| 摘要: | 本研究的目的是預測MLB的投手是否具有獲得賽揚獎的潛力，研究方法是蒐集2008年到2022年(不包括2020年)球季每年7月31日以前的MLB投手數據集以及賽揚獎得主，以投手數據(出賽場數，先發出賽場數，勝投場數，敗投場數等)為自變數，是否獲得賽揚獎(未得獎者為0，得獎者為1)為應變數，投入集成學習的訓練與預測，並嘗試找出是否可以獲得賽揚獎和投手數據的特徵之間的關聯性。本研究在資料前置處理步驟進行了下列4項處理:
1.加入Sabermetrics的項目BsR。
2.將特徵W和ERA為遺漏值的樣本移除，並將特徵L, SV, SO/W為遺漏值的部份補0。
3.處理特徵IP的數值:0.1局改成1/3局，0.2局改成2/3局，以此類推。
4.加入目標項”Cy Young Award”(將不是賽揚獎得主的投手標示為0，賽揚獎得主的投手標示為1)。;The goal of this research is predicting that whether each pitcher in MLB has potential to win Cy Young Award or not. First step is collecting the dataset of pitching statistics in MLB of each year earlier than July 31 from 2008 to 2022 (without 2020), and the awardees of Cy Young Award in these years. This research let the pitching statistics (G, GS, W, L, ..., etc) and ”Cy Young Award”(0 for each pitcher who is not awardee and 1 for awardees) be the independent variable and dependent variable, respectively, use ensemble learning to train and predict, and try to find the relation of whether a pitcher winning Cy Young Award or not and the features in the dataset. This research did the following things in the step of data preprocessing:
1.Add ”BsR”(a subject of Sabermetrics) into the dataset.
2.Remove each sample whose ”W” or ”ERA” is NaN (not a number). If there is NaN in ”L”, ”SV”, or ”SO/W”, then fill 0 into it.
3.Deal with the data in ”IP”: replace 0.1 by 1/3 , replace 0.2 by 2/3, and so on.
4.Add the target, ”Cy Young Award”, into the dataset (let each sample of a pitcher who is not awardee be 0 and let the samples of awardees be 1). |
