Date of Award


Degree Name

MS in Statistics




College of Science and Mathematics


Kevin Ross

Advisor Department


Advisor College

College of Science and Mathematics


This study explores optimal strategies for maximizing scores and winning in the popular dice game Qwixx, analyzing both single and multiplayer gameplay scenarios. Through extensive simulations, various strategies were tested and compared, including a scorebased approach that uses a formula tuned by MCMC random walks, and race-to-lock approaches which use absorbing Markov chain qualities of individual score sheet rows to find ways to lock rows as quickly as possible. Results indicate that employing a scorebased strategy, considering gap, count, position, skip, and likelihood scores, significantly improves performance in single player games, while move restrictions based on specific dice roll sums in the race-to-lock strategy were found to enhance winning and scoring points in multiplayer games. While the results do not achieve the optimal scores attained by prior informal work, the study provides valuable insights into decision-making processes and gameplay optimization for Qwixx enthusiasts, offering practical guidance for players seeking to enhance their performance and strategic prowess in the game. It also serves as a lesson for how to approach optimization problems in the future.