Hello. My name is Ben Welsh. I'm an Iowan living in New York City.
I am a reporter, an editor and a computer programmer. My job is to use those skills, together, to find and tell stories.
I work at Reuters, the world's largest multimedia news provider, where I founded the organization's News Applications Desk. In that role, I lead the development of dashboards, databases and automated systems that benefit clients, inform readers, empower reporters and serve the public interest.
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- https://github.com/palewire/first-python-notebook
TBH I enjoyed looking up Ben and finding out what he's about and done in the past far more than I did just knowing there's a 538 archive on IA.
> Thousands of FiveThirtyEight articles seemingly vanish from the internet
https://www.editorandpublisher.com/stories/thousands-of-five...
And discussions here on hn:
ABC News has taken all FiveThirtyEight articles offline https://news.ycombinator.com/item?id=48152553
Disney erased FiveThirtyEight (article by Nate himself) https://news.ycombinator.com/item?id=48197703
https://web.archive.org/web/20230205124354/https://fivethirt...
It's kinda sad to know no one else will get to experience those interactive visualizations. Though its nice to see the approval comparison page still works
https://web.archive.org/web/20241031232233/https://projects....
https://web.archive.org/web/20140701122958/http://fivethirty...
I remember thinking they were the best data journalists out there, and they had some nice visualizations but did their other predictions actually hold up?
This is why people don't really buy the "but he had Trump at 30%, you just don't understand statistics" apologist line. Sure he hedged in the dying days of the campaign (a cynic might think to try to protect his credibility), but the tone overall was of a person who comprehensively failed to understand the mood of the country from beginning to end.
Which is a problem because these election predictions are not just pure "mathematical models" and "data driven" like 538 would have had you believe. What mathematical model should be used? What data should and should not be used? At some point those things are based on the modeller's understanding of reality.
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HN veers toward "the guts of the content w/out decoration" - limited additional information, framing, weasel words, perceived slanting, etc.
It's uncommon to name an author unless the author themself is an important part of "the story".
I personally have no issue with the original title, however it's not really for me (non US citizen) to judge whether the reporter in question has a name / identity that carries weight in US IT circles.
It helped when I developed an allergy to healing crystals.
After that election, a certain group would tirelessly work to discredit him any time his election predictions were not entirely one-sided.
Yes, he did: https://www.crainsnewyork.com/real-estate/residential/cny-na...
The downside is this furthers the divide between folks who pay for subscriptions and masses who get shoveled ad-powered slop.
https://en.wikipedia.org/wiki/Electoral-vote.com
> Electoral-vote.com is a website created by computer scientist Andrew S. Tanenbaum. The site's primary content was originally poll analysis to project election outcomes. Since the 2016 elections, the site also has featured daily commentary on political news stories.
https://blog.archive.org/2017/04/17/robots-txt-meant-for-sea...
https://blog.archive.org/2018/04/24/addressing-recent-claims... which is a year later mentions that they have an automated process which is still following robots.txt for displaying old pages where the robots.txt was added later.
https://help.archive.org/help/using-the-wayback-machine/ does say they follow it for scraping, but this is phrased in such a way that would still be true for past sites whether or not they changed the policy. There is a page https://www.sysjolt.com/2021/archive-org-no-longer-honors-ro... which claims they don't follow it, but the site owner misspelled "robots" as "robot".
If archive.org can be manipulated to remove content either via legal threats or simple robots.txt it loses a significant portion of its societal value.
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EDIT: dude have you heard of the s in https, http://johntantalo.com gets flagged.
This is just some Disney suits being extraordinarily petty.
"I did approach Disney a year or two ago, through my agent, about acquiring the remaining IP. ...
We were told to basically get lost: ABC was annoyed with my critical public comments about their management of FiveThirtyEight. It apparently wasn’t a long conversation, so I don’t have a lot more color to report than that."
