Commentary: The house always wins in gambling, and the house is getting even tougher through machine learning.
“On the Internet, nobody knows you are a dog,” is easily one of the top 10 New Yorker cartoons of all time. Why? Because it captured the upsides and downsides of online anonymity. All good, right? Well, maybe. What if you are online, and you like to gamble? Who’s on the other side? You have no idea, and that might be more of a problem than you might suspect.
SEE: Artificial Intelligence Ethics Policy (TechRepublic Premium)
For one thing, more and more you may be betting against machine learning algorithms, and if the “house always wins” in the offline world, guess what? It’s even worse in an ML/artificial intelligence-driven online gambling world. Still, understanding the odds helps you understand the potential risks involved as the gambling industry consolidates. So, let’s take a look at how one person used ML to fight back.
A “house” made of machines
Go to any casino in person and the best odds you can get range from the house taking from 1.5% to 5% off the top (craps, baccarat, slot machines and Big Six can take more than 20%). You are essentially renting access to their game. The money you bet allows you to earn back about 95 to 98 cents on the dollar (the card game blackjack, by the way, is your best bet). But any way you choose, over time you almost certainly go broke. Why? Because … math.
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The casino industry will argue that AI/ML helps gamblers by identifying cheats faster. That might be true, so far as it goes, but there is another side to this argument.
I came across an intriguing example of a regular person using ML to see if they could do better at the racetrack betting on the ponies (a $15 billion annual industry in the U.S.). In this example, the regular person is Craig Smith, a noted former New York Times foreign correspondent who left journalism to explore AI/ML.
To test the efficacy of ML and horse racing, he tried Akkio, a no-code ML service I’ve written about a few times before. His goal? To show how their approach can foster AI adoption and how it is already improving productivity in mundane but important matters. Akkio is not designed for gambling but rather for business analysts who want insights quickly into their data without hiring developers and data scientists. Turns out it’s also helpful for Smith’s purposes.
So much so, in fact, that Smith doubled his money using an ML recommendation model Akkio created in minutes. It’s a fascinating read. It also sheds light on the dark side of ML and gambling.
Winners and losers
In his article, Smith interviewed Chris Rossi. He’s the horse betting expert who helped build a thoroughbred data system that was eventually bought by the horse racing information conglomerate DRF (Daily Racing Form). He now consults for people in the horse-racing world, including what he described as teams of quantitative analysts who use machine learning to game the races betting billions annually and making big bucks–some of it from volume rebates on losing bets by the tracks who encourage the practice.
“Horse racing gambling is basically the suckers against the quants,” Rossi said. “And the quants are kicking the —- out of the suckers.”
Not many years ago, sports betting sat in a legally dubious place in the U.S. Then in 2018 the U.S. Supreme Court cleared the way for states to legalize the practice, striking down a 1992 federal law that largely restricted gambling and sports books to Nevada. That decision arrived just in the nick of time. During the pandemic, as casinos shuttered their doors and consumers looked for activities to eat up their free time, online gambling and sports betting took off. Shares of DraftKings, which went public via a SPAC merger, for instance, have risen 350% since the start of the coronavirus’ spread, valuing the company at about $22 billion.
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DraftKings has also been looking to diversify away from business that concentrates around the sports season. The online betting customer is apparently more valuable than a sports betting customer.
More recently, MGM Resorts International, a major Las Vegas player, sought to acquire Entain for about $11.1 billion in January, though the latter rebuffed the bid for being too low. Caesars Entertainment in September announced plans to acquire U.K.-based online betting business, William Hill, for about $4 billion. And to drive the point home on just how hot the space has gotten, media brand Sports Illustrated has gotten into the online sports betting space.
All of this money sits awkwardly next to rising use of ML. Yes, ML can help clean up online gambling by kicking off cheaters. But it can also be the other side of the bet you are making. As one commentator noted, “AI can analyze player behavior and create highly customized game suggestions.” Such customized gaming may make it more engaging for gamblers to keep betting, but don’t think for a minute that it will help them to win. Online or offline, the house always wins. If anything, the new ML-driven gambling future just means gamblers may have incentive to gamble longer … and lose more.
Could you, like Smith, put ML to work on your behalf? Sure. But at some point, the house wins, and the house will improve its use of ML faster than any average bettor can.
Disclosure: I work for MongoDB, but the views expressed herein are mine.