Today, I pondered how AI is changing age-old, even centuries-old concepts about how people should make decisions in various situations, especially in sports and probably in business. It’s far more interesting than just automation. It’s more about fixing bugs in how people have long considered something to be correct and true.
For example, in the game of “Go,” it was believed for decades that invading the corner (3-3 point) was crude and premature. AI then proved otherwise: early capture of the corner is efficient, and chasing after “beautiful” shapes loses to pragmatic control over the center. Or consider the famous 37th move by AlphaGo in the match against Lee Sedol, which was very strange: people did not play that move because they thought it was “playing into empty space.” It was first taken for an AI mistake, but then recognized as brilliant (there are plenty of analyses on YT). In esports, OpenAI Five demonstrated that aggressive early buyback of fallen heroes in “Dota,” which people considered a waste of gold, works.
Pure mathematics almost erased the mid-range shot from the NBA: it has an accuracy of about 40-42% and yields ~0.8 points per attempt, while a three-point shot with even 35% accuracy brings 1.05 points per attempt, and clubs have restructured for pure profit. Well, this is not AI, but mathematics and statistics. The under-basket shot (lay-up/dunk) turned out to be statistically the most effective.
In soccer, there’s the xG – expected goals metric; AI debunked shots from 35 meters and from outside the penalty area as ineffective (chance of scoring ~5% and 20% respectively) and ultimately teams patiently bring the ball into the penalty area, where the xG of the shot increases to 15-40%. It turns out, DeepMind had a project with Liverpool, a system advising coaches on corners – TacticAI. Expert assessors in 90% of cases preferred TacticAI’s recommendations over the tactical setups used in practice.
So, interestingly, if this continues, will a team or athlete using more powerful AI have an advantage due to more successful methods than a team that does not have such knowledge? Will AI game methods be so complex that they can’t be “stolen” to another team through outside observation – just like in the case with Go?