What is the Fiscal Cliff?

Do Consumers Have A Problem With Black Friday Creep?

Before I begin my post today I’d like to wish everyone a happy Thanksgiving weekend, and safe travels to anyone visiting family or friends today to celebrate the holiday.

While today is a day for being thankful, later tonight, after the festivities, thousands of Americans will venture out later tonight for the start of the holiday shopping season. Many stores are opening and holding sales as early as 8:00 PM this year, and are being blasted for the controversial decision.

But exactly how much of a problem does America have with the theory of “Black Friday Creep”? Many people say that this year the retailers have gone too far- but will that stop consumers from going out and getting in line early? Will we see yet another record-breaking sales period? It’s time to see just exactly how hypocritical we are.

__________________________________________________________________________________________________

If there’s one thing we can be certain about, it’s that retailers are opening earlier:

I picked seven major retail chains, most of which specialize in different or all genres of retail. None of them opened before 5:00 in 2008. All of them are opening at or before midnight this year. Even K-Mart and Sears, which had both consistently been among the later openers, are welcoming customers at 10 PM this Thanksgiving.

Opening stores earlier means that there is a greater range of time for people to show up, meaning more customers and more profit. Whether it is because stores are opening earlier or because Black Friday is simply becoming more popular, more customers are showing up, stores are making more money, and- if only slightly- individual customers are spending more money. 

Now for a brief interruption about the Internet: Online spending has been up recently too. Thanksgiving Day spending online was up 18% last year, and Black Friday spending was up 26%. All told, Americans spent $1.295 billion online over those two days last year.

Getting back to in-person shopping, let’s also look at the percent change in total amount spent:

2009 was a down year by all accounts. Individual customers spent the least in that year, and the total amount spent was just barely more than the last year. You could conclude that the recession meant people stopped showing up, but the increase in customers was pretty much the same as between 2007 and 2008. Customers still spent nearly $350 individually on Black Friday in 2009, suggesting that while a recession does have a small impact on most Americans, it’s not enough to stop them from showing up and spending money on Black Friday. And this makes sense- stores usually hold some of their best sales of the year the day after Thanksgiving, making it one of the best days to go bargain hunting on.

Since 2009, however, people are steadily spending more and more.

Now here’s the bad news for retailers:

This graph essentially represents the derivative of the customer graph (for calculus fans out there)- it’s the percent increase in customers over the previous year. While more customers show up each year, the percentage of those that are new customers is slowly but steadily going down. This suggests that at some point- probably after the next few years- Black Friday could top out in terms of customers.

However, I use the phrase “bad news” in a relative sense. When Black Friday finally reaches its customer limit, a good 250-300 million people will be coming out for the sales, each spending an average of about $400. Not bad for retailers at all.

Just for fun, I’ll make some predictions on the type of turnout we could expect for this Black Friday. About 250 million customers (the data suggests 249.8) will show up, spending on average about $393.95. This is actually about five dollars less than the average consumer spent in 2011. However, the spent overall will still increase- a grand total of $56.1 billion*

Let’s get back to issue I talked about at the beginning of the post: the slow creep of stores opening earlier and earlier on Black Friday- and Thanksgiving. Americans say they have a problem with it. Do they?

Not at all. The percentage of total customers on Black Friday that show up at midnight is increasing at an incredible rate. One-quarter of Black Friday shoppers were out at midnight in 2011, a jump of over 600% from 2009. If the trend holds true, we could see nearly half of all consumers out at midnight- or certainly before.

Many Americans are complaining about the Black Friday Creep, but the statistics show that we secretly embrace it. Something needs to drastically change in the average American’s mindset before retail chains put an end to the creep. Until that happens- if it happens- store owners will continue to open earlier and earlier, more and more customers will turn out, and more and more money will be exchanged in the day after- and on- Thanksgiving.

*Why don’t the numbers add up? Most of my data was from the National Retail Federation. I’m guessing they took a survey of Black Friday shoppers and asked them how much they spent for the average individual amount. The number of unique customers is also probably lower than the total here, because many people shop at more than one place on Black Friday.

Who Will Win the Election? (and other political trends, infographic style)

Can We Find A Trend In The Fungal Meningitis Outbreak?

Epidemiologists have some of the most intriguing jobs in the world. No other branch of science can make an immediate impact akin to that of the study of diseases. The fruit of diligent research can be life- no, world-changing. And when an epidemic strikes, their fast actions can save thousands of lives.

