ARG...... no longer argh ?

[Gustaf]

Silver's % to Win wasn't arbitrary.  It was based on a statistical operation.  It probably wasn't reflective of the actual chance to win, but that wasn't really the intention.

And, yeah, RCP's punting of R2K was lame.

Well, it was arbitrary in the sense that it weighed different factors according to his, somewhat arbitrary, judgement calls. If I'm thinking of the right site, which model I read about.

Can you give me an example?

He definitely did a few weights I disagree with (his undecided break-down and pollster quality weights were based on the primary -- yuck), but I still think his methodology is better than averaging the last three polls.

And R2K's arbitrary exclusion of Research2000 polls, no matter how many complaints they got, was not just disagreeable, but indefensible.  (not the biggest deal in the world, but still, wtf?)

Oh, I'm not saying the Atlas model is the best either. Far from it. But my basic issue with this stuff is that we want to know how people will vote. Polls ask EXACTLY that. Mixing polls with other stuff supposedly capturing voter intention is suspect in my opinion. Now, I haven't lived long, though I believe it's a little bit longer than you. But in that life-time my definite experience is that JJ's election rules are actually pretty good: factors not captured by polls seldom exist. I can sympathize with Silver's intentions but polls should be enough in theory and in practice it seems to me that they usually are. So I prefer a somewhat objective weighting together of polls. Of course, bias tends to enter into that as well.

Bottom line is that I like to use a rigid model which has not been subjected to any sort of bias or valuing from the constructor of a model (such as "pollster A is 1.345 times better than pollster B").

I don't have specific examples to offer you. But any kind of weighting would be somewhat arbitrary. If you take Factors A, B, C and say "A is 50%, B is 35% and C is 15%" that is a bit arbitrary. Reading through how he constructed his model and weighted my mental note was "interesting, but seems a bit arbitrary". This was like 6 months ago or something so I don't recall exactly what I was thinking about. I could look it up, but maybe you get my point anyway? Please note that I'm not specifically disagreeing with something, saying "A should be 42.5% instead" merely that I have no idea what weight A should have and I'm suspicious of assigning a weight to it.

I'm not sure whether this entire rant makes sense or anything...if it doesn't I will have to read the damn model again.

[Grad Students are the Worst]

I think you're misunderstanding what the regression model was for.  The regression model ended up worth less than one crappy poll in most states.  It wasn't for picking up trends the polls didn't pick up. It was for picking up the slack when a state wasn't being polled.  For instance, the assumption was the Dakotas -- being highly similar -- would move together.  So if SD didn't have a poll for a month, but ND had a few, SD would be adjusted to move along with ND.  In some cases, like Indiana, Silver's model was skeptical because the demographics suggested that it shouldn't be close.  But eventually the IN regression came into step, and even when it was out of step, it was the difference of a point and a half at most.  Why?  Because Indiana did have polls, and the model ceded to them.

Silver also provided a raw, trend-adjusted polling model.  That included only subsequent national poll movement (downweighted), pollster quality (see previous caveat), sample size.  True, how much sample size is weighed is arbitrary.  But we do that kind of things in our mind, and it always ends up more arbitrary that way.

The RCP average is really the same thing as the Atlas, a "dumb" average.

[Gustaf]

I think you're misunderstanding what the regression model was for.  The regression model ended up worth less than one crappy poll in most states.  It wasn't for picking up trends the polls didn't pick up. It was for picking up the slack when a state wasn't being polled.  For instance, the assumption was the Dakotas -- being highly similar -- would move together.  So if SD didn't have a poll for a month, but ND had a few, SD would be adjusted to move along with ND.  In some cases, like Indiana, Silver's model was skeptical because the demographics suggested that it shouldn't be close.  But eventually the IN regression came into step, and even when it was out of step, it was the difference of a point and a half at most.  Why?  Because Indiana did have polls, and the model ceded to them.

Silver also provided a raw, trend-adjusted polling model.  That included only subsequent national poll movement (downweighted), pollster quality (see previous caveat), sample size.  True, how much sample size is weighed is arbitrary.  But we do that kind of things in our mind, and it always ends up more arbitrary that way.

The RCP average is really the same thing as the Atlas, a "dumb" average.

Ah, it's all coming back to me now. Point accepted on non-poll factors. But I think was a bit sceptical of the weighting of the polls. I guess it was a general impression of a lot of weights and stuff that didn't seem solid enough. I like to keep as close to raw data as possible.

[Grad Students are the Worst]

I half-agree with you.  I could have lived without the pollster quality weights.  But I like the sample size weights, and the time weights.  Those are things that are hard to mentally quantify on-the-fly.  On those alone, I think it was superior to the RCP average.  Purity is not necessarily optimal.  Besides, both present the pure underlying data.  They just calculate it in different ways ("dumb" average vs. maybe-overcomplicated quantified analysis.)

[Gustaf]

I half-agree with you.  I could have lived without the pollster quality weights.  But I like the sample size weights, and the time weights.  Those are things that are hard to mentally quantify on-the-fly.  On those alone, I think it was superior to the RCP average.  Purity is not necessarily optimal.  Besides, both present the pure underlying data.  They just calculate it in different ways ("dumb" average vs. maybe-overcomplicated quantified analysis.)

It's a trade-off between reliability and relevance (yeah, I'm taking an accounting course, right now...bleh). Weighting should make it more relevant/accurate but it does force you to make various judgement calls on issues and thus help introduce bias. I guess the bottom line is, to what extent did 538 over-perform RCP?