Here are some numbers roughly in the right ballpark: during the Disney era, which lasted about 10 years, FiveThirtyEight published about 20 stories a week. Let’s say that each story took about 20 hours to produce between research, writing, graphics and editing.3 Do the math, and that works out to about 200,000 person-hours of work that ABC News just deleted.In another sense, it's a journalistic source with information and commentary on past elections. Even aside from the political context that muddies the waters around or outright denies results, matters of public discourse on the web should not be ephemeral or subject to the decisions of the publication - they should be archived.
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But predicting an election requires a lot more than polling datasets and statistics textbooks. That's the problem that he made himself out to be an election prediction wizard, but really that was off the back of his good prediction in quite a bland and conventional election.
When things got slightly more spicy and reality diverged from his vaunted "models", his "data science" predictably fell in a heap. The worst thing is almost not even that he got it wrong, it's that he seemed incapable of recognizing that present reality was quite significantly different from the past data he had used to build his models. Even after being wrong in so many of these predictions. He just kept churning out these pieces about how Trump was probably finished this time.
> But predicting an election requires a lot more than polling datasets and statistics textbooks. That's the problem that he made himself out to be an election prediction wizard, but really that was off the back of his good prediction in quite a bland and conventional election.
> When things got slightly more spicy and reality diverged from his vaunted "models", his "data science" predictably fell in a heap
The models were correct in two elections - arguably three because a 30% chance means that an outcome will occur in thirty times out of hundred. That is not zero.
To the person who is running this LLM, please find better things to do with yourself.
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I definitely think a human was involved in signing up for the account and occasionally checks in.
I think my response was plenty coherent.
I still think that’s about accurate. Maybe it should’ve been 40%.
Everyone forgets that it was a pretty close election. Clinton could’ve won without the Comey announcement.
> I still think that’s about accurate. Maybe it should’ve been 40%.
It wasn't accurate. This is something people misunderstand about these predictions. If the 2016 election was held 100 times, Trump would have won 100 times. It's not the same as rolling dice.
These election predictions don't say that. They say something like "the observations I have agree with scenarios that have Clinton winning, 70% of the time". Which is fine and correct as far as his data and model goes, but none of those scenarios were the reality he was trying to predict. They are all just figments of the model though. Getting down to the brass tacks, he predicted Clinton would win, and he was wrong.
Which is fine, we just can't know anything about his process from that failure. Certainly we can't conclude that it was "accurate", since it was not. If we had a good sample of elections where he used the same process and built up a good record then sure.
40k voters, that's really not very many. So it's hard to say whether Trump had a 30% chance of winning or 40% or whatever, but the election at most was a toss-up.
Many random events could have resulted in a different outcome.
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Reality: someone wins.
Internet: "How did 538 mess this up so badly?"
(Something which got worse after Harris was picked, although every polling aggregator went barmy - there's suspicions that a lot of polling agencies aggressively normalized their data to avoid being seen as biased, leading to an almost 50/50 split.)
It's just that this particular criticism (that they got it "wrong") doesn't seem very well founded.
They chose to pretend Biden's mental decline wasn't happening because that was the Democrat party line at the time. How can you trust predictions from someone who is willing to manipulate his results to prop up his political party?
The issue the models had in that period was pretty well documented: the models rated fundamentals (like economic indicators) more highly relative to polls when the election was farther in the future. Those were the same economic indicators that famously do not capture the pain average Americans have felt since COVID, and that politicians across the spectrum have tried to use to deny that pain when they are in power.
You can argue that this issue was well known at the time, and that a better group of analysts might have taken action to mitigate it. I'd argue that myself. It turns out that it's kind of hard, but that's a terrible excuse not to try. That is different than deliberately manipulating results for political ends, though. It's unclear what political ends those would even be -- who exactly benefits from an "unclear winner" forecast in that situation?
You must _hate_ it when you need <= 4 and you roll a 5...
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"Oh but it was only a 70% prediction"
You can't 70% win an election. Silver's prediction was that Clinton would win, but he was not super confident about it. The prediction was wrong. He was right to not be super confident about it, but the prediction of who would win was still wrong.
