Monte Carlo Skill Estimation for Darts

Abstract

In physical games, like darts, the ability of a player to accurately execute an intended action has a significant impact on their success. Determining this execution precision, or skill, for players is thus an important task. Knowledge of skill can be used for player feedback, computer-aided strategy decisions, game handicapping, and opponent modeling. Challenges to estimating player ability include getting precise feedback on executed actions as well as performing the estimation in a natural and user-friendly way. A previous method for estimating skill in darts overcomes the first challenge, but falls short on the second, requiring players to throw 50 darts at the center of the dartboard, which is not a common target in most darts games. In this paper we present an extension of this previous method that enables skill to be estimated when darts are aimed anywhere, not just the center of the dartboard. This method is then utilized to develop a much more efficient and adaptive skill estimation method which requires far fewer darts than the previous method. Experimental results demonstrate the advantages of the proposed approach and additional possible applications are discussed.