You should now an additional graph along the right-hand side of the page, which I have dubbed the 'Swing State Analysis'. What this tells you is which states are likely to 'swing' the election. As I describe it here:
I'm now reporting another new parameter in my output, which is the state that "swings" the election in each of the simulation runs. The way that this works is as follows: I arrange the states from best to worst in order of Obama's (or Clinton's) vote share in each of the 5,000 simulations. I then count electoral votes upward until he equals or exceeds 269 EV. The state that puts him over the top is literally the swing state for that simulation run.This is one of those things sounds fancier than it really is, and produces what should be fairly intuitive results. Obviously, the key variables in this calculation are the number of electors in a state, and just how tight the state is expected to be. These figures might be thought of as a good proxy for how the campaigns should allocate their resources, among other things.
What's interesting is that we come up with relatively different lists in the Obama-McCain and Clinton-McCain scenarios. Pennsylvania, Ohio, and Michigan are important to both candidates to a lesser extent so is Missouri. But New Jersey and Virginia are featured prominently on Obama's list, while they don't make Clinton's Top 15 (New Jersey because she won't lose it; Virginia because she won't win it). Florida, meanwhile, ranks much lower on Obama's list than on Clinton's, because the model thinks that he has a lot of alternative paths to 269 electoral votes that should provide a better return on his investment.

26 comments
Poblano,
I love the site, and your statistical methods. Only could you maybe replace the electoral proportion maps with normal maps with the EV written in. The geography is a little confusing with the electoral proportion maps.
Student Guy
What exactly does the % beside each number on the "swing state analysis" mean?
Thanks!
Mark "Hillry-is-inevitable" Penn floated another dull argument today. Truth is, Obama does better than Clinton in Pennsylvania matchups against McCain because, while Philly Dems prefer Hillary, they would vote for Obama if he's the nominee.
But this is what the idiot said last month:
“Winning Democratic primaries is not a qualification or a sign of who can win the general election. If it were, every nominee would win because every nominee wins Democratic primaries.”
http://www.politico.com/news/stories/0208/8551.html
I guess the goal post has moved. But using this new STUPID argument suggests Hillary will lose the 30 States Obama has won and the ones he's still to win. How can Hillary win the general election?
Student Guy,
Yeah, I'm exploring a couple of alternatives for the cartograms. The thing is that I need something that can update automatically in EXCEL (basically the maps you see in the site now are just generated by some fancy conditional formatting), which limits the options a bit.
Faithfull,
The percentage next to the state is the percentage of the simulation runs in which that state swing the election from a win to a loss. There is exactly one swing state assigned for each simulation run.
I do need to figure out a slightly better way to explain exactly what it's doing, but in the meantime, you can see my discussion here.
"percentage of the simulation runs in which that state swing the election from a win to a loss."
All you need to do is find an economical way to put exactly this explanation into a legend or heading to your swing table. Even that statement works for me.
Perhaps:
"Figures represent the percent of the simulation runs in which a given state swings the electoral vote from a loss to a win." (or is it win to a loss?)
What's also confusing, BTW, is the colors. You might stick to the Dark blue, light blue colors for the two Dem candidates.
poblano,
Thank you for adding the geographical maps along with the electoral cartogram.
Student Guy
Poblano, or maybe the percentages are this?
"Percent of the simulations in which a minimum winning coalition of 270 electoral votes requires winning the given state."
That makes sense to me as a way of showing the odds that a given state will have to be won in order for the candidate to reach 270 electoral votes.
It's a way of showing, for example, how much less important winning FL is to an Obama EV win than to a Clinton EV win.
I thing it´s like this: He listed all states beginning with the state where the Democrat got the least percentage. Then he counted which state brought the Democrat over the magic number of 269.
For example:
Utah: 0
West Virginia: 0
Wyoming: 0
...
Ohio: 20
North Dakota: 23
...
New Hampshire: 107
New Jersey: 122
...
Delaware: 267
Hawaii: 271
...
Vermont: 319
Illinois: 340
Then he did that several thousand times and listed which state let the democrat win the election how often.
When I am right.
Might it not also be reasonable to have a "swing state" analysis for the simulated events in which McCain wins. McCain's "likely" swing state that could put him over the top will not necessarily be the same, will they?
