As online gaming and esports platforms grow in popularity and complexity, concerns over account security and user authentication are increasing. This study explores the use of behavioral biometrics to identify individual players based solely on their in-game actions in Counter-Strike: Global Offensive (CS:GO) using data from professional tournament matches, with a particular focus on the impact of sequence length on identification performance. We propose a machine learning framework that aggregates gameplay data into fixed length sequences and extracts interpretable features related to movement, aiming, and economic behavior. To investigate how temporal context influences model performance, we evaluate binary classifiers across a range of sequence durations, from a few seconds to several minutes. For each duration, behavioral data is aggregated into a single feature vector using statistical summaries, such as mean and count, to represent player behavior over the window. Importantly, the window sizes overlap every five seconds of gameplay, generating more data for the model and allowing for finer-grained analysis. To assess generalizability, models are tested exclusively on unseen matches occurring after the training period. Results show that identification accuracy is sensitive to sequence length, with longer sequences generally resulting in higher predictions. This case study contributes to the development of behavioral authentication systems in gaming by demonstrating how sequence length and feature design influence player identification performance.