<p>Psychological attributes rarely operate in isolation: coaches and practitioners reason about networks of related traits rather than single indicators. We analyze a new dataset of 164 female volleyball players from Italy’s C and D leagues that combines standardized psychological profiling with background information. To learn directed relationships among mixed-type variables (ordinal questionnaire scores, categorical demographics, and continuous indicators), we introduce a hybrid structure learning approach that combines a latent Gaussian copula representation with a constraint-based skeleton and a score-based refinement to produce a single directed acyclic graph. We also study a bootstrap-aggregated variant to improve stability. In simulation studies spanning sample size, sparsity, and dimensionality, the proposed method achieves lower structural error and higher edge recovery than recent copula-based alternatives while maintaining high specificity. Applied to volleyball, the learned network organizes mental skills around goal setting and self-confidence, with emotional arousal linking motivation and anxiety, and places key personality traits, most notably neuroticism and extraversion, upstream of skill clusters. Scenario analyses quantify how improvements in specific skills propagate through the network to shift preparation, confidence, and self-esteem. Overall, the approach provides an interpretable, data-driven framework for profiling psychological traits in sport and for supporting decisions in athlete development.</p>