Gaze-based Assessment of Expertise in Chess

Chess is known worldwide and has acquired a deep cultural significance. It is recognized as a sport by the International Olympic Committee, and this sport has around 600 million players worldwide. Since chess is a strategic game, it requires an estimate of the future actions of the opposite player and the integration of these estimates into the own planning. This makes it a perfect sport to study expertise classification based on eye tracking data, since the players sit still and only have visual information about the actions of the opposite player. We conducted a small study and investigated different features to estimate the outcome of a chess game. The level of expertise of the players was determined by the number of years they had played chess already. We found, that transfer learning seems to be a promising approach since it can be trained on larger amounts of data without annotations.