Kudos to the epidemiologists on the case of the recent outbreak of fungal meningitis. I heard about the outbreak, and then one day later they had already located the cause and were doing work to minimize the damage done by the tainted steroids. Unfortunately, they can’t save every life, and nearly 300 cases have been reported with a fatality rate of about 8%. However, their quick work surely saved many more lives.

For my latest post, I’ve decided to play epidemiologist to try an isolate a trend among the data for the meningitis outbreak. Is there a reason that certain states have been hit harder than others? (Besides, of course, the states that haven’t received the infected drugs, and obvious comparisons like population)

_______________________________________________________________________________________________

The CDC website doesn’t have information on former outbreaks of fungal meningitis (or if they do, they’re hiding it very well), and this made my first idea- does this outbreak parallel previous outbreaks?- very short-lived. However, the CDC did have interesting statistics on another topic, which redirected my plan for this post.

The fungal meningitis outbreak has apparently been classified as a “Healthcare-Associated Infection” or “HAI”. The CDC tracks HAIs in a variety of ways; one of them is an SIR value: Standardized Infection Ratio. This value is found by taking the number of actual HAIs and dividing it by a predicted number of HAIs. Lower numbers are better, and values under 0.5 are very good. Similarly, values over 1.0 are very bad- this means that the included facilities are actually causing more infections than they’re projected to.

Here is a chart of the SIR values (in 2010, the most recent I could find) for all of the states where the tainted steroids have been sent:

 These values are mostly good; Indiana is the only state with a value above 1, and Michigan and West Virginia both have values under 0.5 (Remember that!)

You may have noticed that three states- Idaho, Minnesota, and Rhode Island- don’t have SIR values on the graph. This is because SIR values are independently submitted by health care centers, and some states don’t have enough centers submitting information to the CDC for effective calculation of SIR. These three states are some examples- less than five centers submitted information over 2010, whereas most states have several dozen.

Since the outbreak is a HAI, it would be reasonable to assume that most of the infections occurred in states with high SIR values. But that isn’t the case:

STATE	CASES
ILLINOIS	1
NEW YORK	1
IDAHO	1
PENNSYLVANIA	1
TEXAS	1
NORTH CAROLINA	2
MINNESOTA	7
FLORIDA	17
MARYLAND	16
NEW JERSEY	16
OHIO	11
NEW HAMPSHIRE	10
INDIANA	38
MICHIGAN	53
VIRGINIA	41
TENNESSEE	69
CALIFORNIA	0
CONNECTICUT	0
GEORGIA	0
NEVADA	0
RHODE ISLAND	0
SOUTH CAROLINA	0
WEST VIRGINIA	0
TOTAL	285

Indiana, which had the highest SIR value, has quite a few cases compared to other states. Michigan, however, had the lowest SIR value- and has more cases than any state except Tennessee. But West Virginia, which had the second-lowest SIR value, has zero cases.

Why is there variability in the data? One reason is because not every state received the same amount of the infected drug. Only one facility in West Virginia received the drug, compared to six in Indiana. Based on the data, each facility that received a shipment of the steroid had about 3.8 infections. From this average, we can predict how many cases will occur in each state:

This isn’t very good. We can take it our prediction one step further by applying a state’s SIR to the predicted number of cases (for example, Illinois: 11 predicted cases x 0.678= 8 predicted cases with SIR):

 

There’s still no strong correlation here between SIR and the number of cases. We can calculate our own SIR values for these states using our predicted number of cases and the number of actual cases. Unfortunately, when we do this, only four states- the ones in blue on the data table and the following graph- have SIR values that fall within the standard range of scores (that is, the range of scores for all 50 states.):

M-SIR represents my own calculated SIR value based solely on the meningitis statistics

Quite simply, there’s no correlation between HAI SIR values and the recent fungal meningitis outbreak. The only explanation I can come up with for this is that most facilities used the tainted steroid believing it to be safe, whereas with most HAIs the healthcare center should know how to avoid the problem.

My data:

https://docs.google.com/spreadsheet/ccc?key=0AnZrkjWWJajQdDVETDlPQ0dTcUFXVU1TXzcxS2h1TFE

Note: All data as of October 21, 2012

How Successful Will The iPhone 5 Be?

When it comes to technology- and specifically, new products- perhaps nothing is more anticipated than the iPhone. When the original iPhone was released in 2007, it was the beginning of the Age of the Smartphones. Since then, Apple has stayed on the cutting-edge when it comes to their iPhones, and excitement and anticipation over technological leaps and bounds precede each release.

