Let's suppose that I have a dataset with datapoints about footballers.
The data are about footballers' performance and information (e.g goals, assists, injuries, age, weight etc) on a monthly basis for the last 2 years.
My goal is to see how the performance and status of a footballer at a particular month is related to his performance and status of the next month.
At a first stage, I just want to run some correlation to detect some of these relationships.
In this case, does it make sense to run a separate correlation at each footballer's data of the last 2 years and then average the correlation results across players or directly run a correlation across all footballer's data at any month?