| 摘要(英) |
This research aims to predict Major League Baseball batter’s salary. The batters’ batting
records(H,R,HR,...), fielding records(PO,E,A,...) and other records(year, seniority,age,G,GS)
are independent variables. With the help of feature engineering, we can find out the suitable
feature variables which are fed for training a prediction model. This research uses the record
from 2003-2014 as the dataset of a regression model that predicts batters’ salary after 2015.
In data preprocessing we did three things:
1. Drop the international players(from Serie Nacional de Béisbol, Venezuelan Professional
Baseball League, Dominicana Professional Baseball League,...) data.
2. Natural logarithm of salary.
3. Original data table record performance in each year(batting record, fielding record). However, we changed record method, use sum of last five years performance record(batting
record, fielding record). |
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