Last weekend, Apple released their new iPhone, the iPhone 5. Apple had had nearly a year since its most recent phone, the iPhone 4S, to work on improvements. The major selling points Apple hit on in its press release were the physical features, hailing the new product as the “Thinnest, Lightest iPhone Ever”. For this post, I thought I’d take a look at that claim, and examine the evolution of the iPhone. Is it really the thinnest, lightest iPhone ever? Is there anything that prior incarnations of the smartphone did better? Just how much money is Apple making on the iPhone, anyway?

_________________________________________________________________________________________________

Apple has released a new iPhone fairly regularly- about once a year. I won’t be discussing the technological advancements of the iPhone in this post because it’s clear the iPhone has progressed substantially in terms of technology (Siri, anyone? It’s something Sci-Fi authors could only dream about even recently). Instead, we’ll be taking a look at the physical aspects of each phone.

The following graph represents the change in width for the iPhone over time:

As you can see, there’s clearly no change between this generation and the previous two. However, the other dimensions of the iPhone 5- height, depth, and weight- have changed somewhat. Here are those graphs:

The iPhone 5 is significantly larger in terms of height over its previous incarnations. However, the change shouldn’t be terribly noticeable for anyone using the phone: Only about eight millimeters, or a little more than the length of a red ant. It also results in a potential increase in screen size, though again, not terribly noticeable.

The iPhone 5 is also easily the thinnest iPhone yet, with a depth of only 7.6 mm. But is it really that significant of a change? The new phone has only shed 1.7 mm. Remember that ant from earlier? 1.7 mm is about the length of its head, maybe a little smaller. The change probably won’t make any significant difference in the future.

The weight of the new iPhone is something Apple is significantly proud about. They claim to have eliminated 20% of the weight of the iPhone 4S (and they have, actually), but since the iPhone 4S was so light in the first place (140 grams), is it really such a big deal? Let’s examine: the iPhone 5 is 28 grams lighter, so imagine three pencils, or five quarters. I suppose this could make up a fairly noticeable change- I’ve never actually held and compared the two, so I’m just making an estimation. But again, the iPhone 4S was already very light, so Apple isn’t actually saving the backs of millions of their customers (thank you, the Onion).

I also measured some characteristics of the iPhone that aren’t exactly physical: memory and battery life. We can easily see the change in memory over the generations simply by looking at what was sold- the original iPhone was sold in 4, 8, and 16 GB versions, and while every iPhone generation has had a 16 GB version, it’s the smallest memory option for the iPhone 4S and 5, which have 32 GB and 64 GB variants.

Battery life is more interesting than storage memory. Audio and Video battery life has gradually increased over the years (40 hours of audio since the iPhone 4, and 10 hours of video since the iPhone 3G), but standby life has a different trend. The iPhones 3G, 3GS, and 4 all had the most standby life, at 300 hours. The iPhone 5 has three days less life than that. Apple can still claim that it has increased the battery power, however, since the iPhone 4S only had 200 hours of battery life.

 _____________________________________________________________________________________

Has Apple been doing enough to attract new customers and retain their old ones? How have their iPhone sales done over the years? The answer may surprise you:

Black plots in the above graphs represent the first quarter of Apple’s fiscal year. Their first quarter falls over the holiday season, and so yields significantly higher sales and revenue.

The iPhone has been a complete and utter success for Apple. Sales per quarter after the release of each phone have, at the very least, doubled. In the quarter after Apple released the iPhone 3G, it saw an 861% increase in units sold. Yes, you read that number right: 861%. While each iPhone slowly trends downward after release (I estimated a 25% loss each quarter when a newer model was on the market), Apple still pulls in billions of dollars each month, and could hit $100 billion of revenue for this fiscal year.

Even more promising for Apple is the first-weekend sales of its new iPhone 5. Five million phones were sold, bringing in about $1.5 billion- more revenue than the original iPhone made in its entire run, and nearly as many units sold.

Here is my data: https://docs.google.com/spreadsheet/ccc?key=0AnZrkjWWJajQdFNLdmtnYUxOR1ZlY2lfVHR1ZlpGZ0E

Bounds were determined in several ways, but should be viewed as the maximum and minimum possible totals for each phone. We know that no phone has sold $0, so we have to estimate the lower bound. The estimated exact total is based on the 25% decay rate mentioned above and, barring the discovery of the actual data, is a good ballpark figure for each phone’s sales totals.

How Long Will The Syrian Civil War Last?

I didn’t expect the bombing of U.S. embassies in North Africa and the Middle East when I began to research this post, but the current state of affairs in that region- unfortunately- fits very well this topic.

