教師資料查詢 | 類別: 會議論文 | 教師: 周清江 Chichang Jou (瀏覽個人網頁)

標題: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
語言英文(美國)
收錄於
會議性質國際
校內研討會地點
研討會時間20190406~20190408
通訊作者Chichang Jou
國別日本
公開徵稿
出版型式
出處
相關連結
Google+ 推薦功能,讓全世界都能看到您的推薦!