隨著人工智慧技術的快速發展，尤其在運動領域的應用變得越來越廣泛。目前，人工智慧主要應用於籃球、足球和板球等運動項目的研究，涵蓋比賽結果預測、運動員表現分析和戰術制定等多個方面。然而，在排球領域的研究相對較少，這使得排球比賽的數據分析和預測具有重要的研究價值。本研究旨在開發一個排球專家系統，用於預測比賽結果並推薦最佳陣容，以提升球隊的賽事表現。此系統的目的是通過精確的比賽結果預測和陣容推薦，協助教練在戰術制定中做出更為明智的決策。並且針對現有排球領域研究相對匱乏的現狀，包括相關文獻與完整資料集都相對於其他球類來說缺少很多，因此從台灣企業排球聯賽中分別建立了男生和女生球隊的完整數據集，涵蓋多項表現指標，包括進攻、防守、攔網和組織等方面，這對後續研究和分析上具有重要意義。在方法上，首先開發比賽結果預測系統，使用了多種機器學習技術，包括嶺迴歸（Ridge Regression）、隨機森林（Random Forest）、梯度提升樹（Gradient Boosting Machine）和長短期記憶（Long Short-Term Memory，LSTM），來預測比賽結果，並比較模型的準確率。同時，使用LASSO（Least Absolute Shrinkage and Selection Operator）進行特徵選擇，並進行超參數優化提升模型的預測能力，最後輸出為比賽中兩支隊伍的勝率。其次，開發最佳陣容推薦系統，基於球員個人的數據表現與同隊球員間的相互作用設計一套推薦算法，用於推薦最佳上場陣容，幫助教練在比賽前作出最佳排兵布陣。研究結果顯示，比賽結果預測系統能有效預測比賽結果，男女生比賽結果的預測準確率均接近0.85，最佳陣容推薦系統男女生的推薦準確率也平均高達0.89。整套系統展示了人工智慧在排球領域中的應用潛力，為未來的運動數據分析提供了新的思路和方向。通過精確的比賽結果預測和最佳陣容推薦，系統能夠顯著提升球隊的賽事表現，這對於現代排球運動的戰術分析和戰略制定具有重要意義。

With the rapid development of artificial intelligence (AI) technology, its applications in the field of sports have become increasingly widespread. Currently, AI is primarily applied in sports such as basketball, football, and cricket, covering aspects such as match outcome prediction, athlete performance analysis, and tactical planning. However, research in the field of volleyball is relatively scarce, which makes data analysis and prediction for volleyball matches particularly valuable.Therefore, this study aims to develop a volleyball expert system to predict match outcomes and recommend the best lineups to enhance team performance. The system is designed to assist coaches in making informed decisions in tactical planning through accurate match outcome predictions and lineup recommendations. To address the current lack of research in volleyball, separate datasets for male and female teams from the Top Volleyball League were created, encompassing a wide range of performance indicators.To implement the match outcome prediction system, multiple machine learning techniques, including Ridge Regression, Random Forest, Gradient Boosting Machine, and Long Short-Term Memory (LSTM) were developed to predict match results. Additionally, LASSO (Least Absolute Shrinkage and Selection Operator) was used for feature selection. Hyperparameter optimization was conducted to enhance the model's predictive capabilities, ultimately outputting the win probabilities for the two teams in the match.The results indicate that the match outcome prediction system can effectively predict match results,achieving an accuracy of 0.85 for both male and female matches.The best lineup recommendation system also achieved an accuracy of 0.89 for both male and female lineups. This expert system demonstrates the potential of AI applications in the field of volleyball, providing new insights and directions for future sports data analysis. Through accurate match outcome predictions and optimal lineup recommendations, the system can significantly enhance team performance, which is of great importance for tactical analysis and strategic planning in modern volleyball.

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