• EM algorithm;
  • Importance sampling;
  • Monte Carlo methods;
  • Statistics of games

Summary.  Darts is enjoyed both as a pub game and as a professional competitive activity. Yet most players aim for the highest scoring region of the board, regardless of their level of skill. By modelling a dart throw as a two-dimensional Gaussian random variable, we show that this is not always the optimal strategy. We develop a method, using the EM algorithm, for a player to obtain a personalized heat map, where the bright regions correspond to the aiming locations with high (expected) pay-offs. This method does not depend in any way on our Gaussian assumption, and we discuss alternative models as well.