Today when you finish a game on your free chess app you can tap an Analysis button and see a move-by-move breakdown of your mistakes. The app will even tell you how bad each mistake was. You can tap on any move to see how alternative lines would have played out. This is possible because the chess engine can consider millions of possible future positions per second, where humans can only consider a handful. It distills the results of that vast counterfactual fanout into an interactive annotated board that can provide concise answers, oracle-like, to the questions you pose it, like “Who’s winning in this position? What’s the best response? What’s generally better here, having a bishop and knight or two knights together?”

Where in the old days you could only know how good you were at chess relative to other players, and only in terms of wins and losses, today it’s possible to evaluate different parts of your game—openings, tactics, the endgame—and compare your performance in fine quantitative detail to a computer model of ideal play. To probe weaknesses, the computer can endlessly feed you specific training situations just on the far edge of your ability.

Studying chess in the era of cheap computation is like being able to slow the Matrix down to bullet-time: You can avoid bad futures because you can actually see them. A chess engine can easily tell you that a given move will be a mistake, and it can show you why, say by pointing to the moment where you’d lose a key piece. By training your own vision against the computer’s this way, players are developing a deeper intuition about the game than had ever before been possible. “Chess education today revolves around learning how to learn from the computer,” writes the economist and blogger Tyler Cowen. “There are many more chess prodigies than ever before, and they mature at a more rapid pace.” Forty years ago, there were only two players with a rating above 2700; there are now 44 of them.

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As hardware has gotten exponentially more powerful, the thing that happened to chess has started to become possible in, well, things that aren’t board games. Like basketball. Basketball is a game, too, but it takes place in three dimensions. “Moves” in basketball aren’t discrete decisions that can be expressed in algebraic notation like Nf3 (“Knight moves to the f3 square,” a typical early move in a chess game)—they involve human players flinging and dribbling a ball through space. But technical advances like the miniaturization of cameras, the ability to cheaply store and process of thousands of hours of video, the development of computer vision algorithms that can track moving objects across frames and at different angles, and so on, have allowed us to break basketball down to its essence.

The SportVU system used by the NBA tracks every single player’s movement and the movement of the ball in every game of the season. It turns videos of basketball games into the kind of Xs-and-Os diagrams a coach might draw courtside on a whiteboard. But here, the diagrams are dynamic: They actually move in time, capturing every pass and shot in the game.