It’s been nearly two years now since the start of the Arab Spring. Since then, we’ve seen many changes in North Africa and the Middle East. As the protests grew, they captured international attention. First, Tunisia’s government toppled. Sparked by their success, perhaps, other protests broke out across the region. Egyptians overthrew their own government less than a month later, and then Yemen followed shortly afterwards. In Libya, a civil war broke out, and the rebel forces were successful by the end of August. Many countries have had governmental changes; many others are still seeing ongoing protests.

The bombing in Libya, and subsequent anti-U.S. attacks and protests, prove that the Middle East and North Africa aren’t as stable as we’d hoped they’d be. We should have expected this, though. After facing civil wars and major governmental changes, it would be surprising if a country wasn’t in turmoil. And turmoil is exactly what we have. It’s sadly commonplace for a country to fall back into a state of civil war and disorganization directly following a revolution.

How long will it take for things to stabilize in the region? That’s tough to answer. It’s not like a civil war, which can end overnight. We won’t wake up one morning to find that everything’s better. It will be a long, slow process that will likely take years to finish.

Let’s turn our attention towards the bloodiest state right now, the one still in the midst of a gruesome civil war: Syria. This war has been ongoing for far longer than any of the other armed conflicts associated with the Arab Spring, and shows no signs of stopping. How long will it be until the war ends, and how will it end? That’s the question we attempt to answer today.

 _________________________________________________________________________________________________

I compiled data on 45 different civil wars besides the Syrian war. 43 took place since 1930 (20 since the year 2000- in green), and the other two were the United States Civil and Revolutionary War, which will be used only as standards for comparison. 9 wars took place in the Middle East (in tan on the data table), 22 in Africa (yellow), and the rest took place elsewhere in the world, mainly East Asia and Central and South America. 37 lasted longer than a year (in blue). 

I found data for each war on the death toll, the total population of the country (at the midpoint of the war), the percentage of the population killed, and whether or not the group that was rebelling was successful.

Below is a scatter plot of the death toll of each war vs. when it started. Ongoing (most at low-level) wars are in red in each scatter plot:

There’s no correlation between when a war began and its total death toll. This means that we haven’t been killing more people with new, advanced weaponry, but it also means the number of people dying in these wars is going down.

Additionally, one of the more disturbing trends we’ve seen recently regards the number of civilians killed in times of war. It’s very difficult to find statistics on this matter, especially since most civil wars take place in under-developed countries. However, in many civil wars, a vast majority of the people killed aren’t the soldiers and combatants on either side- they’re innocent civilians, killed by bombs or other atrocities. From the limited data I found on this, I’d estimate maybe 90% of people killed in the average civil war for the past thirty or so years were civilians.

The next chart is a plot of the percentage of the population killed vs. the starting year of the war:

 

Again, there’s no correlation between the two.

Finally, here’s a chart of the results of each civil war. Five are ongoing (four at a low-level), and two are listed as “N/A” because those wars were a result of a power struggle shortly after a revolution:

Most rebels are actually very successful in civil wars. About 1/3 of the civil wars I looked at ended with rebel victories. Another 13 ended in tentative peace agreements, which usually grants some demands of the rebels. Only seven of the 43 wars I analyzed ended with rebel defeats.

Why is this? Simple: The rebels are fighting for much more than the incumbent government. They want change, and will go to greater lengths to get that change. We can see a smaller version of this right here in America with- believe it or not- online and telephone surveys. The results of those polls are often skewed towards change because the people that want change are feel strongest about the issue are more likely to respond to the survey.

All things considered, the average death toll for the civil wars I examined was 266,940, and the average percentage of the population killed was 2.37%. 

In the Middle East alone, the percentage of population killed was very similar: 2.38%.  Taking into account African countries yields a higher percentage: 2.71%. For Syria, this percentage means 535,000-610,000 people would die in the fighting- twenty times the current amount. The 267,000 figure is much more plausible.

The length of the conflict is difficult to predict. The average length from the data was 9.79 years; not counting ongoing conflicts, it was 7.74 years. The figure for the Middle East alone was 7.87 years.  The first number actually makes sense; at the current rate, the death toll would reach 267,000 in a little under nine years.

Let’s compare this to the U.S. Civil and Revolutionary Wars. The Civil War lasted just over four years, yet took the lives of 625,000 people, the vast majority of them soldiers. The Revolutionary War, on the other hand, lasted over eight years- yet only 50,000 Americans were killed, with about 35,000 Europeans. Warfare has certainly changed over the years- and not necessarily for the better. The change from conventional battlegrounds has led to the deaths of many innocent people.

