Exploring the Relationship Between Key Factors and Victory and Defeat in Volleyball Matches using Bayesian Network Modeling

Abstract

Identifying key factors contributing to victory and defeat in volleyball matches using Bayesian network modeling involves probabilistic graphical modeling to analyze the interrelationships among various factors and their influence on match outcomes. By integrating data on player performance, team strategies, match conditions, and opponent characteristics, Bayesian networks can uncover complex dependencies and predict the likelihood of winning or losing based on different scenarios. The research conducted on exploring the relationship between key factors and victory and defeat in volleyball matches using Bayesian network modeling. Through the utilization of Bayesian network modeling, this study aims to elucidate the intricate interplay between different variables and their impact on match outcomes in the context of volleyball. The research employs a Hidden Markov Process Bayesian Network (HMPBN) to model the temporal dependencies and uncertainties inherent in volleyball matches. The dataset comprises a comprehensive collection of volleyball match data, encompassing various observable variables such as points scored, successful serves, defensive plays, and more. The simulation setup involves training the HMPBN model on a dataset consisting of 1000 instances and evaluating its performance on a separate test dataset of 500 instances. The model parameters, including the transition matrix, emission matrix, and initial state distribution, are determined through rigorous statistical estimation techniques. The results of the simulation experiments provide valuable insights into the probabilities associated with different actions and their influence on match outcomes. The study reveals that successful serves have a high probability of contributing to match victories, with an average success rate of 80% in the dataset. Conversely, defensive plays exhibit a lower success rate, with an average probability of success of 60%. The Hidden Markov Process Bayesian Network (HMPBN) achieves an overall classification accuracy of 85% on the test dataset, demonstrating its effectiveness in predicting match outcomes.