RuneScape Player versus Monster (PvM) encounters are fundamentally static. In most cases, Non-Player Character (NPC) behavior is structured the same across all instances of an encounter. Players execute predetermined sequences of abilities called rotations which have been optimized for speed and consistency. The PvM Encyclopedia offers publicly available rotations for every boss, although they are primarily human-generated through trial and error. We propose that RuneScape can be solved and we explore the potential of statistical methods to evaluate both individual actions as well as complete rotations. Player-derived damage in a rotation can be interpreted as discrete random variables with non-identical distributions. We find that the distribution of sequences of abilities obtain Gaussian characteristics over time and show that sufficiently long rotations can be approximated with a Gaussian Probability Mass Function (PMF). These methods are useful for comparative analysis of existing rotations. However, we aim to transcend intuition-based rotation optimization through reinforcement learning—and briefly examine the mathematical landscape of solving stochastic Markov Decision Processes (MDPs) for massively large spaces in the context of RuneScape combat.