Now, to conclude. Based on the data, I expect the civil war in Syria will last about six or seven more years, and will cause the death of a little over 200,000 more people- most of them civilians. I sincerely hope that this prediction is wrong and the war ends quickly- but that’s not what the statistics suggest will happen. History does say, however, that the Syrian rebels stand a good chance of winning.

My data can be found here: https://docs.google.com/spreadsheet/ccc?key=0AnZrkjWWJajQdEt1LWlrWFFxaVZ5c2hFbUdHb2dpdEE

When Will The Next Big Hurricane Hit The U.S.?

I remember the coverage of Hurricane Katrina very clearly. I woke up and immediately went to watch the news, and Katrina was just coming in. We knew it would cause a lot of damage, but I don’t think anyone knew that it would cause quite as much as it did. In the weeks afterward, the costliest natural disaster in American history dominated the headlines.

I have a special reminder of Katrina every year: The hurricane made landfall in New Orleans on my birthday. This connection makes me think of the power of hurricanes annually- and they always fascinate me.

We like to think that we’re above the power of nature. We’ve climbed to the top of the world, and explored the depths of the sea. We’ve built buildings that are ridiculously high- it’s almost like we’re mocking nature, pretending to be invulnerable. And then, something like Katrina happens, be it a hurricane, earthquake, tsunami, anything; and it sends us crashing- sometimes literally- back to earth.

This year, I had another reason to think of hurricanes. On my birthday this year, another hurricane- Hurricane Isaac- made landfall in New Orleans. Thankfully, the damage caused by Isaac was nowhere close to the damage caused by Katrina; however, it was a reminder: at any time, there could be another hurricane just as strong as Katrina. So is there a way to predict when the next “big one” will hit?

____________________________________________________________________________________________________

The attribute that makes natural disasters so powerful is their unpredictability. Volcanoes may give a few weeks’ at max. Hurricanes form and make landfall within a couple of days. We’ve vastly improved our tornado warning system; now we have nearly fifteen minutes of notice! And earthquakes- well, good luck. The 2005 hurricane season was especially unpredictable- we’ll get to that in a little bit. But the variability of hurricanes is what makes predicting the next powerful one especially difficult.

I could use all Atlantic hurricanes as my data set for this project, but I have neither the time nor the patience for that. Instead, I’m looking at only hurricanes who have been powerful enough to have their names retired:

There have been 77 retired names since 1954. One, Gracie (1959), is listed as retired by some sources but not others. Another, Allison (2001), was retired despite never actually becoming a hurricane.

There are a lot of extremely strong hurricanes in this set, but there’s still a very large number (there’s even a tropical storm!).  I’m going to narrow it down even further, to hurricanes that caused most of their damage to the United States. This eliminates hurricanes like Mitch in 1998, but I’m investigating when the next big hurricane will hit the U.S., not the rest of the Atlantic.

42 storms are represented above. I assigned a score to each hurricane based on each of the statistics on the right (scores not shown); Katrina (2005) was easily the most extreme hurricane ever to hit the United States.

Now we can begin looking for trends. Hurricanes overall appear to be getting stronger and it looks like there are more that have been retired in recent years, but…

R² =0.07818

That’s not a very conclusive regression line, and logarithmic or exponential lines don’t really fit either. While the numbers from 2003-2005 are eye-catching (12 hurricane names retired in three years!), 2006 and 2009 did not have any hurricanes retired, and neither hurricane on that graph from 2010 made landfall in the U.S..

On the other hand, there has been a small increase in total named storms over the years:

http://upload.wikimedia.org/wikipedia/en/timeline/61b5be0856ccb449ab4978b2909ae8d7.png

There might be a bit of a cycle going on- in recent years, a two to four year cycle of powerful hurricane seasons appears. 2008 had several strong hurricanes three years after 2005, and 2009 had none after the weak 2006 season. But as we look back (and forward- just Irene in 2011), we can see that this theory doesn’t hold.

How about ENSO? We covered this phenomenon in depth a few months back, and it would make sense. El Nino supposedly represses hurricane growth, and our data supports that. Unusually strong El Nino effects match up with unusually weak hurricane season.  The reverse of El Nino, La Nina, would then spur hurricane growth, right? Not so fast- the data on hurricane seasons that correspond with La Nina doesn’t provide a strong correlation either way.

