Oh yeah generally youre right, but the data was also ‘doubled’ in this way. Because each row is a match, the variables are present for each team separately. For example: there’s attempts from outside the box by team1, and attempts from outside the box by team2. The pattern of relation should be symmetrical because these differences are arbitrary as you say.
Each row in the data is a match, so ‘team1’ represents a win for the “home” team, ‘team2’ represents a win for the “away” team and ‘draw’ represents a draw.
I tried to extract 2 meaningful PC’s from 59 analytics of the matches (variables like ball possession, goal attempts… etc). Then I wanted to see if the matches are clustered on these predictors according to their outcome.
Principal Components Analysis, k-means clustering and actual outcome of the 2022 Fifa world cup matches [OC]
Sadgasm1 OP t1_j1yfh7h wrote
Reply to comment by TheGoatzart in Principal Components Analysis, k-means clustering and actual outcome of the 2022 Fifa world cup matches [OC] by Sadgasm1
Oh yeah generally youre right, but the data was also ‘doubled’ in this way. Because each row is a match, the variables are present for each team separately. For example: there’s attempts from outside the box by team1, and attempts from outside the box by team2. The pattern of relation should be symmetrical because these differences are arbitrary as you say.