For example, although the simulations tend to show Obama (or Clinton winning), there will be runs where McCain wins - in those circumstances what are the states that are most likely to put him over the top.
I suppose it is just as important for the candidate to win his likely swing states as it is for him win his opponents likely swing states, which are not going to be necessary the same thing. The comparison may be very instructive.
For example, for Obama VA and IN are up "relatively" high in the of potential swingers. But I really doubt that IN and VA come up high as possibly swingers necessary for McCain. Which means that McCain may likely not campaign hard there because for him there may not be a lot of incentive, but for Obama he will campaign a bit harder in IN (a neighbor) and VA because they are potential swingers. The inequality of incentives should create a tightening in some unlikely places.
I thought of another way to sort wheat from chafe.
Consider "pbar", the average % chance of being a swinger for all.
Then just look at those states with a p (% chance of being a swinger) that is greater than Upper Control Limit,i.e.
p-bar + 3 * square root of ((pbar)*(1-pbar)/N)
where N is 5000 because that is number of simulations.
This would show the honestly likely swingers, as opposed to the top 15 which is might be good for Letterman but really isn't statistically very meaningful.
What's the definition of the percentages in the new "Must-Win States" analysis?
From the FAQ:
"The notion of the 'Must-Win States' is somewhat more intuitive. Simply put, it the percentage of the time that the candidate who won the election won that state in our simulation runs."
Right now (I think because of the red) you have "must- win" states for McCain and "tipping point" states (aka "likely swing states") for Obama (I presume because of blue).
I think it might make more sense to have Must-Win States for each AND Tipping Point states for each.
Per prior post, I'd like to see the wheat separated from the chafe by seeing which "must-wins" or "tippers" if any are statistically meaningful, i.e. fall outside 3 SD +/- average % for all states. (Basically, a p-chart that Deming would have used).
The true battle ground states would seem to be where meaningfully likely Obama tippers coincide with meaningfully likely McCain must-wins OR where meaningfully likely McCain tippers coincide with meaningfully likely Obama must-wins.
Yeah I don't get the coloring on this analysis either. Why does Obama have tipping point states, and McCain have must-win states? If that isn't the case, then the coloring on the graph is definitely confusing. It seems like there should be four columns, not two.
In order to try to do the wheat from chaffe analysis, I previously suggested, I looked at your raw data from March, which is posted here:
http://www.dailykos.com/storyonly/2008/3/6/212016/8597/980/470910
Obviously, things have changed (noteably, IN going from 0.4% of being a tipper to 4.3% chance!?? How?) but as that is the only complete raw data to which I have access, I used it.
The average % tipper is 0.019568627
Using the following,
=(E52)+3*SQRT(E52*(1-E52)/5000)
where E52=AVERAGE(E1:E51)
So basically, the honest to goodness tippers with that data have a % above ~2.5%, yielding:
CO,OR, WI,MI,NV,OH,NJ,PA,VA,MO,NC, FL
Using the same March data, the average Obama win % was 0.501941176, from =AVERAGE(B1:B51).
So, the honest to goodness really truly likely Obama winners, using =(B52)+3*SQRT(B52*(1-B52)/5000)
suggests looking at those above about 52.3%. The really truly Obama losers would have a win% less than ~48%.
So if you exclude from the honestly likely tippers those which have a win% less than 48%, you'd exclude MO,NC,FL, as really unlikely tippers%. And by those March numbers, all of the rest but VA were "really truly likely" Obama winners. So by this kind of analysis of the March numbers, the really truly likely battle ground state was VA.
Of course this was only using the March data available.
To me this kind of analysis makes more sense that a list of the top ten tippers or must wins.
I think what Kromkowski and Cullen said about the confusion regarding colors etc. above is still valid. I'm not sure as well: why are there Obama Tipping Point States and McCain Must-Win-States displayed, but not vice versa?
I am posting here because not sure where else to post. Also not sure where Poblano is actually most likely to look but I continue:
Doesn't the Electoral Vote Distribution chart look "funky"?
I know that because we are adding up discrete states which are neither uniformly nor randomly distributed our histogram of simulation should not look "normal".
But that "red" tail near zero (presumably representing a McCain blowout a la Nixon vs. McGovern or Reagan v Mondale) really shouldn't be there while at the same time there is such a skew for Obama.
