I'm building a machine learning model in Python to predict soccer player values. I'm trying to predict a "player_value" column containing the value of a specific player. Consider a sample of the columns (features) I'm using.
---------------------------------
appearances | goals | goals_per_game
------------|-------|---------------
20 | 2 | 0.1
60 | 20 | 0.33
54 | 30 | 0.55
43 | 15 | 0.34
30 | 17 | 0.56
I thought that the correct way to use those columns would be creating a goals per game statistic (goals divided by appearances), since a player can have more goals than another player, but with less matches played.
After that, the correlation of the refered columns with the column that I'm trying to predict (player value) decreased. The correlation of the "goals" and the "appearances" columns with the player value column was about 45% each, while the new "goals_per_game" column has a correlation around 18%.
Should I use the columns "appearances" and "goals_per_game" columns individually and not use the "goals_per_game" column? Is my analysis wrong and it does not makes sense to use a "goals_per_game" metric since the player value is higher when using those features individually?