  In this paper, I formalize intelligence measurement in games by introducing
mechanisms that assign a real number -- interpreted as an intelligence score --
to each player in a game. This score quantifies the ex-post strategic ability
of the players based on empirically observable information, such as the actions
of the players, the game's outcome, strength of the players, and a reference
oracle machine such as a chess-playing artificial intelligence system.
Specifically, I introduce two main concepts: first, the Game Intelligence (GI)
mechanism, which quantifies a player's intelligence in a game by considering
not only the game's outcome but also the "mistakes" made during the game
according to the reference machine's intelligence. Second, I define
gamingproofness, a practical and computational concept of strategyproofness. To
illustrate the GI mechanism, I apply it to an extensive dataset comprising over
a billion chess moves, including over a million moves made by top 20
grandmasters in history. Notably, Magnus Carlsen emerges with the highest GI
score among all world championship games included in the dataset. In
machine-vs-machine games, the well-known chess engine Stockfish comes out on
top.