It might help, pedagogically, if we knew what the electoral vote distribution looked like if we assume that every state is a 50/50 toss up - i.e. what would the histogram look like? I suppose everybody is busy, but I just don't have the time to do it.
A 50/50 toss up is like saying we have no information. If in the circumstance of no information, the chances of Obama get routed (<20 EV) are q and the simulation's projected chances of Obama get routed (<20 EV)are statistically meaningfully higher than q, then something is either wrong with the simulator or we should stand up a take notice, of such a curious situation.
Eyeballing is a very very bad thing to do) but with 5000 simulations, that red tail just doesn't seem right.
"Make or Break State" Probability
The goal is to find an intuitive metric, that isolates out the Florida/Ohio/Michigans (big and competitive) but not the Californias (bigger but uncompetitive) or Nevadas (competitive but very small).
The effect a state can have on a selected election is broken into three:
* EV Margin outside range = P(No Effect)
* State causes candidate favored to win in that state to win national Election = P(Fav_Win)
* State causes candidate not favored to win in that state to win national Election = P(NotFav_win)
P(No Effect)+P(Fav_Win)+P(NotFav_Win)=1.0
I propose that the most interesting metric is P("Make or Break") for state = 2 * P(NotFav_Win). Times 2 since it both makes and breaks and gives interesting properties:
* In a fictional election, in which only one state is perfectly 50/50 competitive (and the other states are evenly split) P("Make or Break") will equal 1.0.
* If the state is 100% uncompetitive, or the national election is 100% uncompetitive, then P("Make or Break") equals zero.
* If you sum up the P("Make or Break") for all states you get a value equal or less than 1.0 (I believe).
Implementation:
- For each Monte Carlo run (out of 10000), if underdog win's state, and the state EV was within the margin of victory, increase P(NotFav_Win) for that state by 0.0001.
Please fix your typo!
"You should now ____ an additional graph along the right-hand side of the page"
