Several presidential election cycles ago Nate Silver emerged as a media darling statistician with his model that seemed to beat all the other pundits in accurately predicting the outcome of the voting.

Then he was poached away from the site that he built and after that he was poached away again and with all that poaching he became way overdone and much too big for his albumen. Or his britches. Or something. 

The point is he started to try to predict things that his mathematical statistical model was in no way capable of predicting, and he gloated and preened and turned into a genuine asshole.

Eventually he struck out on his own again, and by some accounts he is back on track, but you know me. Once somebody makes it onto my coveted asshole list, its very hard to scrape them back off.

So I’ve bestowed my favors on G. Elliott Morris and his statistical models at Strength in Numbers.

On the other hand, I don’t like to spend too much time studying the electoral predictions, because, well, I don’t want to end up disappointed like I have been so many times in the past.

But (V) at E_V.com has provided a summary of Morris’s latest simulations, and I thought I’d pass it along.

These are the results of 40,000 probability-based simulations. In 85% of the runs, the Democrats controlled the House. In 55% of the runs, they controlled the Senate. The odds of winning both are 50-50, a true tossup. One of the most important factors is the national popular vote (NPV) for Congress. If Democrats win less than 4%, they are unlikely to win either chamber. In the 4-7% range, they will probably win the House and lose the Senate. If they win the NPV by at least 7 points, they will probably win both. Currently they are roughly at D+8 in the generic House poll, which is a rough predictor of the NPV. Of course, each (Senate) election has its own quirks due to the candidates’ campaigns, mistakes, and scandals, funding, and so on that models can’t fully capture. Morris is going to run the model regularly going forward as new Senate polling comes in, the fundamentals (e..g, Donald Trump’s approval) change, and the handicappers change their predictions based on all the many factors they consider. The idea is to factor as much available information as possible into the models. The tricky part is to figure out how to weight each piece.

On the same day, (V) also had an article titled Bank Closed Hundreds of Trump’s Accounts on Account of Money Laundering.

It concerned Capitol One Bank closing 385 of the bank accounts owned by that jackass currently in the Oval Office and the fact that this only became public knowledge because that jackass sued the bank.

Some interesting pieces of information appear in that article. Did you know that U.S. currency weighs exactly 1 gram per bill? I verified it on my kitchen scale. So who says the US doesn’t use the metric system? It’s easy to calculate how much a suitcase full of a million dollars weighs.

Anyway, that whole article is worth a read. The bank closed those accounts because of suspicions of money laundering, and now that the jackass has made those closures public, a future Democratic AG or a state AG could very well take notice.