538's own post-mortem's on the event highlight that Trump was a very unusual candidate running in a very unusual election and as such the model was missing a lot of important information. They learned from the experience and adjusted the model going forward. Anyone complaining about that event is really just highlighting that they don't understand how statistical modeling works and are upset about how the model misled them or others which isn't Nate or 538's fault and is entirely on the consumer of their reporting. It's not like they didn't try to educate their consumers in their reporting.
The prediction is for one single outcome at one point in time. The prediction can not be that Clinton 70% wins it, or wins it 70 out of every 100 times because there is no 100 2016 elections. Those things may apply to his mathematical models, but obviously the models are attempting to predict the real world. Try to weasel out of it as much as you like, but the prediction was that Clinton would win, and the prediction was wrong.
"Oh he was only giving the odds for his model, you don't understand it's your fault he mislead you" -- no. Every analyst and pundit has a model or a system, obviously nobody thinks any of them can see the future. Nate Silver was very explicitly predicting the outcome of the election. As you can see from all his commentary articles that came out along with the numbers.
And yes, 538's vaunted models and data science fell over when encountering situations that had not been seen or anticipated or built on before, obviously. We didn't need Einstein or even Nate Silver to tell us that. That's the problem isn't it. All this hamming up of "data science" and "mathematical models" is meaningless. Your data and math can be perfect and correct, but if they fail to provide an understanding of the world, then they are perfectly useless.
Yes. And the 2nd Law of Thermodynamics was just violated by millions of atoms within my lungs, that happened to increase in energy above the ambient average due to collisions. Clearly thermodynamics is pseudoscience, too!
The 70% figure is saying “we know most of the information needed to determine what the outcome of the election will be but we don’t know everything so can’t be certain”. There is no process where you can know every factor that determines the result in advance with absolutely accuracy and I don’t know why people expect there would be.
[1] https://www.sciencedirect.com/science/article/pii/S026137942...
What it would be reasonable to say is if his model had correctly predicted the outcome of a significant sample of elections, then you could say his model has some accuracy or predictive power. But it still would never have been accurate or right in the specific instances it got wrong, that's just a misconception about how statistics and predictive models work. I hope this helps.
>What it would be reasonable to say is if his model had correctly predicted the outcome of a significant sample of elections, then you could say his model has some accuracy or predictive power.
I don't know why you're couching that in a hypothetical, FiveThirtyEight has repeatedly done that exercise.
>But it still would never have been accurate or right in the specific instances it got wrong
It is core to the concept of a probability that the result is going to go the opposite way from the prediction sometimes! It's meaningless to call it "wrong".
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When somebody gives a prediction of the outcome of an election? I classify it as correct if they predicted the candidate who wins.
> Do you take every 51% prediction from FiveThirtyEight and if the result is a win you consider that forecast accurate? And every 49% prediction must result in a loss? This just not how statistical forecasts work.
No, but it is the way to map statistical forecasts to reality. He was quite explicitly predicting the outcome of the actual election. That prediction was incorrect.
The whole rating of the accuracy of these models is really snakeoil dressed up as science. There is a lot less rigorous science and a lot more feelings and adjusting numbers and twiddling formulas retrospectively than you were probably led to believe.
Would a 99-1 for Trump model have been worse or less accurate than a 51-49 for Clinton model? Despite predicting the correct outcome whereas the Clinton model predicted the incorrect outcome?
> I don't know why you're couching that in a hypothetical, FiveThirtyEight has repeatedly done that exercise.
Not really with much rigor. Where are their reproducible published papers and data sets? They made their name with a bit of luck on a fairly predictable election, but were unable to show a significant advantage in their methods across a number of elections.
> It is core to the concept of a probability that the result is going to go the opposite way from the prediction sometimes! It's meaningless to call it "wrong".
No no, that's not true. There are two different things here. Firstly, if you had a model and method of predicting elections that you applied to a sample of elections and showed that it had a good ability to correctly predict, then you can say your model is a good prediction across typical elections. The model getting one wrong does not make it a bad model over a set of elections. It absolutely is wrong for that particular election though. And secondly if you use a model to make a prediction about a particular election, when your prediction turns out to be wrong, it was not retroactively correct because it just followed the model and you claim the model is good. That's just not how statistics or predictions work.