AI and ML systems are turning the professional gambling world on its head.
The act of gambling on games of chance has been around for as long as
the games themselves. For as long as there's been money to be made
wagering on the uncertain outcomes of these events, bettors have been
leveraging mathematics to give them an edge on the house. As gaming has
moved from bookies and casinos into the digital realm, gamblers are
beginning to use modern computing techniques, especially AI and machine
learning, to increase their odds of winning.
But that betting blade cuts both ways, as researchers work to design
artificial intelligences capable of beating professional players at
their own game -- and even out-wagering sportsbooks.
The rate at which machine-learning AI systems have caught up and
overtaken the skills of their human opponents has accelerated at a
frightening pace in the past few years. IBM's Watson famously wiped the
floor against Jeopardy's master class of players in 2011. AlphaGo, from
Google's DeepMind division, beat European Go champion Fan Hui less than
three years ago, in 2016, before mopping up South Korean professional Go
player Lee Sedol two months later and posting a 60-0 record in online
matches against some of the best players on the planet a year later.
The year 2017 saw AlphaZero, an AlphaGo offshoot, demolish the world
champion chess program, Stockfish 8, in a 100-game matchup after
spending just four hours learning how to play. And while it took a bit
longer to master Dota 2 -- "3500 simulated years" longer -- OpenAI's
digital competitor managed to best the top amateur players in 2018.
AI has proved itself quite literally capable of beating humans at their
own games, but does that hold true when the chips are down and real
money is on the line? As the Libratus system from Carnegie Mellon
University showed in 2017, the answer remains a resounding yes.
It wasn't as much a poker tournament as a three week curb-stomping.
Professional poker players Jason Les, Dong Kyu Kim, Daniel McAulay and
Jimmy Chou spent 20 days playing 120,000 hands of heads-up, no-limit
Texas Hold'em against the AI but wound up losing by a margin of more
than $1.76 million in the end.
"In the start here, we lost the first day," Les told Engadget in 2018.
"Whatever -- not a big deal. And then we were losing, but then we fought
back up to nearly equal. We were feeling really confident!" But
confidence wasn't enough to halt the gambling juggernaut's advance. "It
just kept improving every single day, and we started going backwards and
backwards," he continued.
The loss stung for Les and Kim who, two years prior, beat the pants off
another poker AI, Claudico. That said, their drubbing wasn't nearly as
bad as what Lengpudashi, Libratus' second iteration, put World Series
veteran Alan Du and a team of engineers through later that year. Even
though the humans tried to apply machine-learning lessons gleaned from
the original tournament, the result was another bloodbath. The AI won by
a landslide after more than 36,000 hands were played.
"People think that bluffing is very human -- it turns out that's not
true," Libratus co-developer Noam Brown said in a statement. "A computer
can learn from experience that if it has a weak hand and it bluffs, it
can make more money."
Robert De Niro's Casino character, Sam "Ace" Rothstein, was a living
actuarial table for the Las Vegas mob. His encyclopedic knowledge of
sports variables allowed him to make betting (and winning) look easy.
Today, thanks to the rise of big data analytics and algorithmic AI,
virtually any schmuck at the local sportsbook can perform at Rothstein's
In fact, sports betting -- whether it's guessing who will win outright
or what the margin of victory will be -- is well suited for
machine-learning applications. As Rory Bunker and Fadi Thabtah of
Auckland University of Technology and the Nelson Marlborough Institute
of Technology, respectively, illustrate in their 2017 study, "A machine
learning framework for sport result prediction," estimating the outcome
of sports is a fairly straightforward affair for machine-learning
"One of the common machine learning (ML) tasks, which involves
predicting a target variable in previously unseen data, is
classification," the researchers write. "The aim of classification is to
predict a target variable (class) by building a classification model
based on a training dataset, and then utilizing that model to predict
the value of the class of test data."
With these models, clubs and managers can better size up their opponents
and formulate better strategies to win more matches, while sportsbooks
and individual bettors can more accurately estimate the game's outcome
ahead of time. "In sport prediction, large numbers of features can be
collected including the historical performance of the teams, results of
matches, and data on players," the researchers continue, "to help
different stakeholders understand the odds of winning or losing
What's more, these systems are in no way left wanting for training data.
