Aggregate several attributes into one using PCA I am working on a soccer dataset. As we all know, soccer has a lot of different metrics related to the game. We have Key passes, Accurate Passes, Shots on Target, Saves, Clearances and so on...
I have more than 200 columns which makes it infeasible to analyze every single player based on every stat. 

There are some stats that are more important to one position that the others. For instance, I want to know the Shots on Target of a forward but not for goalkeepers. So, I'm thinking in a way to group important stats per position. 

My first thought was using PCA and I would like to know what you think about it. Is it possible to get the principal component for the stats that I'm interested for every position?

Example:
Goalkeepers have the following interesting stats:
- Diving Save
- Saves Inside Box
- Saves Outside Box
- Sweeper Keeper

Strikers:
- Inbox Attempts
- Key Passes
- Shots on Target

I want to reduce the dimensionality of all these important stats and have a singular stat resumes how the player of that position is performing.

Is PCA the best choice or is there a better option?

Kind Regards
I see. Yes PCA is a good tool that you can use on your choice of attributes. The tricky part is that PCA is based on variance. Hence, if you have 2 attributes which are more important and have low variance and then 5 more with a lot of variances then the latter will be predominant on the first PC (I guess some standardisation would help). Hence, maybe it makes more sense to incorporate some corresponding weights on each attribute, a weighted average for instance (dummy example, but may fit the cause).