Objective: The purpose of this study is to determine if the cranial sagittal vertical axis (Cr-SVA) measured in full spine standing radiographs is a better predictor of clinical results than the C7 sagittal vertical axis (C7-SVA) in adult patients operated on spinal deformity with a. If you have any suggestions for additional data I should incorporate don’t hesitate to send them my way ( on the PI boards, or here). Study design: This is nonconcurrent prospective study approved by the Institutional Research Ethics Committee. And did you know PI made a market for NY-09 for some reason? There’s plenty more little nuggets in there (if nothing else, I found a few potentially mispriced markets along the way). Not sure what to make of these WA-08, WI-01, and WV-03 forecasts. How can TheCrosstab’s model favor Republicans at >99% in OH-12? How can DDHQ/0ptimus’s model think Democrats are 60-40 favorites in PA-14? While there are some out-there predictions all over, a few stick out in these states. Will be interesting to see (now that primaries are over) how new polling starts to tease this apart. (The rest of Florida is estimated with relative consensus).Ĭonsensus and disagreement both indicate that these races are tight. While I haven’t systematically measured divergence (not sure that it would be very informative), by eye this is the race the modelers and experts disagree about the most. Is Curbelo in trouble or winning in FL-26? PredictIt thinks it slightly favors the Democrats. This open race for Ed Royce’s seat is rated a toss-up by the experts, a lean D to likely D by DDHQ and The Crosstab, an a Lean R by 538. Below I’ve highlighted a few examples of interesting divergences. So they should be expected to produce different results from each other, especially at the individual House district level. But in the interim, you can feel free to use the spreadsheet as a springboard to visit those sites whenever you feel to see the most recent version of their model’s output.Įach modeler and forecaster works with different assumptions, algorithms, and even data. While I’ll work on automating that, for now expect only a weekly update. I plan to update this spreadsheet once a week – partly because it’s currently very time-consuming to get data for DDHQ and 538 for individual races. Governors – forecasts for each governor’s office race.Senate – forecasts for each Senate race.House – forecasts for each individual House race (only “competitive” races are shown).Major markets – the high volume markets that cover the overall outcome of the 2018 midterms.There are four sheets in the spreadsheet: In this way you can see where there’s consensus and disagreement among modelers or between modelers and the market. I’ve included data from several other modelers/forecasters for you to gauge what the chances are in a given race (whether or not we have a market). To help you find the markets you’re interested in, I’ve organized everything relevant to the midterm elections into a single spreadsheet:įind markets and compare their prices to what other forecasters and models say The simplest crosstab is 2x2 table The same rules for categorization apply. PredictIt’s interface isn’t always ideal and many markets can slip through the cracks. Predictive Validity Test scores are used to predict some future outcome. Keeping track of all the markets in play this November is challenging.
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