CLOSE
Original image
Getty Images

5 Heartbreaking (or Miraculous) Moments in Sports Betting History

Original image
Getty Images

A controversial call at the end of Monday Night Football cost Green Bay a victory last night, and the referee's decision swung an estimated $150-$250 million in the gambling world. Every once in a while, a play or call will have serious repercussions for bettors. Here are a few tales that will make the gambling community wince.

1. Chris Duhon's Heave

As the clock dwindled on Duke and UConn's 2004 Final Four matchup, Blue Devils fans had to hang their heads. Their underdog squad was going to lose 79-75, thereby ending their title hopes. Worse still, various betting lines on the game were giving the Devils between two and three points, so Duke fans who had bet on the game were going to endure a double punch to the stomach: their team was losing, and so were their wallets. On the final play of the game, though, senior guard Chris Duhon chucked a 38-foot three-pointer off one leg as time expired. The shot banked in to make the score 79-78. It was cold comfort for Duhon and his teammates. However, it was great news for anyone who'd wagered on Duke. Since the underdogs covered the spread on the meaningless play, they all won their bets. The shot swung at least an estimated $30 million to Duke bettors, with some estimates ranging as high as $100 million.

2. Robin Ventura's Grand-Slam Single

Getty Images

Game Five of the 1999 National League Championship Series between the Atlanta Braves and the New York Mets felt like it might never end. The game was tied 2-2 in the top of the 15th inning before Mets reliever Octavio Dotel gave up a run to stake the Braves to a 3-2 lead. In the bottom of the 15th, though, the Mets managed to tie the game at 3-3 when catcher Todd Pratt drew a bases-loaded walk. The next batter, Robin Ventura, clubbed a pitch over the Shea Stadium fence for a walk-off grand slam. The Mets were going to win the game 7-3. Only there was a holdup: when Ventura got between first and second base, his teammates mobbed him in a raucous celebration. He never got to finish his home run trot or even touch second base. Since Ventura only touched first, the official scorer didn't give him a home run and the four RBIs he had coming from the slam. Instead, Ventura got credit for a single and one RBI.

The "grand slam single" was obviously enough to give the Mets the 4-3 win, but it caused a sticky situation in Vegas. The over/under (combined number of runs scored by both teams) on which bettors had wagered was 7.5. If the Mets had gotten all four runs Ventura's slam should have scored, the total number of runs would have been 10, and bettors who took the over would have won. Instead, the 4-3 final score resulted in the under bettors winning. Unfortunately for the sports books, it wasn't immediately clear that the Mets weren't going to get those three extra runs, so NBC posted the score as 7-3 on its broadcast. According to the Las Vegas Review-Journal, some casinos started paying out on "over" bets when the 7-3 score was initially posted and didn't stop until NBC announcer Bob Costas told viewers the correct score five minutes or so later. As a result, if you were quick enough, this game did the seemingly impossible: it paid out for both the over and the under.

3. The Machine Throws a Wrench at Gamblers

Getty Images

When the Los Angeles Lakers played the San Antonio Spurs in the 2008 Western Conference Finals, it seemed pretty obvious that Kobe and company were going to earn their first NBA Finals trip since 2004. At the end of Game 5, the Lakers had all but clinched a four-games-to-one series victory. They had the ball with a 97-92 lead and needed only to run out the clock and get ready for the Finals. Instead of the customary aimless dribbling to wind things down, though, backup guard Sasha "The Machine" Vujacic tossed off a three-pointer as time expired. Final score: 100-92. The bad news for Vegas? The line was Lakers -7.5, which meant that Vujacic's shot covered the spread. Sports business reporter Darren Rovell wrote that given the large amount of worldwide action on the playoff game, the shot may have swung $100 million in bets.

