Looks like Mickey Rooney died, marking yet another victim of the Curse of the Simpsons. He was a guest star in one of my all-time favorite episodes, Radioactive Man the Movie ("The goggles, they do nothing!").
I also see that over at the new fivethirtyeight site, Ben Morris did a very similar analysis to see if professional wrestlers die early. Unlike being a guest on the Simpsons, which correlates with an additional 3 years of longevity, being a pro wrestler seems to be very bad for your health.
One good point on Ben's analysis is that he used different actuarial tables for different years (to account for improving health technology), while I only used a recent one. I don't think this would change the Curse of the Simpsons much, and if it did it would be in the direction of making the anti-curse even stronger.
So it looks like wealthy people in Canada and Denmark are big on nepotism. That's not actually that surprising, what I'm curious about is the slight upturn on the left side of the plot. Why do sons of poor fathers work for the same employer more often than average?
The article claims that becoming an elite punter or placekicker is almost entirely an issue of training and getting high quality instruction, physical gifts are almost inconsequential. Besides lots of training, one also needs to work the social aspect of the game and get noticed by attending high-profile camps. Interesting stuff!
Darrell Huff's How To Lie With Statistics is one of my all-time favorite books. The only problem with it is that it has become a bit dated (you have to calculate inflation on the fly to make sense of a lot of his examples involving money). Since people are still abusing statistics and data visualization today, I thought it would be fun to have some posts pointing out modern examples.
Today's deceptive abuse of data comes from this Mother Jones article, which in turn was inspired by this Bloomberg article. The quick summary is that gun-related deaths are projected to exceed traffic-related deaths. The Mother Jones article then follows up to point out that this has already happened in several states. Shock! Horror!
Here's the super sneaky plot from Bloomberg.
The tricks here. 1) The y-axis is cropped, making the trends appear larger. It looks like the firearms deaths per year has crossed 30,000 6 times. One could easily say that the number of firearm deaths has stayed constant over the last 30 years. 2) The real trick is that they are plotting raw numbers rather than per capita. For example, it looks like there were 30,000 firearm deaths in 1983 and again in 2005. But the US population grew by 57 million people in that time! If you sampled 10,000 random Americans in 1983, 13 of them would be killed by firearms. But grabbing 10,000 Americans in 2005, only 10 would get killed by firearms. Despite the curve looking like it's going up, you are actually safer from gun violence today than compared to just a few years ago.
Note the phrase "set to pass". It makes you think gun deaths are going up, so you are getting less safe. Really, the title should be "gun violence dropping at a slightly slower rate than traffic fatalities, which are also decreasing". But that doesn't sound scary and sell magazines. Or get web clicks, or whatever these guys do to generate revenue.
[caption id="attachment_126" align="alignnone" width="630"] Here's a more honest plot of the data, looking at things per capita.[/caption]
[caption id="attachment_127" align="alignnone" width="630"] Looking at just firearm homicides (so not including suicides and accidents), There's a HUGE drop over the last 20 years. Umm, no one really knows why this happened.[/caption]
It's like your had Joe at 290 lbs on a diet and Emily at 250 lbs on a diet. After 6 months, Joe is down to 220 lb (-70 lb) and Emily has dropped to 210 (-40 lb). Would you say Emily's weight is "set to pass" Joe's? I wouldn't recommend it since Emily has been working out and can still throw a solid punch at 210.
Inspired by Bloomberg's crappy plot, Mother Jones jumped in and pointed out that several states had "already hit this grisly milestone". Look, they made a handy map:
WOW, that makes it look like the west coast has a huge problem! Gun deaths have surged ahead of traffic deaths in lots of states. But I lived in Texas. Things I know about Texans, 1) they have a lot of guns, and 2) they can't drive for shit. Seriously, the fire hydrant outside my apartment got obliterated 3 times! And I only lived there for two years.
The lessons we can take away from these maps:
Wyoming looks like it has a car problem
Louisiana has a murdering-people-with-guns problem
The west coast is one of the safer places when it comes to both driving and not getting shot.
You can't see Alaska, but take my word for it that it has average gun homicide, but lots of gun deaths. This is driven by Alaska having nearly double the suicide rate of the US average.
Comparing my maps with the Mother Jones one, the only commonality seems to be Louisiana, where there are in fact a surprisingly large number of gun deaths. The rest of the states they shade a dangerous-looking brown are all on the safer side.