情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣用品,情趣,情趣,情趣,情趣,情趣,情趣,情趣,情趣,情趣,情趣,情趣,情趣,情趣,情趣,情趣,情趣,情趣,情趣,情趣,情趣,
^^ nice blog!! ^@^
徵信, 徵信, 徵信, 徵信社, 徵信社, 徵信社, 感情挽回, 婚姻挽回, 挽回婚姻, 挽回感情, 徵信, 徵信社, 徵信, 徵信, 捉姦, 徵信公司, 通姦, 通姦罪, 抓姦, 抓猴, 捉猴, 捉姦, 監聽, 調查跟蹤, 反跟蹤, 外遇問題, 徵信, 捉姦, 女人徵信, 女子徵信, 外遇問題, 女子徵信, 徵信社, 外遇, 徵信公司, 徵信網, 外遇蒐證, 抓姦, 抓猴, 捉猴, 調查跟蹤, 反跟蹤, 感情挽回, 挽回感情, 婚姻挽回, 挽回婚姻, 外遇沖開, 抓姦, 女子徵信, 外遇蒐證, 外遇, 通姦, 通姦罪, 贍養費, 徵信, 徵信社, 抓姦, 徵信社, 徵信, 徵信公司, 徵信社, 徵信, 徵信公司, 徵信社, 徵信公司, 女人徵信, 外遇
徵信, 徵信網, 徵信社, 徵信網, 外遇, 徵信, 徵信社, 抓姦, 徵信, 女人徵信, 徵信社, 女人徵信社, 外遇, 抓姦, 徵信公司, 徵信社, 徵信社, 徵信社, 徵信社, 徵信社, 徵信社, 女人徵信社, 徵信社, 徵信, 徵信社, 徵信, 女子徵信社, 女子徵信社, 女子徵信社, 女子徵信社, 徵信, 徵信社, 徵信, 徵信社, 徵信, 徵信社, 徵信, 徵信社, 徵信, 徵信社, 徵信, 徵信社, 徵信, 徵信社,
^^ nice blog!! thanks a lot! ^^
徵信, 徵信社,徵信, 徵信社, 徵信, 徵信社, 徵信, 徵信社, 徵信, 徵信社, 徵信, 徵信社, 徵信, 徵信社, 徵信, 徵信社, 徵信, 徵信社, 徵信, 徵信社, 徵信, 徵信社, 徵信, 徵信社, 徵信, 徵信社, 徵信, 徵信社, 徵信, 徵信社, 徵信, 徵信社, 徵信, 徵信社, 外遇, 抓姦, 離婚, 外遇,離婚,
徵信, 外遇, 離婚, 徵信社, 徵信, 外遇, 抓姦, 徵信社, 徵信, 徵信社, 徵信, 外遇, 徵信社, 徵信, 外遇, 抓姦, 徵信社, 征信, 征信, 徵信, 徵信社, 徵信, 徵信社, 征信, 徵信, 徵信社, 徵信, 徵信社, 徵信, 徵信社, 徵信, 徵信社, 徵信社, 徵信社, 徵信, 外遇, 抓姦, 徵信, 徵信社, 徵信, 徵信社,
當舖或專利或商標或存證信函都歡迎討論。專利或商標或存證信函或當舖都很重要。
關鍵字:當舖,當舖,專利,專利,商標,商標,存證信函,存證信函,商標設計,自創品牌,關鍵字。
艾葳酒店經紀公司提供專業的酒店經紀, 飯局小姐,領檯人員,領台,傳播妹,或者想要到台北酒店、林森北路酒店,私人招待所,或者八大行業當酒店PT,酒店公關,酒店兼職,想去酒店上班, 日式酒店,制服酒店,ktv酒店,禮服店,整天穿得水水漂漂的禮服酒店,鋼琴酒吧當酒店領檯,酒店小姐,公關小姐??,還是想去制服店當上班小姐,水水們如果想要擁有打工工作、晚上兼差工作、兼差打工、假日兼職、兼職工作、學生兼差、兼差、打工兼差、日領工作、晚上兼差工作、酒店工作、酒店上班、酒店打工、兼職、兼差、兼差工作、酒店上班等,想了解酒店相關工作和特種行業內容,想找打工、假日兼職、兼差打工、或晚班兼職想擁有快速賺錢又有保障的工作嗎???又可以現領請找專業又有保障的艾葳酒店經紀公司!
艾葳酒店經紀是合法的公司工作環境高雅時尚,無業績壓力,無脫秀無喝酒壓力,高層次會員制客源,工作輕鬆,可日領、現領。
一般的酒店經紀只會在水水們第一次上班和領薪水時出現而已,對水水們的上班安全一點保障都沒有!艾葳酒店經紀公司的水水們上班時全程媽咪作陪,不需擔心!只提供最優質的酒店打工,酒店上班,酒店打工環境、上班條件給水水們。心動嗎!? 趕快來填寫你的酒店上班履歷表
水水們妳有缺現金、有卡債、缺錢卡奴的煩腦嗎?想到日本留學日本打工嗎?妳是工讀生找工作??想要擁有高時薪又輕鬆的夜間兼職工作,打工機會和,假日打工,兼職工作日領假日打工的機會嗎??想實現夢想卻又缺錢沒錢嗎!??整天還在煩腦如何賺錢有什麼賺錢方法,和賺錢最快方法!?,想要打工,日領工作,短期打工,兼差工作,打工兼差工作嗎!?,
請加入我們艾葳酒店經紀公司工作單純輕鬆”高時薪”又可日領徵想要當傳播妹,上班小姐,酒店兼差,酒店兼職,歡迎學生打工,!!!
加入我們實現夢想就從現在開始^__^
酒店經紀人,
菲梵酒店經紀,
酒店經紀,
禮服酒店上班,
酒店小姐兼職,
便服酒店經紀,
酒店打工經紀,
制服酒店工作,
專業酒店經紀,
合法酒店經紀,
酒店暑假打工,
酒店寒假打工,
酒店經紀人,
菲梵酒店經紀,
酒店經紀,
禮服酒店上班,
酒店經紀人,
菲梵酒店經紀,
酒店經紀,
禮服酒店上班,
酒店小姐兼職,
便服酒店工作,
酒店打工經紀,
制服酒店經紀,
專業酒店經紀,
合法酒店經紀,
酒店暑假打工,
酒店寒假打工,
酒店經紀人,
菲梵酒店經紀,
酒店經紀,
禮服酒店上班,
酒店小姐兼職,
便服酒店工作,
酒店打工經紀,
制服酒店經紀,
酒店經紀,
菲
梵,
Post a Comment