Fitsner, Elise2025-08-072025-08-072025https://hdl.handle.net/1828/22561This project looks at how unsupervised machine learning (specifically, clustering data using a Gaussian mixture model) can be used to try to identify different player archetypes in Counter-Strike: Global Offensive, using only data that relates to the spatial behaviour of the players. We found that within each side, a player's individual playstyle and the style of the round they were playing seem to have had more influence on the clustering than which map they were playing on, and we were able to identify four distinct styles of play on each side. In future work, this approach could be applied to areas such as cheat detection, improving video game map design, and quantifying effective play patterns to support skill development and training.enmachine learningplayer typologyvideo gameshuman mobilityPosterDepartment of Computer Science