If they are going to make silly maps, I'm going to go ahead and show them how to really mis-interpret data. Well, we see that car deaths are dropping faster than gun deaths. Clearly, we need to take the lessons we've learned from cars and apply them to guns. From now on, all guns shall come with airbags, crumple zones, and rear defrosters. The next step in car safety will probably be the move to automated driverless cars. The same should be done with guns! Google will start making a shooterless gun--you just wave it around and advanced algorithms developed in the Stanford CS department will decide when to actually fire.
The real crime here is that this data is telling us some interesting stories, and raising lots of cool questions. But rather than address those, the authors bend the data to make some scare-stats. Things I see in the data:
wow, traffic deaths have dropped like a rock! There's clearly been a ton of useful technology invented (anti-lock brakes, airbags, crumple zones, power steering, on and on). Check out this cool video of a '59 Chevy doing a head-on with a 2009 Chevy. The dummy in the '09 maybe breaks a leg, the '59 dummy is killed instantly. There's also things like seat-belt laws, anti-DUI adds, stricter rules on teen drivers, continually upgrading the deadliest roads. Lots of work has gone into making driving safer, and at least some of it works.
There's a huge drop in auto deaths starting around 2007. Is that a result of the economic crash? Did people switch to the bus since they couldn't afford their cars? Did they just drive on fewer vacations? Do you just not drive much if you don't have to go to work? Should I be concerned that while the stock market has recovered, it doesn't look like people have gone back to driving (i.e., this has been a jobless, and thus comuteless, recovery)?
The "tall pole" in gun violence is actually suicides. Hey, let's make sure everyone has access to mental health care. Seriously, be on the lookout for folks suffering from depression.
While there are lots of reasons for driving being safer, the drop in gun violence is actually an amazing mystery. It doesn't look like violent crime is driven by the economy or police policies. Ironically, an excellent Mother Jones article makes a strong case that lead exposure is responsible for violent crime rates.
Anybody else remember Danny Almonte? He was the star Little League pitcher who led a team from the Bronx to a third place finish in the Little League World Series in 2001. Then it was revealed that Danny was actually two years older than his birth certificate said, making him too old for the league. The lesson here is that you can look like a damn good athlete if you are playing against littler kids.
I'd heard about the ice hockey relative age effect in the horrible Malcolm Gladwell book Outliers, and again in the slightly less horrible Freakenomics by Dunbar and Levitt.
If one looks at the distribution of birth dates in the general population, it's pretty random. There's about the same number of people born on every day of the year. That's not true when you look at professional hockey players, they are more likely to have birthdays early in the year.
The most common explanation for this is that it's a case of "the rich get richer"**. If we take a bunch of 6 year-old kids and put them in a hockey league, some will do well and others will suck. Coaches will spend more time with the talented kids, encouraging them to continue in the sport, putting them on all-star teams, etc. But out of those 6 year-olds, some will have just turned 6, while others are almost 7. That translates to some of the kids being 17% older than other. At young ages, that can be the difference between a couple growth spurts. Coaches and parents are probably pretty bad at telling the difference between a "talented" kid and one that is simply slightly older than everyone else. Just like Danny Almonte, the older kids will look impressive just because they are older. Once a kid gets labeled as skilled, even if it's only because he's older than the competition, he gets extra resources and his lead over the other kids grows. (note: I came back and switched to all male pronouns in this section, you'll see why shortly)
That's a fine story for how the relative age effect gets started. I'm a little disappointed that I can't find anyone who's gone out and tested it though. After all, maybe it's just that younger kids get sick of losing to the older kids and quit more quickly.
I was surprised to hear that there's also an age-effect in tennis. I thought there would be little to no birthday bias in tennis because junior tennis tournaments are usually organized in two-year age ranges (e.g., 14 and 15 year-olds are placed in the same brackets and play against each other). So even if you are born in January, you still spend every-other year playing against a field where half the competition is older than you.
It's surprisingly easy to go out on the intertubes and scape the birth dates of professional and junior athletes, so I did. After ruining a couple credit ratings, I got around to looking for relative age effects. Results!
