Cilliza Discusses Allan Lichtman’s Prediction

By
Jennifer Gray
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Well, it seems that Allan Lichtman, the historian famous for his “13 Keys to the White House” election prediction model, has hit a bit of a snag with this one.

Lichtman, who accurately predicted nine out of the last ten presidential elections, thought he’d nailed it once again by forecasting a win for Kamala Harris over Donald Trump. But, as we all know now, that prediction didn’t hold up. Now, instead of owning up to the miss, Lichtman’s pointing fingers everywhere but his own model. And Chris Cillizza—himself no stranger to political hot takes—isn’t buying it.

According to Lichtman, his prediction failed not because the model itself might be flawed but because of “disinformation,” Elon Musk’s influence, and a trio of “isms”: misogyny, racism, and xenophobia. Apparently, the “13 Keys” approach—allegedly based on objective factors that predict election outcomes independent of campaign noise—suddenly got thrown off by external forces.

Cillizza, who used to work for CNN as an analyst, took to his YouTube channel to call out this excuse-riddled rationale. He pointed out the obvious contradiction: the whole point of the “13 Keys” model is that it’s supposed to predict outcomes based on structural factors, not cultural or social biases. As Cillizza put it, “The entire justification of the keys is: the campaign doesn’t matter.”

So why does Lichtman think his model failed? Well, he seems convinced that Americans weren’t acting “rationally or pragmatically” due to an “explosion of disinformation” and that society’s prejudices overpowered logic at the ballot box. The irony here is pretty rich—after all, Lichtman’s model is supposed to factor in the irrational swings of the American electorate. That’s the whole premise. Blaming the outcome on the electorate’s alleged irrationality seems a little too convenient, doesn’t it?

Lichtman even took it a step further, saying the election was skewed by “misogyny, racism, and xenophobia.” Look, claiming these “dark trends” in American history explain election results isn’t a new tactic. But for a model that supposedly runs on objective markers, how do you go from statistical trends to essentially accusing half the country of being ruled by hate? It’s quite the leap. Cillizza even pointed out that when you create a prediction model, you should be prepared to deal with outliers and human unpredictability. Instead, Lichtman seems more interested in arguing why the electorate itself “failed” his model rather than the other way around.

And it’s not just Cillizza who’s skeptical. Even radio host Charlamagne Tha God and CNN’s own Van Jones have voiced their doubts about the “racism” and “sexism” explanations for election outcomes. For all the divisiveness we’ve seen in the media, they recognize that Americans—across the political spectrum—don’t make decisions solely on identity politics. They’re facing real issues—rising prices, crime rates, and border security—that weigh more on their minds than the labels people try to slap on them.

Cillizza summed it up pretty well when he said sometimes, “You gotta just take the L.” If you’re in the business of predicting elections, you’ll get it wrong sometimes. That’s the nature of any model—especially one that relies on assumptions about voter behavior. Lichtman’s refusal to admit that maybe his system isn’t as foolproof as he thought is precisely why his explanation is getting dragged in the court of public opinion. Predicting the future is tough, but making excuses when you’re wrong? That’s just sloppy.

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