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How Math and Lasers Can Make You a Better Golfer

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A few years ago, number crunchers and stat geeks infiltrated the MLB front offices and changed the way America’s pastime is played. Now they’re doing it to Scotland’s.

In 1999, the PGA launched the stat-gathering, laser-wielding ShotLink system. Now volunteers strategically surround each hole at each tournament, surveying the landscape with camera-like lasers. Those lasers track and measure every shot with insane accuracy—the margin of error is just a few centimeters.

They’ve sparked golf’s own “Moneyball” era. New information is flooding the scene, and TV broadcasters don’t have to fill airtime with stuffy stories about their grandchildren as they wait for measurements anymore. Cameramen don’t have to guess where the best spots to film are. And number junkies don’t have to settle for the same stale stats that have been staples of the game for decades—they can cook up new ones.

Nearly 600 of them, actually.

The flurry of information is changing how the pros play. Players can track every nuance of their game with unseen specificity: If Tiger wants to know how he does in bunkers 30 feet from the hole, there’s a stat for that. If Lefty wants to evaluate his approach shots, there’s a stat for that.

Players can pinpoint problems in their game like never before, giving them a better idea of what to practice—and what kind of shots to avoid. A player who once had a fuzzy idea of what caused his putting woes can finally say, “Ah! Those 16 footers going downhill are to blame.” And then he can attack the problem.

No wonder number crunchers are becoming as common as caddies. Duffers like Luke Donald and Stuart Appleby are becoming vocal data junkies, and by hiring a few number nerds, they’re increasing their chances of earning more dough.

Here’s how. Two professors at Penn found that players were more likely to miss a birdie putt than a putt for par—even if the distance was the same. Why? Because of a phenomenon called “loss aversion.” People prefer avoiding losses to making gains, and golfers are no different. They're more hesitant when shooting for birdie—a habit that increases their chance of leaving a shot short. According to Sean Martin at Golf Week, “If a top-20 player in 2008 was able to overcome this bias, he could increase his earnings by more than $1 million.”

Although ShotLink has made players more aware of their mental roadblocks, the game hasn’t gotten easier. More golf course designers are using ShotLink data to make the pin harder to find. By studying shot patterns, designers can manipulate the tee box, mowing patterns, and hazards to give pros more gray hairs.

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iStock // Ekaterina Minaeva
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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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Nick Briggs/Comic Relief
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What Happened to Jamie and Aurelia From Love Actually?
May 26, 2017
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Nick Briggs/Comic Relief

Fans of the romantic-comedy Love Actually recently got a bonus reunion in the form of Red Nose Day Actually, a short charity special that gave audiences a peek at where their favorite characters ended up almost 15 years later.

One of the most improbable pairings from the original film was between Jamie (Colin Firth) and Aurelia (Lúcia Moniz), who fell in love despite almost no shared vocabulary. Jamie is English, and Aurelia is Portuguese, and they know just enough of each other’s native tongues for Jamie to propose and Aurelia to accept.

A decade and a half on, they have both improved their knowledge of each other’s languages—if not perfectly, in Jamie’s case. But apparently, their love is much stronger than his grasp on Portuguese grammar, because they’ve got three bilingual kids and another on the way. (And still enjoy having important romantic moments in the car.)

In 2015, Love Actually script editor Emma Freud revealed via Twitter what happened between Karen and Harry (Emma Thompson and Alan Rickman, who passed away last year). Most of the other couples get happy endings in the short—even if Hugh Grant's character hasn't gotten any better at dancing.

[h/t TV Guide]

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