“My gut feeling says Donald Trump. And I suspect this is true for many concerned Democrats.”

So writes Statistician Nate Silver in the New York Timesand adds that your gut feeling – and yours – can’t be trusted.

The Silver Bulletin The author, founder of FiveThirtyEight and former baseball analyst, rose to fame by analyzing quantitative data and statistics in politics and making election forecasts based on probabilistic models that broke down weighted averages of public opinion polls.

But at least this year, he has thrown in the towel on making a clear decision, saying former President Donald Trump and Vice President Kamala Harris are locked in a true neck-and-neck race as the polls point them at one Head-to-head racing brought.

“You should come to terms with the fact that a 50:50 forecast actually means 50:50,” he wrote. “And you should be open to the possibility that these predictions are wrong, and that could be the case both in the direction of Mr. Trump and in the direction of Ms. Harris.”

Silver said that with both candidates only a point or two apart in the seven battleground states that will likely decide the election, error is “the only reliable prediction.”

Silver Model of the 2024 race currently reflects this, noting that “we honestly don’t know” who will win.

Silver, in his Just opined, made a seemingly counterintuitive statement: “It is surprisingly likely that the election will not be a photo finish.”

To that end, he pointed out that either Trump – whose supporters are often low in civic engagement and can be harder to detect in public opinion polls – or Harris – who could benefit from pollsters subconsciously weighing in Trump's favor – win the election comfortably could by pushing the choice to the edge of the margin of error or beyond.

In fact, he said his model shows a 60 percent chance that one of them will win the Electoral College votes from six of the seven battleground states.

Justin Grimmer, a Stanford political scientist, raised concerns about election forecasters using probabilistic models in one year interview with the Harvard Graduate School of Arts and Sciences Colloquiy podcast earlier this month.

“The clearest failure of these predictions is in 2016, when the predictions coming out of these models were very confident to moderately confident that Clinton would win,” he said, pointing out that Silver's model gave Trump a much higher 28.6 percent Chance admitted winning than other forecasters. “Certainly the consensus view across all models was a Clinton win. And that didn't happen. And so they fundamentally misunderstood this election. When we think about harm, it’s interesting to think about how these models are consumed by the public.”

Grimmer said many news organizations turned to forecasting after 2008 and 2012, when Silver correctly predicted Barack Obama's election victories in most states, and realized it could fill content needs.

“It turns out it’s a nice summary of what could happen,” he said. “And of course a lot of stories emerge. If the probability of a candidate winning an election goes from 55 to 50 percent, perhaps that could be a whole cycle of stories about what happened, why that probability changed, and what the origin might be.”