Modeling Psychological Profiles in Volleyball via Mixed-Type Bayesian Networks
This provides an interpretable, data-driven framework for profiling psychological traits in sport, aiding decision support in athlete development, but it is incremental as it builds on existing methods for mixed-type Bayesian networks.
The authors tackled the problem of modeling psychological profiles in volleyball by analyzing a new dataset of 164 female players and introducing latent MMHC, a hybrid structure learner for mixed-type variables, which achieved lower structural Hamming distance and higher edge recall in simulations compared to recent methods. The learned network organized mental skills around goal setting and self-confidence, linking emotional arousal to motivation and anxiety, and quantified how improvements in specific skills propagate through the network.
Psychological attributes rarely operate in isolation: coaches reason about networks of related traits. 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, continuous indicators), we introduce latent MMHC, a hybrid structure learner that couples a latent Gaussian copula and a constraint-based skeleton with a constrained score-based refinement to return a single DAG. We also study a bootstrap-aggregated variant for stability. In simulations spanning sample size, sparsity, and dimension, latent Max-Min Hill-Climbing (MMHC) attains lower structural Hamming distance and higher edge recall than recent copula-based learners 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 locates Big-Five traits (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. The approach provides an interpretable, data-driven framework for profiling psychological traits in sport and for decision support in athlete development.