SCOTUS Nomination Timing

After yesterday’s news about the death of Antonin Scalia’s death, the conversation almost immediately turned to whether or not President Obama should or would nominate a new candidate.  There’s obviously a lot being said about this right now by better legal and political minds than mine, but I did start wondering what kind of timing there normally was between Supreme Court nominations and Presidential Elections.  Thanks to Wikipedia, I was able to find a list of all 160 Supreme Court nominations that have occurred since 1789. I combined this with a list of election dates, and calculated the difference between the day the person was submitted to the Senate and the next presidential election.  I graphed days vs election year, and color coded the dots with the outcome of the nomination.

A few notes:

  1. I didn’t fully vet the Wikipedia data. If there’s an error in that data, it’s in this chart.
  2. All day calculations for years prior to the 1848 election are approximate. Prior to that, states had a 34 day window prior to the first Wednesday in December to hold their election. I gave them a default date of November 3rd for their year, which could be off in some cases.
  3. There were a few cases in which presidents attempted to nominate someone after the election but before the next inauguration. If they got re-elected, I counted that nomination from the election that would take place 4 years later. If they were leaving office, I gave them a negative number.
  4. 310 days is approximately the number of days between January 1st of a year and the general election, so I put a reference line there.
  5. These nominations include Chief Justice nominations….and those nominees may have been active justices when they were nominated.

With that out of the way, here you go:

Days to election

Rutheford B Hayes sets the record for getting things in under the wire, as he nominated William Burnham Woods in late December of 1880. He actually also nominated Stanley Matthews in January of that year, but it didn’t go to a vote. Matthews was renominated and confirmed a few months later by Garfield.

Overall only about 15% of nominations ever have come in this close to the election, and the success rate of those nominations is a little less than half. To compare, those nominees submitted before January 1st of the election year have about an 80% all time success rate. Obviously we haven’t even dealt with this in a while, but it’s interesting to see that historically this was more common than in recent years.

This could get interesting kids!

Guns and Graphs Part 2

In the comment section on my last post about guns and graphs there was some interesting discussion about some of the data.  SJ had some good data to toss in, and DH made a suggestion that a graph of gun murders vs non-gun murders might be interesting.  I thought that sounded pretty interesting as well, so I gave it a whirl:

Gun graph 4

Apologies that not every state abbreviation is clear, but at least you get the outliers. Please note that the axes are different ranges (it was not possible to read if I made them the same) so Nevada is really just a 50/50 split, whereas Louisiana is actually pretty lopsided in favor of guns.  That being said, the correlation here is running at about .6, so it seems fair to say that states that have more gun homicides have more homicides in general. Now to be fair, this chart may underestimate non-gun murders, as those are likely a little harder to count than gun related murders. I don’t have hard data on it, but I’m somewhat inclined to believe that a shooting is easier to classify then a fall off a tall building.  Anyway, I pulled the source data from here.

While I was looking at that data, I thought it would be interesting to see if the percent of the population that owned guns was correlated with the number of gun murders:
Gun graph 5

Aaaaaaaaand…there’s no real correlation there. It’s interesting to note that Hawaii and Wyoming are dramatically different in ownership percentage, but not gun homicide rate. Louisiana and Vermont OTOH, have nearly identical ownership rates and completely different gun homicide rates.

Then, just for giggles I decided to go back to the original gun law ranking I was using, and see if gun ownership percentage followed that trend:

Gun graph 6

There does appear to be a trend there, but as the Assistant Village Idiot pointed out after the last post, it could simply be that places with lower gun ownership have an easier time passing these laws.


No One Asked Me: Yesterday’s Weather

“I always dress for yesterday’s weather.”
-my brother

Okay so that’s not really a question.  In my defense though, my brother’s got a philosophy degree, which means most of what he says is an attempt to provoke a reaction, make a grand statement about life, explain his more questionable dating choices, or to get more attention, though not necessarily in that order.  Anyway, he posed this statement to me recently, then arched his eyebrow.  It’s possible I was supposed to take that as an opportunity to extrapolate some deeper meaning about his relationship with his ex-girlfriend, but instead I got curious.  If you really did always dress for yesterday’s weather, how often would this be okay?

