I am running a analysis on a soccer data set. I applied regression directly on the input data and this is the output I obtained. I then applied the standardization technique on the input features. This the result:  I got my required parameter correct in both the cases but I was surprised to see the huge difference in both. Some parameters are having negative weights now which had positive ones before and were positive large enough. Why is there this statistical difference? And which one depicts the analysis better?
Why is there considerable difference after applying standardization on the input data?
machine learning;data mining;regression;feature scaling