Machine Learning in Sports Analytics: Volleyball Match Outcome Prediction

In the competitive realm of sports, accurate predictions of match outcomes can provide significant strategic advantages. This study aims to develop an accurate prediction system to assist coaches in making informed decisions. While there has been extensive research on AI applications in various sports, volleyball remains relatively underexplored. To bridge this gap, we first developed two comprehensive datasets from the Taiwan Volleyball League, one for male teams and one for female teams, each consisting of a range of performance metrics. We then leveraged various machine learning (ML) techniques, including Ridge Regression, Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) models, to predict volleyball match results. To enhance model prediction, we also applied LASSO regression for feature selection and hyperparameter optimization for parameter selection. The results demonstrated that our ML models could effectively predict match outcomes, providing valuable insights for coaches to develop strategies and improve team performance.