How to approach this data set for linear regression? I am new to data science and am currently working on a data science project and have to answer a few questions about the following data set with 18k data points:
https://www.kaggle.com/karangadiya/fifa19

The question I have to answer is as follows:
To which extent can you determine the value of a player using the players most important features?

Features in this case being the columns from 'Crossing' to 'GKReflexes'. I grouped up all the players as follows:
Att = ['RF', 'LF', 'RS', 'LS', 'RS', 'RW', 'LW', 'CF', 'ST'] # attack positions
Mid = ['LAM', 'CAM', 'RAM', 'LM', 'LCM', 'CM', 'RCM', 'RM', 'LDM', 'CDM', 'RDM'] # midfield positions
Def = ['LCB', 'CB', 'LB', 'RCB', 'RB', 'LWB', 'RWB'] # defense positions
GK

I already looked at the data and was able to determine that certain player features are much higher for certain positions.

I plan on using multiple linear regression to answer the research question, but I'm not sure on how to split the data into a train and test set. 

I'm thinking of one of the following approaches:


First split the large data set into smaller data sets for each position (GK, DEF, ATT and MID) and then make a train and test set for each position. 


or


Leave the large dataset as it is with all of the positions combined and split that into a training and test set.


I am currently leaning more towards the first approach because I think I'll be able to better determine the price of the player using the features that are important to the players position, but I am not sure if that's the correct way to go at it.
So in the first approach you want to manually choose some features that seem more important?

If so, I think you might not be able to accurately diagnose the most important features, I think it is better to first choose the most impactful features using an algorithm like backward-elimination, etc to figure out what are the most important features, then ignore the less important columns and split the dataset to training and test error.