The key to this problem would appear to be the 2005 hurricane season. It was unusual in many ways. By all measures, it was the strongest hurricane season ever. Hurricane Emily was the earliest category 5 storm ever. Vince formed father northeast (into cooler waters) than any other storm on record. Wilma strengthened ridiculously fast after its formation. Hurricane Epsilon was the latest hurricanes ever, lasting deep into December. Tropical Storm Zeta went all the way into January. And yes; those are Greek letters, meaning that the 21 letters from the English alphabet were exhausted- the only time this has ever happened. In all, the 2005 season produced 31 tropical depressions, 27 named storms, and 15 hurricanes (7 of category 3 or above). It accounted for 3,913 deaths and over $150 billion of damage.

I can find just two unusual aspects concerning the climate in 2005. The first is that El Nino was expected to develop, but didn’t. The second is that the years leading up to it- 2002-2005- were four of the five warmest years on record (at the time). Fourteen hurricane names were retired for these four years, and the one other year in the top five was 1998, which saw the wraths of Hurricanes Mitch and Georges. Did the heat finally reach a climax in 2005, causing the extreme hurricane season? Maybe, but more likely not. In the Pacific, 2005 was an average or below average year. (It should be noted that 2006 was the most active Pacific season since 2000, however.) Additionally, many of the years since 2005 have been warmer, and yet none of them had hurricane seasons quite as strong.

Overall, I just can’t find a trend for powerful storm season. Hurricanes are simply so unpredictable that a storm season predicted to be “slightly above-average” can produce 28 named storms; the opposite can be true as well. We don’t know when, specifically, in a season the next big hurricane will hit, or even what year it will be.* We can make guesses, but nature will laugh at us and prove us wrong anyway. The best we can do is prepare for the worst and try to minimize the damage and loss of life.

*In 2011, Colorado State University, one of the leading hurricane prediction centers, announced that it would no longer be releasing quantitative forecasts six months prior to each season, as  "...forecasts of the last 20 years have not shown real-time forecast skill." Hurricanes are unpredictable even for the professionals from a long distance away.

Will There Ever Be A Time When No One Alive Has Walked On Another World?

The death of Neil Armstrong brought out a lot of emotions in me. I wasn't around when he took his first steps on the moon, but I was certainly aware of the magnitude of this achievement. To be able to look up to the moon and know that he and eleven others had walked there, on that sphere so far away, was captivating.

The death of the last frontiersman also instilled a thought in my head, though. No one has walked on the moon in nearly forty years. Some of you may remember this profound post made by Randall Munroe on xkcd:

With no plans in the near future to revisit the moon- and with Mars still a ways off- there may soon be a time when there will be no one alive who has walked on another planet or moon. It's a sad thing to think about.

When will that be, if it happens? I don't have the time to look up actuarial tables, so I used the average life expectancies for people with ages equal to the remaining astronauts (as can be found on Wolfram|Alpha). I took an extra liberty and assumed that, on average, each of them are within the top 10% of the population in terms of health (astronauts must be fit, and their habits likely continue into an older age). I did not do any research into their individual health conditions.

I also feel like getting someone on Mars is a matter of when, not if. NASA doesn't have any sort of formal timetable out on this project, but I've heard 2030 as a ballpark figure by multiple sources. My goal is to discover the odds of any of the astronauts surviving to 2030.

The results are promising on this front. By the time 2030 comes around three astronauts in particular- Eugene Cernan, Charles Duke, and Harrison Schmitt- will be younger than the lifespan for the top 10% of people their age. Taking all the astronauts together, the average chance that any one of them will survive until 2030 is about 7%. All things considered, I'd say the odds are good that someone will survive until we set foot on Mars. Anything past that 2030 date, however, and the odds go sharply down. All of the astronauts will be pushing 100 by then.

I don't feel great about looking at "death rates" because there's hardly anything statistical about it. It all depends on your lifestyle and past, and for these astronauts, I'm sure there's a long, bright future still ahead of them. I mean no disrespect to anyone, especially Neil Armstrong.

America needs a new frontiersman now, and I'm sure NASA is hard at work on it. You could say our Curiosity needs to be satisfied again. Let us move forward to Mars, while we look backwards with fond memories of our missions to the moon. RIP, Neil Armstrong.

My data:
https://docs.google.com/spreadsheet/ccc?key=0AnZrkjWWJajQdGlLMzFNWm1RUWFaZHJ5ZllRVkQ4c2c

How do the Olympics Affect the Presidential Election?

The United States puts a lot of time and effort into sporting events. In the US, the Super Bowl is the most watched television program ever (and also the second, and the third...). Athletes are some of the highest-paid people on the planet. In 2007, around 19 million people participated in fantasy football leagues across the country.

