Modeling Psychological Profiles in Volleyball via Mixed-Type Bayesian Networks

2025-09-26

AI Summary

This study addresses the challenge of modeling mixed-variable psychological traits in volleyball players—encompassing ordinal Likert-scale items, categorical demographic variables, and continuous physiological/behavioral indicators. We propose latent MMHC, a novel method integrating latent-variable Gaussian copula models with constrained skeleton construction, Max-Min Hill-Climbing structure learning, score-based refinement, and Bootstrap aggregation for enhanced stability. Its key innovation lies in being the first to embed latent-variable mechanisms into hybrid Bayesian network learning for mixed data. In simulations, latent MMHC significantly reduces structural Hamming distance (−32.7%) and improves edge recall (+28.4%) versus baselines. Empirical analysis on athlete data identifies self-efficacy and goal-setting as central hubs, while neuroticism and extraversion occupy upstream driver positions; quantitative path analysis demonstrates that targeted improvement in these traits propagates through multiple pathways to elevate overall psychological skill levels.

Technology Category

• Machine Learning: Bayesian Learning
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Modeling psychological trait networks in volleyball players
• Learning directed relationships among mixed-type psychological variables
• Providing interpretable framework for athlete psychological profiling

Innovation

• Latent MMHC combines Gaussian copula with constraint-based learning
• Bootstrap aggregation enhances network structure stability
• Learned Bayesian network quantifies psychological trait interactions