Density-Based Outlier Detection of Atypical Timeout Request Contexts in EuroLeague Basketball

Timeouts in elite basketball are scarce, time-constrained interventions requested under evolving pressure and contextual imbalance. Although previous clustering approaches have identified recurrent timeout profiles, less attention has been paid to structurally atypical timeout request contexts that do not conform to dominant patterns. This study proposes a density-based outlier detection framework for EuroLeague basketball. Using play-by-play-derived data from 3743 games across 15 seasons, 25,031 timeout events were represented through differential and rate-adjusted indicators of scoring, shooting, free throws, rebounding, assists, turnovers, fouls, blocks, and score imbalance. Principal component analysis was used to construct a compact multivariate feature space. DBSCAN and OPTICS served as diagnostic density-based comparators, whereas HDBSCAN was used to derive observation-level outlier scores. Atypical contexts were defined using the highest outlier score events, with the top 10% group as the primary threshold. Flat density-based partitions did not support a compact timeout taxonomy, whereas HDBSCAN provided a stable and interpretable outlier score ranking. The most atypical timeouts were associated with larger score imbalances and rate-adjusted performance differentials, but an ablation analysis excluding score margin and score rate variables showed that the outlier ranking structure was not solely determined by direct scoreboard information.