It would stand to reason that sports have some sway over the decisions people in America make. The biggest decision of all occurs every four years: the presidential election- and coincidentally, the election happens about three months after the world's biggest sporting event: The Olympics.

The question I intend to answer today is how much impact the Olympics have over the presidential election. Do incumbents win reelection more often when the U.S. wins the most golds? Do they receive a boost when the U.S. successfully hosts the games?

To compile this data I again used the IOC's medal ranking tables that I used in my first Olympic post. The U.S. has "won" (had the most gold medals in) 15 out of 24 summer Olympics. I did not include the winter games, as the more recent ones have occurred in non-election years. I also did not include boycotted games, games that took place after the election (1956) or the 2012 London games.

The results may surprise you. Of the fifteen times the U.S. came in first in the standings, the incumbent party was reelected eight times, and a new party was elected seven times. Similarly, of the nine times the U.S. did not come in first in the standings, the incumbent party was reelected five times, and a new party was elected four times.

When the U.S. has hosted the Olympics, the results have been more favorable: the incumbent party has won three out of four times. On the other hand, when the Olympics were cancelled during World War I and II, the incumbent party won each time, proving that the U.S. at least cares more about the war effort than sports.

Then again, when we boycotted the 1980 Moscow games, incumbent president Jimmy Carter lost to Ronald Reagan.

Speaking of the Soviet Union, I identified nine Olympics when the U.S. had a clear rival- in politics and in the medal count: Germany in 1936, China in 2008, and the Soviet Union from 1952 to 1988. The U.S. won more gold medals than their rivals just three times out of nine- yet the voting public didn't seem to care. The six "losses"are split evenly between the incumbent party and a new party, and the incumbents actually lost more often than not when the U.S. defeated their rival.

So what does this mean? The U.S. isn't as in to sport as one may think. We'll have to use other, more efficient means of forecasting the upcoming presidential election.

My data: https://docs.google.com/spreadsheet/ccc?key=0AnZrkjWWJajQdDcyV3kzWWVGaUVZcXRndnJ3M3MxMUE

How to Become an Olympic Host Country

Yesterday, we concluded that Olympic host countries have an advantage over other countries. They tend to score about three places higher on the medal tables over other years. Today, we'll look at how to become an Olympic host country; specifically, what factors in the most to the IOC's decision about who hosts Olympic games?

This is what the IOC has to say about the process:

"The IOC elects host cities following a two-stage process. Cities wishing to stage the Games in question become 'Applicant Cities'; the IOC Executive Board then selects a number of applicants to be considered 'Candidate Cities', from which one is chosen by a vote of the IOC session."

Obviously, a lot goes into this selection. There is no one factor that determines who gets to host the Olympics. Often, a city may be very well-qualified to host the games, but because several games have been hosted in the region recently, they will be rejected.

But if everything else was considered equal, is there something that can be considered a deciding factor?

I explored five independent factors: Population, Population Density, Size (Area), Human Development Index, and Gross Domestic Product (Total GDP). (Did I miss something? Let me know in the comments).

I searched each of these based on today's totals, not at the time the IOC awarded the Olympics. This might change the data slightly, but a good amount of countries (Belgium, Sweden) had relatively large development but not anymore, and a good amount of countries (Mexico, South Korea) had relatively small development but now are well-developed.

Based on today's statistics, here are the results:

Population, Area, Population Density
When the Olympics began around the turn of the century, many- but not all- the countries that hosted had high populations. The United States, France, Great Britain, Germany- all have large populations. And when you look at the data, it appears that a great amount of countries have high population, size, and area. However, there are many countries that also have a large size that have not hosted the games. India, Indonesia, and Brazil all have large populations and areas yet have not hosted an Olympics (Brazil, of course, will host 2016's summer games). Many African countries also have the same problem. Does it have to do with security? Or perhaps the lack of good facilities for athletes? Or maybe it's a matter of human development.

Human Development
It's understandable that the IOC would not want to shine a spotlight on countries that are not well-developed. An unstable country, such as Syria, over the seven-year period between a selection and the actual games, could spell disaster for the Olympics. While we can identify a more solid trend relating HDI to the Olympics, it's still not a very strong link. Mexico, Russia, and the countries making up the former country of Yugoslavia are not in the top tier of the HDI listing like most other Olympic hosts, and China does not even make the top 100. While many of the countries that host the Olympics do have very high Human Development Indices, it's not at all a solid trend (An r^2 value of 0.1). This leaves just one category:

Gross Domestic Product
The GDP of a country has the strongest identifiable link to hosting the Olympics. It's still not perfect, but it's much stronger than any other trend. What helps this variable the most is the United States. By far, the U.S. has the highest total GDP of any country and the U.S. has also hosted the most Olympic games. The trend isn't perfect, but it's much, much better than any other factor I can identify.

