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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?

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The name of the procedure you are looking for is

Autocorrelation

This procedure looks a lot like the first you describe.

Answer fot this question in ResearchGate

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  • $\begingroup$ Thanks but my question was not about HOW to implement the first method which I describe but about WHICH method to use (out of the two described at my post above) to test what I describe at my post. $\endgroup$ – Outcast Jun 4 '19 at 17:10
  • $\begingroup$ The first. I was giving you the name of the procedure so you could look for it easier $\endgroup$ – Juan Esteban de la Calle Jun 4 '19 at 17:19
  • $\begingroup$ Haha ok but in more scientific discussions (like the ones on Datascience.stackexchange) we tend also to explain WHY something should be implemented etc. $\endgroup$ – Outcast Jun 4 '19 at 17:33

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