A note on a pack of Skittles reads, “No two rainbows are the same. Neither are two packs of Skittles. Enjoy an odd mix.” Of course that can’t possibly be right, because there are a finite number of color combinations and there are many packs of Skittles in the world. That led possiblywrong down a path of wondering how many packs it’d take before getting two identical ones. The answer came 27,000 Skittles later.

Monica Ramirez tried her hand with modeling deaths on Game of Thrones and trying to predict the next ones:

Since the series is so famous for killing principal characters (It’s true! Yu can’t have a favourite character because he/she wouls die, and slowly, other characters take the lead… and would probably die too), I decided to make a Classification Model in Python, to try to find any rule or pattern and discopver: Who will die on this last season?

I’m always on a viewing delay with this stuff, so I’m not sure whether this is right or completely wrong, but there you go. The above shows the characters ordered by probability of death (not order in which they will die).

Jen Luker noted, “As amazing as @github is, it is a tool designed to track code, not people. I’m sharing my annotated GitHub history to show you what it can’t tell you about a developer.”

As amazing as @github is, it is a tool designed to track code, not people. I'm sharing my annotated GitHub history to show you what it can't tell you about a developer. pic.twitter.com/b94kYqQHaZ

— Jen Luker (@knitcodemonkey) April 25, 2019

Data as footprints? Footprints can tell you where someone went, but you have to evaluate surroundings to figure out what he or she did along the way. And there’s a lot that can happen between when the footprints set and when you find them.

For The Washington Post, Tim Meko mapped floods, tornados, hurricanes, extreme temperatures, wildfires, and lightning:

Data collection for these events has never been more consistent. Mapping the trends in recent years gives us an idea of where disasters have the tendency to strike. In 2018, it is estimated that natural disasters cost the nation almost $100 billion and took nearly 250 lives. It turns out there is nowhere in the United States that is particularly insulated from everything.

NOWHERE IS SAFE.

Every month I collect the new tools, resources, and datasets. Here they are for April.

James Holzhauer is the new hotness on Jeopardy! with Daily Double hunting, big wagers, lightning clicks, and all-around trivia skills. For FiveThirtyEight, Oliver Roeder looks at how Holzhauer dominates:

Holzhauer has played this game like no one has ever played it before — large bets coupled with expert navigation of the game board. He has now played 14 games with his total winnings sitting above $1,000,000 and counting, and he is well on his way to surpassing the $2,520,700 won by the most famous “Jeopardy!” record-holder of all, Ken Jennings. One difference? It took Jennings 74 straight victorious shows to bring in that haul, and if he maintains his current pace, Holzhauer is on track to break that record in as few as 34.

So not only is he hunting for Daily Doubles (because we know where they usually are), but he builds a pot first so that he’ll have more to wager. And then, when the time comes, he has no problem putting the money on the line.

Caitlin Dewey for OneZero describes the case of the Fruit Belt neighborhood in Buffalo, New York, or “Medical Park” as it was incorrectly named in Google Maps:

Lott learned that the issue had been festering for years, and she wanted answers. The 2,300 residents in the Fruit Belt didn’t refer to the community as “Medical Park,” but Google Maps had done so since the late 2000s. Community members argued the designation was a calculated tweak in favor of gentrification, a digital rechristening that would be used to sell houses, market Airbnbs, and wrest the neighborhood’s future from the people who had made a home there for generations.

Lott didn’t know it at the time, but the misnomer also revealed a great deal about the invisible process major tech firms use to put neighborhoods on their maps — and how decisions based off arcane data sets can affect communities thousands of miles away.

Los Angeles Clippers commentator Ralph Lawler has a saying: “First to 100 wins. It’s the law.” The Los Angeles Times checked the numbers to see how true the statement is. It’s been true for over 90 percent of games over the years, but has become less true as pace and the three-point shot has changed dramatically in recent years. Now it’s more like first to 114.

I marked this article for later reading. It’s about Stephen Curry’s love of popcorn as a pre-game and half-time snack. Sounded amusing. Then I got to it and discovered that he scores every arena’s popcorn on a five-factor, five-point scale using a worksheet. Nice.

Give him the MVP on this factoid alone.

See the full scorecard.

By now we’ve all seen the zoomed out thumbnail view of the Mueller Report. It gives you a quick look at the amount of the report redacted, but that’s about it. So, Axios tagged every paragraph with events, topics, people, and places to make things easier to find and jump to.

Generative models can seem like a magic box where you plug in observed data, turn some dials, and see what the computer spits out. SpaceSheet is a simple spreadsheet interface to explore and experiment for a clearer view of the spaces between. Even if you’re not into this research area, it’s fun to click and drag things around to see what happens.

The redacted version (pdf) of the Mueller report was released today. Here’s the thumbnailed view for a sense of the redactions.
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This week’s issue is public.

Hi,

Warning: This week’s issue talks about sexual harassment at DataCamp.
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Feeding off the words of John Tukey, Roger Peng proposes a search for better questions in analysis:

The goal in this picture is to get to the upper right corner, where you have a high quality question and very strong evidence. In my experience, most people assume that they are starting in the bottom right corner, where the quality of the question is at its highest. In that case, the only thing left to do is to choose the optimal procedure so that you can squeeze as much information out of your data. The reality is that we almost always start in the bottom left corner, with a vague and poorly defined question and a similarly vague sense of what procedure to use. In that case, what’s a data scientist to do?

Story of my life.

Notre-Dame in Paris, France was on fire. The New York Times describes what happened in a detailed yet concise information graphic. Made in only a day, a 3-D model provides the imagery, and rotation and zooming highlight the relevant points.

For The New York Times, Sahil Chinoy on privacy and how easy it is now to automate surveillance through public video feeds:

To demonstrate how easy it is to track people without their knowledge, we collected public images of people who worked near Bryant Park (available on their employers’ websites, for the most part) and ran one day of footage through Amazon’s commercial facial recognition service. Our system detected 2,750 faces from a nine-hour period (not necessarily unique people, since a person could be captured in multiple frames). It returned several possible identifications, including one frame matched to a head shot of Richard Madonna, a professor at the SUNY College of Optometry, with an 89 percent similarity score. The total cost: about $60.

A part of me finds this creepy. The other part wants to try out the system.