Any number of Major League Baseball stats can easily be gleaned from
MLB.com, Baseball Reference, Sean Lahman's database and Retrosheet, for
example, just as advanced NHL stats can be found at Hockey Reference and
NFL data is available from NFL.com, ESPN or Pro Football Reference.
Even the ATP tour for tennis has begun collecting analytic data to
improve the game for players and fans alike, having teamed with Infosys
"In US sports, we're data junkies," ATP chair umpire Ali Nili explained
to Forbes in 2018. "If you look at an NBA game, afterward you look at
stats. If you're not a fan of the game they look like gibberish but a
6-year-old fan of the sport can translate it for you. The scope of data
collection these days has no end. Tennis is no different, we're getting
more data and using more and more information."
With so much data so freely available, it's no surprise that a number of
enterprising outfits are already leveraging AI and ML to perform
seemingly impossible feats of predictive sports analysis. UK-based
Stratagem, for example, is training AI to extract actionable patterns
from football, baseball and tennis matches and use that data to make
The company currently employs human analyzers to track matches, then
combines the data with odds from a variety of bookies to improve its
wagering, but it is also in the process of developing a deep neural
network to perform the same task in real time simply by watching a
broadcast feed of the match, according to The Verge.
"Football [soccer] is such a low-scoring game that you need to focus on
these sorts of metrics to make predictions," Stratagem founder Andreas
Koukorinis told The Verge in 2018. "If there's a shot on target from 30
yards with 11 people in front of the striker and that ends in a goal,
yes, it looks spectacular on TV, but it's not exciting for us. Because
if you repeat it 100 times the outcomes won't be the same. But if you
have Lionel Messi running down the pitch and he's one-on-one with the
goalie, the conversion rate on that is 80 percent. We look at what
created that situation. We try to take the randomness out, and look at
how good the teams are at what they're trying to do, which is generate
Even more impressive are the predictions made by Unanimous AI. In 2016,
the company released its "swarm intelligence" platform UNU, which
"enables groups to get together as online swarms... combining their
thoughts, opinions, and intuitions in real-time to answer questions,
make predictions, reach decisions... as a unified collective
intelligence," according to a press statement. Using UNU, Unanimous
managed a superfecta at that year's Kentucky Derby. That means the
company correctly predicted which four horses would cross the finish
line first, in order, beating 540-to-1 odds.
"We were reluctant to take this challenge," David Baltaxe, Unanimous'
chief information officer, said in a statement. "Nobody here knows
anything about horse racing, and it's notorious for being unpredictable.
Still, UNU surprises us again and again, so we recruited a swarm of
volunteers through an online ad. The whole thing took 20 minutes."
NFL: FEB 05 Super Bowl LI - Falcons v Patriots
The following February, Unanimous' system correctly predicted the
outcome of Super Bowl LI -- down to the precise 34-28 final score --
then went on to correctly pick 11 of that year's 18 Oscar winners.
Should the capabilities of AI and ML systems continue to improve -- and
there's no evidence to suggest that they won't -- this technology could
fundamentally upend the world of professional sports betting. However,
there are still limitations to what these systems can accomplish. For
example, current predictive systems don't have a means of accounting for
a team's mojo, how well the players "click" with one another or shifts
in a game's momentum.
"In some team sports, very good players don't do much that's
measurable," Adam Kucharski, author of The Perfect Bet: How Science and
Math Are Taking the Luck Out of Gambling, told Digital Trends last
February. "A very good player might just get into a good position.
Tackle rates won't show that. It's their positioning and intuitive
behavior that is having an influence."
But with future advancements in supplementary fields like machine
vision, these predictive engines will become even more potent. Say, for
example, Jonathan Toews from the Blackhawks crumples on the ice after a
vicious slash to the knee. Existing algorithms can't currently take his
injury into account and update their odds, at least until the results
from the team doctor are reported. But with machine vision, future
systems may well be able to suss out the seriousness of his injury
simply by gauging how badly he's grimacing.
Ultimately, this technology will reach a singularity wherein we'll be
using AI-driven analytics to place real-time bets on robo-death matches.
At least, with any luck we will.
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