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| 題 名 | 建構美國職棒大聯盟的勝隊預測模式:以人工類神經網路方式=Construction of the Winner Predictive Model in Major League Baseball Games: Use of the Artificial Neural Networks |
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| 作 者 | 陳麒文; 李天行; 梁玉秋; | 書刊名 | 大專體育學刊 |
| 卷 期 | 16:2 2014.06[民103.06] |
| 頁 次 | 頁167-181 |
| 分類號 | 528.955 |
| 關鍵詞 | 資料探勘; 羅吉斯迴歸分析; 管理; 數量方法; Data mining; Logistic regression; Management; Quantitative methods; |
| 語 文 | 中文(Chinese) |
| 中文摘要 | 本研究之主要目的在於利用資料探勘中之人工類神經網路方式來建構美國職棒大聯盟的勝隊預測模式。研究對象則是洋基隊與紅襪隊在2006年至2012年共127場例行賽,所有例行賽中的各項變數,包括主客場球隊、主客場先發投手之左右投狀況、主客場先發投手上一場之變數、主客場打擊群上一場之變數、賽事時間、比賽時之華氏溫度等共60個自變數。同時,本研究也以兩隊在2013年球季的比賽進行迴溯分析來驗證模式的準確度。除了人工類神經網路外,本研究也利用羅吉斯迴歸分析來建構兩隊的預測模式,並進一步探討上述兩種工具所建構之模式的差異與良窳。研究結果顯示,以人工類神經網路所建構出來的預測模式,其勝隊預測準確率較高,達72.22%;本研究也利用2013年的比賽來驗證模式的預測績效,結果預測正確率達73.68%。本研究結論:由於本研究將「運動」、「運動產業」結合「管理」、「數量方法」、「資料探勘」等領域,並提供了投注者一個精確、簡易合理的解決途徑來預測勝隊。所以建議使用人工類神經網路來預測 勝隊是一項值得信賴的工具。 |
| 英文摘要 | The purpose of this study was to construct the winner predictive model in Major League Baseball (MLB) by using the artificial neural networks (ANNs) as a tool. One hundred and twenty-six games from the seasons of 2006 to 2012 held between New York Yankees and Boston Red Sox teams were involved in this study, and 60 variables were collected to analyze. Meanwhile, the winner predictive model was applied to the season of 2013 games to verify the model accuracy by virtual bets. In addition to ANNs, logistic regression was also used to develop the winner predictive model to discuss the differences, and to explore the advantages and disadvantages between these two tools. The results showed that ANNs-based predictive model had 72.22% of winning accuracy. Moreover, for the season of 2013 games between Yankees and Red Sox teams were used by way of virtual bets to test the model's accuracy, and the results showed 73.68% of accuracy. Because of combined many areas, such as sports, sports industry, management and quantitative methods, and also equipped the ability of data mining, our findings provided an accurate, simple and reasonable way to predict the winning team for bettors. Therefore, the use of ANNs model was recommended as a suitable tool to predict the winning team. |
本系統中英文摘要資訊取自各篇刊載內容。