Predicting Yearly Winning Percentage of MLB Teams by Regression Trees
學年 107
學期 2
發表日期 2019-04-06
作品名稱 Predicting Yearly Winning Percentage of MLB Teams by Regression Trees
作品名稱（其他語言）
著者 Chichang Jou; Li-wen Lo
作品所屬單位
出版者
會議名稱 International Conference on Internet Studies (NETs 2019)
會議地點 Nagoya, Japan
摘要 Major League Baseball of the USA is considered the most competitive and challenging arena of baseball. And the population of baseball fans is still increasing. Many scholars and fans are interested in using each team’s performance data to predict outcomes of MLB games. Their prediction accuracy is around 50%. Our goal is to use performance data to predict the yearly winning percentage of each team. Our research method is Classification and Regression Trees (CART) and Maximum Likelihood Regression Trees (MLRT). In addition, we will discuss the prediction accuracy of the CART and MLRT models, and apply the result to predict the playoffs list of the MLB. We find that these models all have good prediction effectiveness for the yearly winning percentage with the MAPE between 12% and 13%. CART models are slightly better than MLRT models in winning percentage prediction. For the playoffs prediction, MLRT is only better than CART in 2018 for models eliminating collinear variables. The prediction effectiveness of CART is the same or better than the MLRT for the rest.
關鍵字 MLRT;CART;Winning;Percentage Prediction;Playoff Prediction;MAPE;MLB
語言 en_US
收錄於
會議性質 國際
校內研討會地點 無
研討會時間 20190406~20190408
通訊作者 Chichang Jou
國別 JPN
公開徵稿
出版型式
出處
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機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/116860 )

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