When combined correlation of features decreases
The only metric you can use to assess the added value of each feature, is the accuracy of your predictive classifier. You can simply include all three feature variables and build your classifier from those. Then you can remove each of the $3$ features and build the three possible classifiers from each of the remaining $2$ features (for example, 'appearances' and 'goals', 'appearances' and 'goals_per_game', and so forth). This analysis gives you the added value of each of the three features to your predictive accuracy. Note the apparent dependency between 'goals' and 'goals_per_game'.
This strategy to feature selection is called sequential backward search. If you have an excess of $10$ features, more advanced algorithms like floating search and MCMC are likely to yield better performing subsets of features. In your case with solely $3$ feature variables sequential backward search should work fine.