At the moment, though, Stratagem 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.
Two monitors show feeds from the company’s neural networks, currently analyzing an archive game.
Photo by James Vincent / The Verge
“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.