Historic Papers Documenting the 'Laws of Base Ball' Sell for More Than $3 Million

Swing, hit, run, slide into home. That’s essentially all there is to baseball, right? Wrong. The sport’s far more complex than that, thanks in part to the “Laws of Base Ball,” a collection of 23 historic documents from 1857 that established some of the sport’s essential rules. Last Sunday, these early instructions became one of history’s highest-priced pieces of sports memorabilia, The New York Times reports, thanks to an anonymous bidder who acquired the seminal papers via online auction for a staggering $3.26 million.

SCP Auctions, which described the rules as the “Magna Carta of our national pastime,” was responsible for the sale. Bidding began on April 6, and lasted for just over two weeks.

Daniel “Doc” Adams, president of the New York Knickerbocker Base Ball Club, wrote the “Laws” in January 1857 when 14 baseball clubs met in New York City to codify rules for the sport, The Guardian writes. Today, Adams is credited with creating many of the game’s fundamental instructions, including nine men on a side, 90-foot base paths, and nine innings to a game.

For years, the "Laws of Base Ball" were owned by the family of William Grenelle, a Knickerbockers delegate to the 1857 convention. An anonymous buyer purchased the documents in 1999 for $12,000; he didn’t know their true value until the auction house appraised them and predicted they would sell for more than $1 million, the Associated Press reports

Think $3.26 million is a lot of money? Believe it or not, die-hard baseball fans have shelled out even more cash for prized relics from the sport's history. In 2012, Babe Ruth’s 1920 New York Yankees jersey sold for $4.4 million. In 2010, James Naismith's 1891 "Founding Rules of Basketball" sold for $4.3 million, The Guardian points out.

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iStock // Ekaterina Minaeva
Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
May 21, 2017
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iStock // Ekaterina Minaeva

Jacques Mattheij made a small, but awesome, mistake. He went on eBay one evening and bid on a bunch of bulk LEGO brick auctions, then went to sleep. Upon waking, he discovered that he was the high bidder on many, and was now the proud owner of two tons of LEGO bricks. (This is about 4400 pounds.) He wrote, "[L]esson 1: if you win almost all bids you are bidding too high."

Mattheij had noticed that bulk, unsorted bricks sell for something like €10/kilogram, whereas sets are roughly €40/kg and rare parts go for up to €100/kg. Much of the value of the bricks is in their sorting. If he could reduce the entropy of these bins of unsorted bricks, he could make a tidy profit. While many people do this work by hand, the problem is enormous—just the kind of challenge for a computer. Mattheij writes:

There are 38000+ shapes and there are 100+ possible shades of color (you can roughly tell how old someone is by asking them what lego colors they remember from their youth).

In the following months, Mattheij built a proof-of-concept sorting system using, of course, LEGO. He broke the problem down into a series of sub-problems (including "feeding LEGO reliably from a hopper is surprisingly hard," one of those facts of nature that will stymie even the best system design). After tinkering with the prototype at length, he expanded the system to a surprisingly complex system of conveyer belts (powered by a home treadmill), various pieces of cabinetry, and "copious quantities of crazy glue."

Here's a video showing the current system running at low speed:

The key part of the system was running the bricks past a camera paired with a computer running a neural net-based image classifier. That allows the computer (when sufficiently trained on brick images) to recognize bricks and thus categorize them by color, shape, or other parameters. Remember that as bricks pass by, they can be in any orientation, can be dirty, can even be stuck to other pieces. So having a flexible software system is key to recognizing—in a fraction of a second—what a given brick is, in order to sort it out. When a match is found, a jet of compressed air pops the piece off the conveyer belt and into a waiting bin.

After much experimentation, Mattheij rewrote the software (several times in fact) to accomplish a variety of basic tasks. At its core, the system takes images from a webcam and feeds them to a neural network to do the classification. Of course, the neural net needs to be "trained" by showing it lots of images, and telling it what those images represent. Mattheij's breakthrough was allowing the machine to effectively train itself, with guidance: Running pieces through allows the system to take its own photos, make a guess, and build on that guess. As long as Mattheij corrects the incorrect guesses, he ends up with a decent (and self-reinforcing) corpus of training data. As the machine continues running, it can rack up more training, allowing it to recognize a broad variety of pieces on the fly.

Here's another video, focusing on how the pieces move on conveyer belts (running at slow speed so puny humans can follow). You can also see the air jets in action:

In an email interview, Mattheij told Mental Floss that the system currently sorts LEGO bricks into more than 50 categories. It can also be run in a color-sorting mode to bin the parts across 12 color groups. (Thus at present you'd likely do a two-pass sort on the bricks: once for shape, then a separate pass for color.) He continues to refine the system, with a focus on making its recognition abilities faster. At some point down the line, he plans to make the software portion open source. You're on your own as far as building conveyer belts, bins, and so forth.

Check out Mattheij's writeup in two parts for more information. It starts with an overview of the story, followed up with a deep dive on the software. He's also tweeting about the project (among other things). And if you look around a bit, you'll find bulk LEGO brick auctions online—it's definitely a thing!

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Name the Author Based on the Character
May 23, 2017
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