Biostatistics PhD candidate Hilary Parker dived into the most poisoned names in US history. Her own name topped the list. There were several fad names such as Deneen, Catina, and Farrah that saw a quick spike and then a plummet, but the trend for Hilary is different.

“Hilary”, though, was clearly different than these flash-in-the-pan names. The name was growing in popularity (albeit not monotonically) for years. So to remove all of the fad names from the list, I chose only the names that were in the top 1000 for over 20 years, and updated the graph (note that I changed the range on the y-axis).

I think it’s pretty safe to say that, among the names that were once stable and then had a sudden drop, “Hilary” is clearly the most poisoned.

There it is minding its own business, enjoying a steady rise in popularity over a few decades, and then boom, Bill Clinton is elected, and the name dies a quick death.

Be sure to check out the rest of the analysis. Good stuff. [Thanks, @hspter]

The Mercator projection can be useful for giving directions, but when it comes to world maps, the projection doesn’t hold up well as you move far north and south. By how much? Give this puzzle game a try and match the red boundaries to their respective countries.

Carlos Scheidegger and Kenny Shirley, along with Chris Volinsky, visualized Major League Baseball Hall of Fame voting, from the first class in 1936 (which included Babe Ruth) up to present.

All a fan can do is accept that Baseball Hall of Fame voting, conducted by the Baseball Writers Association of America (BBWAA), is a phenomenon unto itself. If we can’t understand baseball Hall of Fame voting, though, maybe the next best thing is visualizing the data behind it. The set of interactive plots on this webpage is our attempt to do that. We were especially interested in two things: (1) viewing the trajectories of BBWAA vote percentage by year for different players throughout history, and (2) simultaneously viewing the career statistics of these players, to help find patterns and explain their trajectories (or to reassure ourselves that the writers really are crazy).

The interactive is on the analysis side of the spectrum, so you might be a bit lost if you don’t know a lick about baseball. However, if your’re a baseball fan, there’s a lot to play around with and dimensions to poke around at, as you can filter on pretty much all player stats such as home run count, batting average, and innings played. At the very least, you’re getting a peek at how statisticians pick and prod at their data.

Start at the examples section for quick direction. I eventually found myself looking for downward trajectories. Poor Mark McGwire. [Thanks, Chris]

As the Super Bowl draws near, Facebook took a look at football fandom across the country.

The National Football League is one of the most popular sports in America with some incredibly devoted fans. At Facebook we have about 35 million account holders in the United States who have Liked a page for one of the 32 teams in the league, representing one of the most comprehensive samples of sports fanship ever collected. Put another way, more than 1 in 10 Americans have declared their support for an NFL team on Facebook.

It’s a fairly straightforward geographic breakdown based on the most liked team in each county, as shown above. So you can kind of see where rivalries come from.
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We’ve all seen rain maps for a sliver of time. Screw that. I want to see the total amount of rainfall over a ten-year period. Bill Wheaton did just that in the video above, showing cumulative rainfall between 1960 and 1970. The cool part is that you see mountains appear, but they’re not actually mapped.

The hillshaded terrain (the growing hills and mountains) is based on the rainfall data, not on actual physical topography. In other words, hills and mountains are formed by the rainfall distribution itself and grow as the accumulated precipitation grows. High mountains and sharp edges occur where the distribution of precipitation varies substantially across short distances. Wide, broad plains and low hills are formed when the distribution of rainfall is relatively even across the landscape.

See also Wheaton’s video that shows four years of rain straight up.

Is there more recent data? It could be an interesting complement to the drought maps we saw a few months ago. [Thanks, Bill]

I’m almost certain this relationship is significant. Side note: Is there a meaningless-correlations tumblr yet? [via]

Arthur Buxton plotted the most common colors of Penguin Publishing science fiction colors and arranged them over time. Also available in print.

I wonder if there’s a good way to show connections between the titles or the different covers for each title.

How wealthy are the richest people in the world? How do they compare to each other, and how does their net worth change over time? Bloomberg just put up an interactive tool to answer such questions, and it’s updated daily with new data.
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For the past year, I’ve been working on Data Points: Visualization that Means Something, and you can pre-order it now.

Visualization has grown a lot in the 5-something years I’ve written for FlowingData. It’s not just a tool for analysis anymore. Visualization is a way to express data, and it comes in the form of information graphics, entertainment, everyday interfaces, data art, and yeah, tools, too. Your approach to data and visualization changes based on application.

But even with all these (awesome) new applications, there’s a constant across all of them: the data.

Data Points starts here, and takes you through the process of understanding data, representing it, exploring it, and designing for different applications. Whereas Visualize This was about getting your feet wet with lots of code examples, Data Points is code-independent and is a perfect complement that helps you understand and allow others to understand data better, which is sorta the whole point.

The manuscript is written, the 240 graphics (by me and many of your favorites) are set, and I’m really happy with how it turned out.

It’ll officially be out late March or early April. Crazy, nerve-racking, and exciting all at the same time.

More details to come. Until then: pre-order the book today.

Thanks to Sha Hwang, you can now siltscan videos on YouTube and Vimeo with an easy-to-use bookmarklet. Just go to the video and click. In case you’re unfamiliar with the technique, here’s a description from Golan Levin:

Slitscan imaging techniques are used to create static images of time-based phenomena. In traditional film photography, slit scan images are created by exposing film as it slides past a slit-shaped aperture. In the digital realm, thin slices are extracted from a sequence of video frames, and concatenated into a new image.

Be sure to switch over to HTML5 on YouTube or Vimeo first. The bookmarklet won’t work with Flash.