Listen to Andreas Koukorinis, founder of UK sports betting company Stratagem, and you’d be forgiven for thinking that soccer games are some of the most predictable events on Earth. “They’re short duration, repeatable, with fixed rules,” Koukorinis tells The Verge. “So if you observe 100,000 games, there are patterns there you can take out.”
The mission of Koukorinis’ company is simple: find these patterns and make money off them. Stratagem does this either by selling the data it collects to professional gamblers and bookmakers, or by keeping it and making its own wagers. To fund these wagers, the firm is raising money for a £25 million ($32 million) sports betting fund that it’s positioning as an investment alternative to traditional hedge funds. In other words, Stratagem hopes rich people will give Stratagem their money. The company will gamble with it using its proprietary data, and, if all goes to plan, everyone ends up just that little bit richer.
It’s a familiar story, but Stratagem is adding a little something extra to sweeten the pot: artificial intelligence.
At the moment, the company uses teams of human analysts spread out around the globe to report back on the various sporting leagues it bets on. This information is combined with detailed data about the odds available from various bookmakers to give Stratagem an edge over the average punter. But, in the future, it wants computers to do the analysis for it. It already uses machine learning to analyze some of its data (working out the best time to place a bet, for example), but it’s also developing AI tools that can analyze sporting events in real time, drawing out data that will help predict which team will win.
Stratagem is using deep neural networks to achieve this task — the same technology that’s enchanted Silicon Valley’s biggest firms. It’s a good fit, since this is a tool that’s well-suited for analyzing vast pots of data. As Koukorinis points out, when analyzing sports, there’s a hell of a lot data to learn from. The company’s software is currently absorbing thousands of hours of sporting fixtures to teach it patterns of failure and success, and the end goal is to create an AI that can watch a range of a half-dozen different sporting events simultaneously on live TV, extracting insights as it does.
At the moment, though, Strategem is starting small. It’s focusing on just a few sports (soccer, basketball, and tennis) and a few metrics (like goal chances in soccer). At the company’s London offices, home to around 30 employees including ex-bankers and programmers, we’re shown the fledgling neural nets for soccer games in action. On-screen, the output is similar to what you might see from the live feed of a self-driving car. But instead of the computer highlighting stop signs and pedestrians as it scans the road ahead, it’s drawing a box around Zlatan Ibrahimović as he charges at the goal, dragging defenders in his wake.
Stratagem’s AI makes its calculations watching a standard, broadcast feed of the match. (Pro: it’s readily accessible. Con: it has to learn not to analyze the replays.) It tracks the ball and the players, identifying which team they’re on based on the color of their kits. The lines of the pitch are also highlighted, and all this data is transformed into a 2D map of the whole game. From this viewpoint, the software studies matches like an armchair general: it identifies what it thinks are goal-scoring chances, or the moments where the configuration of players looks right for someone to take a shot and score.
“Football is such a low-scoring game that you need to focus on these sorts of metrics to make predictions,” says Koukorinis. “If there’s a short 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 goal-scoring opportunities.”
Whether or not counting goal-scoring opportunities is the best way to rank teams is difficult to say. Stratagem says it’s a metric that’s popular with professional gamblers, but they — and the company — weigh it with a lot of other factors before deciding how to bet. Stratagem also notes that the opportunities identified by its AI don’t consistently line up with those spotted by humans. Right now, the computer gets it correct about 50 percent of the time. Despite this, the company say its current betting models (which it develops for soccer, but also basketball and tennis) are right more than enough times for it to make a steady return, though they won’t share precise figures.
At the moment, Stratagem generates most of its data about goal-scoring opportunities and other metrics the old-fashioned way: using a team of 65 human analysts who write detailed match reports. The company’s AI would automate some of this process and speed it up significantly. (Each match report takes about three hours to write.) Some forms of data-gathering would still rely on humans, however.
A key task for the company’s agents is finding out a team’s starting lineup before it’s formally announced. (This is a major driver of pre-game betting odds, says Koukorinis, and knowing in advance helps you beat the market.) Acquiring this sort of information isn’t easy. It means finding sources at a club, building up a relationship, and knowing the right people to call on match day. Chatbots just aren’t up to the job yet.
Machine vision, though, is really just one element of Stratagem’s AI business plan. It already applies machine learning to more mundane facets of betting — like working out the best time to place a bet in any particular market. In this regard, what the company is doing is no different from many other hedge funds, which for decades have been using machine learning to come up with new ways to trade. Most funds blend human analysis with computer expertise, but at least one is run completely by decisions generated by artificial intelligence.
However, simply adding more computers to the mix isn’t always a recipe for success. There’s data showing that if you want to make the most out of your money, it’s better to just invest in the top-performing stocks of the S&P 500, rather than sign up for an AI hedge fund. That’s not the best sign that Stratagem’s sports-betting fund will offer good returns, especially when such funds are already controversial.
In 2012, a sports-betting fund set up by UK firm Centaur Holdings, collapsed just two years after it launched. It lost $2.5 million after promising investors returns of 15 to 20 percent. To critics, operations like this are just borrowing the trappings of traditional funds to make gambling look more like investing.
David Stevenson, director of finance research company AltFi, told The Verge that there’s nothing essentially wrong with these funds, but they need to be thought of as their own category. “I don’t particularly doubt it’s great fun [to invest in one] if you like sports and a bit of betting,” said Stevenson. “But don’t qualify it with the term ‘investment,’ because investment, by its nature, has to be something you can predict over the long run.”
Stevenson also notes that AI hedge funds that are successful — those that “torture the math within an inch of its life” to eek out small but predictable profits — tend not to seek outside investment at all. They prefer keeping the money to themselves. “I treat most things that combine the acronym ‘AI’ and the word ‘investing’ with an enormous dessert spoon of salt,” he said.
Whether or not Stratagem’s AI can deliver insights that make sporting events as predictable as the tides remains to be seen, but the company’s investment in artificial intelligence does have other uses. For starters, it can attract investors and customers looking for an edge in the world of gambling. It can also automate work that’s currently done by the company’s human employees and make it cheaper. As with other businesses that are using AI, it’s these smaller gains that might prove to be most reliable. After all, small, reliable gains make for a good investment.