[caption id="attachment_96" align="alignnone" width="300"] Age distribution of boys tennis players. Significantly peaked early in the year.[/caption]
[caption id="attachment_97" align="alignnone" width="300"] Age distribution of girls tennis players. Consistent with random (but some odd-looking spikes, might be nice to get a larger sample).[/caption]
[caption id="attachment_98" align="alignnone" width="300"] Age distribution of NBA basketball players. Consistent with random.[/caption]
[caption id="attachment_99" align="alignnone" width="300"] Age Distribution of NFL football players. Consistent with random.[/caption]
[caption id="attachment_100" align="alignnone" width="300"] Age Distribution of NHL hockey players. Peaked significantly early in the year.[/caption]
For each sport, I took the players and calculated which day of the year (1-365) they were born on. I confess, I didn't adjust for leap years. Statistical p-values from a KS-test comparing to a flat distribution are listed, with significantly different distribution in red.
This is also cool since it is a way to address the issue of nature vs nurture in what makes an elite athlete. Since genes should be the same regardless of when someone is born, it's a good bet that the RAE is a purely environmental effect. Note the cool excerpt over at si from a book looking at nature v nurture in athletics.
It looks like the NBA, NFL, and girls tennis have no relative age effects. Meanwhile, theNHL, and boys tennis have significant age effects. Others have also found RAE for MLB baseball players. The NBA doesn't surprise me too much. You can't teach height, so it might be harder to hold on to an early advantage in youth basketball. You might spend your junior high years as a dominant 6-foot tall center, only to wind up point-guard sized in high school. All that time you spent practicing your post moves won't help much if you stop growing at 6'-2".
Let's see if we can visualize where the differences may be coming from. On one side, we have hockey, baseball, and boys tennis all with strong RAE,
and then we have basketball, football, and girls tennis with no RAE,
It's hard to find a pattern that explains which sports have RAEs and which don't. Baseball and tennis are more focused on individual skill, but then why doesn't girl's tennis show an RAE? Football and basketball are more physical, but then so is hockey, so why does it still have an RAE? Looking at the images above (grabbed haphazardly from google image searches), it sure looks like there's one group of all white guys, and then everyone else in the other group. I checked to see if NFL quarterbacks (one of the few football positions where the majority of players are white) have an RAE, but they don't.
Can we come up with a good explanation for which sports have RAEs and which don't? Here are my two stabs in the dark:
1) Football and basketball are intrinsically more physical sports. If a late bloomer suddenly hits 6' 7", a basketball coach will try to develop him even if he hasn't had much previous experience. A player's relative age doesn't matter, as long as they eventually hit some physical mile-stone, they can get a shot at playing. I even just read that an Olympic discus player is getting a shot trying out for an NFL football team. This isn't the case with the "skill" sports. If you haven't mastered a backhand or fielding grounders by the time your 13, you're never going to make the pros. Coaches in the skill sports aren't looking for late bloomers they can develop.
2) Parents invest a lot of resources developing potential athletic talent in their kids. Most of the theories of RAEs involve coaches giving more attention to older kids, but I think a large part of it has to be parents seeing their kids being successful at an early age and encouraging them to continue in a sport. If you ask a coach if your kid has talent and should be signed up for more classes, the coach will undoubtedly say yes (kinda like taking a car in for a free brake inspection, you're going to be told you need new brakes). But when a parent actually sees their kid is doing well at a sport, that's what will convince them to actually shell out the extra cash and time for private lessons, a fancier hockey stick, etc. I don't think it's much of a stretch to think that parents are susceptible to being gender biased, and are more on the lookout for athletically gifted sons than gifted daughters.
3) Of course, it could be a combination of the two above. Some sports are easier to pick up later in life, AND boys who show early promise in a sport (by being slightly older than their competition) end up getting more encouragement and resources than others. There's a sociology PhD thesis in there somewhere...somebody go get on that.
Some things I'd love to see:
Can anyone find a girls/women's sport that does shows an RAE?
How early does the RAE appear in a sport? The top tier 8 year-olds? 12 year-olds? Once there is an RAE does it change in strength with time?
Sport leagues tend to use Jan 1 as the date cutoff for age brackets, while schools use different dates (usually around September). Could schools be eliminating RAEs in some sports and not others? For example, scouts and college coaches watch high school football games, but not tennis, instead relying on things like USTA rankings.
**This rich-get-richer effect is eerily similar to how professional astronomy works as well. Lots of newly minted PhD's apply for prestigious fellowship. The few that land them tend to go on and have solid careers. Was it because the fellowship committee did a good job selecting the best applicants, or would any new grad have been successful given the extra resources and exposure that come with a prize fellowship?
