The biggest soccer leagues in Europe have different rankings and attributes. The skill ofplayers and teams as a whole are evaluated by 9000 members of EA sports. In order toanalyze whether it is possible to predict the outcome of a match with respect to theserankings, two different classification algorithms are applied:neural networksandrandomforest.The result of both methods indicate thatrandom forestis a better learner thanneuralnetworkswhen applied to data with a multiclass label. However, when attempting toclassify a binary outcome,neural networksperform better. The independent variables(player- and team attributes) that affect the outcome variable the most are: reactiontime, short passes, ball control, long passes and dribbling. When applying both methodson every separate league, the results show a difference in the evaluation measures.