To keep track of performance, Matt Stiles made the Dodgers Data Bot, which provides a dashboard view of various baseball metrics sourced by Baseball Reference.

This repository — a growing work in progress — feeds Dodgers Data Bot, a statistical dashboard about the LA Dodgers’ performance.

The code executes an automated workflow to fetch, process and store the team’s current standings along with historical game-by-game records dating back to 1958. It also collects batting and pitching data, among other statistics, for the same period. These records are processed and used to bake out the site using the Jekyll static site generator, in concert with Github Pages, and D3.js for charts.

Thumbs up for personal projects. You can find the code on GitHub.

Reading the words of my younger self and revisiting that guy’s process was… educational.

The recent solar storms brought pretty lights to the night sky in some parts of the country, but they can also bring challenges to the power grid. For Bloomberg, Hayley Warren, Denise Lu, and Naureen Malik look at the surges through the lens of data collected by Whisker Labs Inc.

While no significant failures were reported, there is potential for these surges to cause major damage. The last time a storm this strong struck Earth, there were power outages in Sweden and damaged transformers in South Africa, according to the US Space Weather Prediction Center. Strong solar storms can also affect radio signals, global navigation systems, satellites and even pipelines. SpaceX’s Starlink unit said on its website that it experienced “degraded service” that its team was investigating.

I don’t know about you, but where I live, the housing prices keep going up, and they just seems way too high. Is it like this everywhere? For The Washington Post, Kevin Schaul and Rachel Lerman made maps that show the increase or decrease, but mostly increase, in house prices by ZIP Code.

Visualize This is a real book now! The official publication date is May 29, but you might get it early if you order now, depending on where and when you order it.

The publication process is interesting, because you write and write and make lots of charts over many months. There’s editing and revision. It’s on your mind constantly. Then there’s a gap when your part is done and your publisher (for me, Wiley) takes over. All of a sudden, the book is printed, you hold it in your hands, and it’s satisfying.

Get a copy today: Amazon — Wiley — Bookshop.org

Wilson Lin used an abstract map to visualize 40 million posts and comments from Hacker News. He calls it the Hackerverse. Lin described the full process of scraping, using text embeddings to map words to locations, and making an interface that worked with thousands of points:

What can we do with the 30 million comments? Two things I wanted to try to analyze at scale were popularity and sentiment. Could I see how HN feels about something over time, and the impact that major events has on the sentiment? Can I track the growth and fall of various interests and topics, and how they compare against their competition?

Assuming you were still alive flying into a black hole, NASA’s Goddard Space Flight Center visualized what the views might look like.
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There are a lot of tools to visualize data. Some are visualization-specific. Some are tools that let you make charts but are focused on other data things. New apps come out with new features that promise new things. This can make it tricky to find the best visualization tool.

Also, the “best” depends on what you want to visualize and how you want to do it. A data dashboard on a projected screen carries different requirements than an exploratory tool on a laptop, which carries different requirements than a data story that scrolls on your phone. Look for the tools that are best for you.

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To capture solar energy for use in the evening, batteries have grown in popularity over the last few years, especially in California. For the New York Times, Brad Plumer and Nadja Popovich show the shift with a pair of stacked area charts.

Five years ago, these pair of charts would have been a single animated one.

When you analyze data, there are times when a trend, pattern, or outlier jumps out and smacks you in the face. Or, you might calculate results that seem surprising. Maybe they’re real, but maybe not.

Based on data from NASA’s Stratospheric Observatory For Infrared Astronomy (SOFIA), Villanova University researchers developed a map of the magnetic fields in the Milky Way.

For Strange Maps, Frank Jacobs:

The colors show the interaction between warmer dust clouds (pink), cooler ones (blue), and magnetic fields, indicated by radio filaments (yellow) — mysterious tendrils up to 150 light-years long. By revealing variations in the orientation of magnetic fields across dust clouds (some with fanciful names like The Brick and Three Little Pigs), this map offers a first glimpse at the complex arrangements of dust and magnetism in the CMZ.

PerThirtySix made a communal plot that asks for your opinion via scatterplot and you can see how you compare against the aggregates. A new poll goes up every day.

The inspiration for this comes from a whiteboard in an office I used to work at. Every so often, a new pair of questions would be posted and people would contribute their answers by marking where on the scatterplot they belonged. It was fun seeing how my answers compared to others, and guessing who might have answered where. I hope this tool brings you some of that fun!

From the oldie-but-goodie department, this fun program uses a genetic algorithm to drive car thingies across a bumpy terrain. Change parameters. Watch the cars go. See how far the winner travels before crashing.

The code is available on GitHub.

In case you’re unfamiliar, a genetic algorithm creates mutations in a population of objects or systems. Those that perform better move on to the next generation. The algorithm keeps going until you get an optimized point. In this case, the algorithm tries to optimize travel distance.

See also evolving floor plans and an optimized brewery road trip. [via kottke]

For Reuters, Mariano Zafra, Anurag Rao, and Jon McClure describe how bird flu can pass between mammals, but, while not impossible, transmission to humans is still unlikely.

Because of the heavy viral load in milk and mammary glands, scientists suspect the virus can spread between cattle during the milking process, either through contact with infected equipment or with virus that becomes aerosolised during cleaning procedures.

One in five commercial milk samples tested in a nationwide survey contained particles of the H5N1 virus, according to the FDA. The agency said, though, there is no reason to believe the virus found in milk poses a risk to human health and that pasteurisation effectively killed the virus.

Maybe one day AI tools will be advanced enough to process a random dataset and produce valuable insights that incorporate the context of the real world. That day is not today.

Stephen Wolfram gets into modeling biological evolution:

Why does biological evolution work? And, for that matter, why does machine learning work? Both are examples of adaptive processes that surprise us with what they manage to achieve. So what’s the essence of what’s going on? I’m going to concentrate here on biological evolution, though much of what I’ll discuss is also relevant to machine learning—but I’ll plan to explore that in more detail elsewhere.

I mostly put this here as a bookmark for myself, but I have a feeling you’ll read through this before me.

You might’ve heard a little something about AI these past few months. If the ideas seem kind of fuzzy, Nicky Case and Hack Club are collaborating on a guide for how these things work and the issues that we should address as AI-based things grow more common. It has comics.

While the current tools are fun to play with, there are and will be real safety challenges as the systems slurp up more data and process faster. It grows more likely that the systems will directly affect your day-to-day life. So it seems worthwhile to know a bit how they work instead of blindly trusting companies than run with different motivations than you.

It’s been over 200 years since the cicadas of Brood XIII and Brood XIX came up at the same time. For the New York Times, Jonathan Corum revisits old cicada maps by Charles L. Marlatt from 1922. The spatial distributions look similar to current patterns and show how predictable these things are, even though they’re in the ground for so long.