A rival team has an ambitious but craven assistant coach who, with a regime change, could be better controlled towards our interests.

Another team's key player couldn't be replaced on short notice, were they to be removed from the board, in a deniable 'gang-related' incident in front of the nightclub they frequent.

The roster of a favored team could be boosted with secret financial incentives to candidates, funded off-the-books by new gambling and prostitution operations.

Small potatoes- more realistic: Les Wexner pumped tens of millions into the university to whitewash his reputation; keep them perennial favorites but grease some SEC refs as necessary to keep our guy out of the limelight

I grew up with a kid whose dad worked in security for the St. Louis Rams NFL team. I was shocked when he told me how many attempts there are to blackmail or otherwise extort money from players.

The radio station chief is a con artist rainmaking the agency, he will cut us in if we squeeze him hard enough.

This team sounds fun

In this model the probabilities were measured by an ensemble of LLM's. We give them context and ask them to follow a specific methodology for making a forecast, and then aggregate their forecasts into a "crowd" forecast. We can get input from humans as well to compare/combine them further, but in this particular instance I just used our AI Forecaster.

You're right that a human analyst - especially a very experienced one - is going to be able to still do a better job of analysis most of the time, but the AI is useful for having loops to re-examine the question, consume and curate new information, etc. We've built the system with that assumption: let humans do what they're best at, let AI do what it's best at...

This could be a whole sub plot in a comedy with a bunch of out of touch analysts. "he's with her because he's into thick ones", "no that's just how black guys are", "no she must be the throat goat". You could even add a whole additional layer of jokes into it where they project snobbish DC norms onto people who aren't that.

Unfortunately nobody will ever make a serious workplace comedy that ridicules federal intelligence because hollywood and the feds are buddy buddy.

Not exactly - all the forecast questions get scored so we know how accurate the AI is and how calibrated it is. It's no different than asking a large crowd of humans on Good Judgment Open or Metaculus these same types of questions. Everything we ask eventually happens or it doesn't, and we can score it all.

I think it's important to to point out that there's a lot of "slop" to go around. I mean, making bad decisions and/or projections based on flawed data is nothing new.

The entire human scientific community has been shown recently on average to be generating "slop" and calling it science.

My bottom line: humans generate a lot of BS--and LLM's are just averaging it and giving it back to us.

It's not about joy, it's about selling their "analysis".