan structure of recent results, plus the environment you set, plus honest uncertainty — turned into odds. It isn't: a prediction of turnout, candidates, scandals, or the economy. Those enter through the levers and the dial; the model just translates your scenario into probabilities.