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 — 8,000 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 in a state takes its electoral votes.
4. Add up the votes; first to 270 wins.

Then we count the results: the Republican won in 75% of those 8,000 runs — that's the implied probability. The electoral-vote range 239–335 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. Modelling that correlation is what separates an honest forecast from flipping 51 separate coins (which would make landslides nearly impossible and pin every election near 50/50).

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.