We’ll dip into what the model says in a moment, but first a note about models in general: there are a lot of them, from complex equations generated by nerdy academics (like the work by Patrick Hummel and I seen here) to funny coincidences like the Redskins Rule, which holds that the incumbent party keeps the White House if Washington’s football team wins its last home game. (This is true in 17 of the last 18 elections!) Every year, some of these models are right and some are wrong, and the difference is often just luck. As a result, models get a bad rap as being very good at predicting the past and lousy at predicting the future.

But every election gives researchers more data to work with and a better idea of what works and what doesn’t. Not all models are bogus just because many of them are. Our model combines powerful scientific algorithms with both real-time and historical data sources. We have examined the last 10 presidential elections and found that our new model would have correctly predicted the winner in 88 percent of the 500 individual state elections.

The following chart shows our predictions for each state in the general election, based on this model:

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