It turns out this is one of those interesting stats questions that you can sort of come up with an answer for, but you have to make all sorts of assumptions to get there.   I did some poking around, and here are the parameters I figured I’d have to work with:

  1. You are perfectly rational.  Now this may not be a great assumption1, but it’s one we have to go with if we hope to get anywhere.  The problem with this is that people, especially those of us in northern climates, tend to start rebelling against winter every year. It’s a pretty well documented phenomena that some time around March/April people in northern climates just say “screw it” to the coat/gloves/scarf thing.  I don’t totally know how to take this in to account, but it’s something to keep in mind.
  2. You are like me.  It appears at least some types of cold/heat perception are pretty heritable, so when in doubt I assumed you’d act exactly like I do.  Hey, it worked in middle school.
  3. You modify clothes approximately every ten degrees (Fahrenheit).  This one was actually remarkably hard to find data about.  The problem is that apparently our bodies make lousy thermometers, and we have a remarkable spread of preferences.  The most consistent breakdowns I could find were actually on running or other outdoor sport sites, and they seem to support my “ever 10 degrees” hypothesis.  Apparently that’s where you can measure an impact on performance.
  4. You live in Boston. Yeah, you don’t.  Never have actually.  But I do, and the data’s actually stored for a while.
  5. Being stuck in the rain without an umbrella will bug you, but having an umbrella you don’t need won’t.  Umbrellas are like towels.  Always good to carry one.
  6. You don’t use an umbrella or other rain gear if it’s snowing.  Because snow’s not mean like that and you already have a jacket on and you’d look silly, that’s why.


Alright, with those out of the way, lets talk data.  I found a handy site called Weather Underground that actually keeps detailed archives of the weather.  From there I pulled all the data for Boston from Jan 1st, 2010 to June 22nd, 20152.  After that I measured a few things:

  1. How often the daily high temperature changed from one day to the next by more than 10 degrees in either direction
  2. How often the average daily temperature changed from one day to the next by more than 10 degrees in either direction
  3. How often a clear day was followed by a rainy day.

Basically if any of those three changes occurred, I assumed that you ended up dressed incorrectly.  It’s not perfect…the rain could have happened overnight for example, but it’ll get us in the ballpark.  I knocked off a few values because of fluctuations that fell in to either of the extremes (ie under 25 degrees or over 80 degrees).  Essentially if the day before was 85 and the next day was 96, I assumed you still dressed the same way.   At that point we normally resort to things like swimming or staying inside as opposed to clothing changes.  I did not account for changes in the daily low, as those usually happen at night, and the average picks up those changes. Based on all of this you ended up about 65.5% accurate.  Not bad!

Okay, so what went wrong on the other days?  Well, of the days you got wrong, here’s what tripped you up:

Temperature Changed: 49%

It Rained: 38%3

It Rained AND the Temperature Changed: 13%

Cool!   Now what if we wanted to know your luckiest month?  Well I have that too!

Month % of days you are properly dressed
August 75%
February 71%
July 70%
September 70%
October 68%
January 66%
November 66%
December 65%
June 63%
April 60%
May 60%
March 59%

So you’re actually headed in to a pretty good stretch here!  July’s almost here and August is really your month. At the very least you have some time to kill before March.  Use it wisely, and feel free to put this data on your LinkedIn/Facebook/ profile.  It’s sure to impress.

You’re welcome.


1. At least that’s what mom said when I mentioned it to her.
2. Hey, happy birthday!
3. Interestingly, that means if you took my advice and always carried an umbrella, your accuracy would go up to almost 78%.  Things to consider.