Signals of Struggle: Detecting Player Difficulties Using Machine Learning

Abstract

Struggle is an inevitable part of gameplay, and it’s often what makes games meaningful, rewarding, and fun. Still, some moments of difficulty spiral into frustration or confusion, and can cause players to quit entirely. Being able to detect and interpret struggle is thus essential for designing better player experiences, but identifying these moments remains challenging. We present a machine learning approach for detecting player struggle in real time using gameplay telemetry. Using three quests built in Terraria that each emphasize a different set of game mechanics – gathering, combat, or crafting – we collected data on how players interact with different systems and had them annotate where they encountered difficulty. Using this dataset, we trained Random Forest classifiers and evaluated model performance across different feature sets, window sizes, and step sizes. Our results show that such a model can successfully identify whether unseen players are experiencing struggle in the crafting quest, while the other quests proved more difficult. We also tested whether a model could classify the type of struggle as cognitive or performative, and found promising results for the crafting and combat quests. Our findings demonstrate the potential of using player telemetry to detect struggle, laying the groundwork for future adaptive systems that offer real-time, context-aware support tailored to individual player needs.