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How Martina Navratilova Became the Smartest Player in the Game

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Tennis legends and best friends Martina Navratilova and Chris Evert faced each other a record 80 times, including 60 meetings in tournament finals, from 1973 to 1988. While Evert dominated the early stages of what would become one of the greatest rivalries in sports, Navratilova eventually solved Evert and retired as the most accomplished player in women’s tennis history. Navratilova has a wide-ranging support staff, and the use of rudimentary tennis analytics, to thank for that.

Untapped Potential

In 1975, 18-year-old Navratilova defected to the United States from her native Czechoslovakia. She had already established herself as a rising star in women’s tennis, but it wasn’t until three years later that she captured her first Grand Slam singles title by defeating Evert in the Wimbledon final. Navratilova defended her Wimbledon title against Evert the following year, but after watching Evert finish 1980 as the world No. 1 singles player, Navratilova reevaluated her career.

Team Navratilova

Navratilova began surrounding herself with a support staff of nutritionists, trainers and other specialists to improve her game. During the summer of 1981, she began training with basketball star Nancy Lieberman, who helped improve Navratilova’s physical and mental strength. The decision paid immediate dividends, as Navratilova won the Australian Open in 1981 and the French Open and Wimbledon titles in 1982.

At various times over the next few years, Navratilova’s support staff included championship bodybuilder Lynn Conkwright and tennis kinetics expert Rick Elstein.

“It was a lot like the circus coming to town,” one player told Johnette Howard, author of The Rivals, a detailed history of the Evert-Navratilova rivalry. “You didn’t know what or whom you’d see next.”


One of the most influential members of Team Navratilova was Miami-based nutritionist Dr. Robert Haas, who was brought on board shortly after Navratilova’s upset loss in the quarterfinals of the 1982 U.S. Open.

“Martina was always a good player, but her career was erratic,” Haas told People in 1982. “She felt she trained hard, but there was some element missing. Her own good sense told her it was probably diet.”

Under Haas’s watch, Navratilova cut out red meats, fats and sugars. In The Rivals, Howard writes that Haas performed tests on daily-drawn samples of Navratilova’s blood for 39 variables and planned her meals accordingly. Haas, who authored the bestseller Eat to Win, called Navratilova his Bionic Woman. Indeed, Navratilova seemed more machine than woman in 1983, when she won 86 of 87 matches.

“Some day she will become the first computer-programmed player in history but all she wants is to win more Wimbledon titles than anyone else and to be the world’s No. 1,” the Glasgow Herald wrote in 1983.

Some of Navratilova’s peers found her focus on diet ridiculous.

“You should eat what you want,” Hana Mandlikova told the Sarasota Daily-Herald in 1984. “I would go crazy if some computer told me what to eat.”

Haas’s influence went beyond crafting Navratilova’s diet, however. He would sit courtside and chart Martina’s strokes and Evert’s reactions on his laptop. Navratilova would study this information before a match.

“[Evert] was the only player we did the computer analysis with,” said Haas, who nicknamed his program ‘Smartina.’

‘We Were Right’

In 1984, Navratilova won a record 74 consecutive matches, including six against Evert in tournament finals. After losing 21 of her first 25 matches against Evert, Navratilova ended her career with a 43-37 advantage in the series. Seemingly defying time, Navratilova went 27 years between her first and record-tying 20th win at Wimbledon and she retired with 18 Grand Slams.

While Navratilova’s numbers speak for themselves, her use of specialists and analytics is another part of her legacy.

“Even though other people weren’t doing it, our thing was, sports science will be huge one day,” Haas said in The Rivals. “Having your own trainer, nutritionist, using computers for analysis and teaching will be very big one day. … I think Martina was able to establish a new model for athletes. People were, of course, skeptical and they laughed at us. But what are you going to do? As it turned out, we were right.”

What’s Next?

In 2012, ESPN the Magazine ranked the advancement of analytics in the major sports and tennis ranked second to last, ahead of boxing. Craig O’Shannessy, a leader in the tennis analytics field, is hopeful that will change.

“Analytics in tennis should be something that is a strength of our game,” O’Shannessy said at the 2012 MIT Sloan Sports Analytics Conference. “To a lot of observers, tennis is like pinball. The ball goes here, the ball goes there. There seems to be no rhyme or reason, but tennis is exactly the opposite. It’s 50% chess; you’re going to make a move or hit a ball to a certain part of the court, there’s going to be a natural reaction to that. It’s also 50% poker; the percentages of the game absolutely matter. I think tennis very much lends itself to analytics.”

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iStock // Ekaterina Minaeva
Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
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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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Cs California, Wikimedia Commons // CC BY-SA 3.0
How Experts Say We Should Stop a 'Zombie' Infection: Kill It With Fire
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Cs California, Wikimedia Commons // CC BY-SA 3.0

Scientists are known for being pretty cautious people. But sometimes, even the most careful of us need to burn some things to the ground. Immunologists have proposed a plan to burn large swaths of parkland in an attempt to wipe out disease, as The New York Times reports. They described the problem in the journal Microbiology and Molecular Biology Reviews.

Chronic wasting disease (CWD) is a gruesome infection that’s been destroying deer and elk herds across North America. Like bovine spongiform encephalopathy (BSE, better known as mad cow disease) and Creutzfeldt-Jakob disease, CWD is caused by damaged, contagious little proteins called prions. Although it's been half a century since CWD was first discovered, scientists are still scratching their heads about how it works, how it spreads, and if, like BSE, it could someday infect humans.

Paper co-author Mark Zabel, of the Prion Research Center at Colorado State University, says animals with CWD fade away slowly at first, losing weight and starting to act kind of spacey. But "they’re not hard to pick out at the end stage," he told The New York Times. "They have a vacant stare, they have a stumbling gait, their heads are drooping, their ears are down, you can see thick saliva dripping from their mouths. It’s like a true zombie disease."

CWD has already been spotted in 24 U.S. states. Some herds are already 50 percent infected, and that number is only growing.

Prion illnesses often travel from one infected individual to another, but CWD’s expansion was so rapid that scientists began to suspect it had more than one way of finding new animals to attack.

Sure enough, it did. As it turns out, the CWD prion doesn’t go down with its host-animal ship. Infected animals shed the prion in their urine, feces, and drool. Long after the sick deer has died, others can still contract CWD from the leaves they eat and the grass in which they stand.

As if that’s not bad enough, CWD has another trick up its sleeve: spontaneous generation. That is, it doesn’t take much damage to twist a healthy prion into a zombifying pathogen. The illness just pops up.

There are some treatments, including immersing infected tissue in an ozone bath. But that won't help when the problem is literally smeared across the landscape. "You cannot treat half of the continental United States with ozone," Zabel said.

And so, to combat this many-pronged assault on our wildlife, Zabel and his colleagues are getting aggressive. They recommend a controlled burn of infected areas of national parks in Colorado and Arkansas—a pilot study to determine if fire will be enough.

"If you eliminate the plants that have prions on the surface, that would be a huge step forward," he said. "I really don’t think it’s that crazy."

[h/t The New York Times]