4. Florida-Miami, 2008

Getty Images

This Sunshine State rivalry has never been short on hard feelings, but the animosity between the two traditional powers and their fans peaked following their 2008 meeting. Florida was widely considered one of the best teams in the country, while the Canes looked like they might have another down year. As a result, the spread was big; the Gators were 21-point favorites. The game played out about as expected with Florida laying down a pretty firm drubbing. With about a minute left, the Gators had the ball and a 23-3 lead. Ordinarily, teams would just run out the clock in this situation and enjoy the victory. Not Florida coach Urban Meyer, though. The Gators kept running plays in an attempt to score. Eventually the drive stopped 12 yards short of the goal line, and kicker Jonathan Phillips poked in a 29-yard field goal with 25 seconds left to move the score to 26-3. Hurricanes coaches and fans were upset with what they saw as a classless attempt to run up the score and cover the spread, but Meyer claimed he just wanted to get the young kicker some late-game experience before the meat of the Gators' schedule. Either way, Florida covered the spread on the meaningless kick, which must have made countless Gator bettors happy.

5. No Touchdown for Troy

Getty Images

The NFL saw a great meaningless gambling moment in 2008, when Steelers safety Troy Polamalu seemed to scored a touchdown on the final play of Pittsburgh's game against San Diego. While it looked like the lusciously locked DB had successfully nabbed a fumbled lateral and scampered into the end zone, the referee somewhat confusingly allowed, then disallowed the score. The play had no impact on the game's outcome (Pittsburgh still won 11-10), but the gambling repercussions were serious. The Steelers had been 4.5-point favorites heading into the game, and if Polamalu's score counted, anyone who bet on Pittsburgh and laid the points would have won. Instead, they lost their bets, which cost these bettors an estimated $64 million, and that's not to mention those fantasy owners (like this writer) who started the Pittsburgh D and lost a fumble recovery and score from the reversal.

Portions of this story appeared here in 2008.

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

Original image
iStock
Animals
arrow
Scientists Think They Know How Whales Got So Big
May 24, 2017
Original image
iStock

It can be difficult to understand how enormous the blue whale—the largest animal to ever exist—really is. The mammal can measure up to 105 feet long, have a tongue that can weigh as much as an elephant, and have a massive, golf cart–sized heart powering a 200-ton frame. But while the blue whale might currently be the Andre the Giant of the sea, it wasn’t always so imposing.

For the majority of the 30 million years that baleen whales (the blue whale is one) have occupied the Earth, the mammals usually topped off at roughly 30 feet in length. It wasn’t until about 3 million years ago that the clade of whales experienced an evolutionary growth spurt, tripling in size. And scientists haven’t had any concrete idea why, Wired reports.

A study published in the journal Proceedings of the Royal Society B might help change that. Researchers examined fossil records and studied phylogenetic models (evolutionary relationships) among baleen whales, and found some evidence that climate change may have been the catalyst for turning the large animals into behemoths.

As the ice ages wore on and oceans were receiving nutrient-rich runoff, the whales encountered an increasing number of krill—the small, shrimp-like creatures that provided a food source—resulting from upwelling waters. The more they ate, the more they grew, and their bodies adapted over time. Their mouths grew larger and their fat stores increased, helping them to fuel longer migrations to additional food-enriched areas. Today blue whales eat up to four tons of krill every day.

If climate change set the ancestors of the blue whale on the path to its enormous size today, the study invites the question of what it might do to them in the future. Changes in ocean currents or temperature could alter the amount of available nutrients to whales, cutting off their food supply. With demand for whale oil in the 1900s having already dented their numbers, scientists are hoping that further shifts in their oceanic ecosystem won’t relegate them to history.

[h/t Wired]

SECTIONS
BIG QUESTIONS
BIG QUESTIONS
WEATHER WATCH
BE THE CHANGE
JOB SECRETS
QUIZZES
WORLD WAR 1
SMART SHOPPING
STONES, BONES, & WRECKS
#TBT
THE PRESIDENTS
WORDS
RETROBITUARIES