臺灣博碩士論文加值系統

預測運動比賽的結果一直是普羅大眾的熱門話題，而一場的比賽的勝負常取決於許多複雜的因素，但也因為其存在不確定性，所以預測比賽往往也是球迷看球的樂趣之一，這也使得運動比賽預測成為一項有趣的研究議題。
本論文的研究目標在於使用長短期記憶(LSTM)類神經網路建立預測模型，來嘗試預測NBA籃球比賽的勝負，本文針對2017-18年度NBA賽季進行研究，將籃球比賽相關數據加以評估，並挑選可能影響比賽勝負的關鍵屬性，再將選擇的特徵帶入模型進行訓練和測試，最後分析結果。在輸入資料方面可細分為兩個部分，第一個部分以輸入形式劃分，將比賽輸入分為每3、4、5場比賽預測;第二個部分以數據範圍劃分，又可分為全域預測與特定隊伍預測。藉由測試數個不同的輸入模式與適時調整模型參數，以找出更好的預測結果的可能性。實驗結果顯示，在針對特定隊伍的預測下，最佳的隊伍預測率達71%。

Sport result prediction is a popular topic .The outcome of a contest depends on many complicated factors due to its uncertainty. It is an interesting funny topic of fan for contest prediction.
In this paper, we applied LSTM Neural Networks to Build a predictive model and predict the result of basketball games in NBA league .We focus on 2017/2018 NBA season and select the key attributes which may affect the outcome of the contest. The selected features can be used for training, testing and result analysis. In terms of input data can be divided into two parts: the first part is to divide the data with three games, four games and five games as a number of test sets to predict the outcome of contest for the LSTM method. The second part is to conduct the set prediction with respect to all teams or a specific team. We can find the possibility with better prediction by testing several input modes and adjusting model parameters timely. The results show that the accuracy of specific team prediction is up to 71%.

致謝辭 i
中文摘要 ii
Abstract iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章 緒論 1
1.1. 研究動機與目的 1
1.2. 研究內容與範圍 1
1.3. 論文架構與流程 2
第二章 理論基礎與相關研究 3
2.1. 類神經網路 3
2.1.1. 類神經網路的演進 3
2.1.2. 類神經網路之分類 4
2.1.3. 類神經網路的型態與運作 5
2.1.4. 類神經網路的訓練 5
2.2. 循環神經網路 6
2.2.1. RNN的訓練 7
2.2.2. RNN的問題與限制 8
2.3. 長短期記憶(Long short-term memory,LSTM) 8
2.3.1. 激勵函數(Activation function) 9
2.3.2. LSTM的結構與運作 11
2.4.應用於比賽預測的相關研究 13
第三章 LSTM預測模型流程與方法 15
3.1. 實驗資料集與前處理 16
3.1.1. 資料前處理 17
3.1.2. 資料特徵編碼 18
3.2. 特徵選擇與評估 20
3.2.1. 比賽紀錄的特徵 20
3.2.2. 球員屬性的特徵 21
3.3.模型實作及驗證流程分析 23
3.3.1. 資料集的輸入形式 24
3.3.2. 資料集的數據範圍 26
3.3.3. 模型的參數設置 26
第四章 實驗結果與評估 28
4.1. 環境建置與實驗資料 28
4.2. 預測結果試驗 31
4.2.1. 實驗一: 全域預測 31
4.2.2. 實驗二: 特定隊伍預測 32
4.3. 結合運彩公司賠率投注試驗 36
第五章 結論與未來展望 39
5.1. 結論 39
5.2. 未來展望 39
參考文獻 40

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