In the end, no one variable determines which country is awarded the Olympic games. But countries with a higher GDP have a much stronger chance. This is something to keep in mind, when, in 2013, the IOC makes their decision between Istanbul, Turkey; Madrid, Spain; and Tokyo, Japan, for the 2020 Summer Olympics.

Apologies for the late post tonight; tomorrow we'll look at how the Olympics affect politics.

My data and graphs:
https://docs.google.com/spreadsheet/ccc?key=0AnZrkjWWJajQdFlxT3J5QW9vdmJsbFc5UkFnT0N2M3c

Does Being a Host Country Matter? - Olympics 2012

For the past two and a half weeks, Olympic fever has swept the globe. With the festivities over, I've been scouring some historical results of the Olympic games, looking for trends. In the next three days, I'll post three separate problems I examined and the conclusions that I drew from my research.

Today, we'll explore whether or not being a host country matters or not. Do countries win more medals than usual when they host?

Tomorrow, we'll take a look at what it takes to become a host country. What factors in the most to being selected to hold an Olympiad? (We're not counting bribery)

Wednesday, we'll examine Olympic and election results to determine if U.S. performance at the Olympic games influences the presidential election.

Before I begin today's post, I should make it clear that there is no real "winner" to the Olympics. The IOC makes a point of stating that it is an individual competition between individual athletes- not between countries. However, that doesn't stop them from releasing tables with the medal counts for each country- information for which was very helpful as I researched this topic.

If you look at the earliest few Olympics, it would make sense that being a host country greatly improves your performance level. The first five countries to host- Greece, France, the US, the UK, and Sweden- all won more medals than anyone else when they hosted. (The rankings are sorted by gold medals, however, so Greece and Sweden didn't actually "win" or come in first on the table).

There are reasons for this trend, however. These countries were athletic powerhouses during this time period first, and second, they didn't have to travel as far to compete. Keeping in mind the airplane was invented seven years after the first Olympics (and wasn't exactly ready for mass transportation at that point), travel was a major issue in the first few Olympics. Just look at the nationalities of athletes who competed in the 1904 St. Louis games:

Both the 1900 Paris and 1904 St. Louis Olympic games were notably low-quality, so for some strong evidence we need to move closer to today. However, the Soviet Union and United States dominated the Olympic games for many, many years. In fact, Germany, Canada, Norway, and China are the only countries to be ranked first besides the USSR and the US since the 1940s, and only China accomplished that in the summer games (Incidentally, this happened in the last summer Olympics, when China hosted).

On average, host countries place about fifth in the Olympics. But we can't really draw any conclusions from this, because some countries win all the time (the U.S. has won nearly 1/5 of all gold medals!), and some countries don't happen to do very well. Canada, for example, ranked 27th when it hosted the 1976 summer games with no gold medals- and it wasn't that big of a surprise. They'd ranked 27th in 1972, as well. So rather than look at the overall placement of host countries, let's see how they fared compared to the Olympics before and after.

Since 1960, only two countries- Italy in 2006, and Canada in 1988- have seen their ranking drop from the previous Olympics when hosting. In fact, on average, countries move up nearly three positions on the medal tables when they host. The trend is even more clear when we look at how countries fare after they host the Olympics. Host countries fall nearly five positions on the table in the next Olympics. This is astonishing because many countries have relative positions on the tables; that is, they win about the same amount of gold medals per year. (This average, however, is brought down by Greece, who fell 43 spots in 2008 after hosting in 2004. Yet even without Greece the average is over -3.5)

We can conclude that hosting an Olympic games certainly helps boost your chances of winning medals. Look at Japan, in the 1972 winter games. They won four medals, despite winning none in the 1968 or 1976 winter Olympics. And hosting the 1992 summer Olympics was enough to propel Spain 19 positions up the medal tables to sixth- only to fall seven places in 1996.

This is good news for Brazil- they host the 2016 Summer Olympics, and they may need a little help winning medals in front of the home audience: Brazil finished 22nd in London, winning just three golds.

For a full look at my data, go to this link: https://docs.google.com/spreadsheet/ccc?key=0AnZrkjWWJajQdDNUbW5xX1pBN0RGdHVqN19IbDQ2V1E