As a consequence of a lack of balance between the levels of difficulty of a game and the players’ skills, the resulting experience for players might be frustrating (too difficult) or boring (too easy). Players having a bad experience could impact game creators negatively, leading to irreparable damage. The main motivation of this study was to find effective ways to reduce the gap between skills and difficulty,to help developers create a more suitable experience for players. This paper shows the results of applying Neural Networks and Support Vector Machines to data collected from the pressure exerted to a gamepad’s button with the purpose of finding patterns that can help predict: difficulty, fun, frustration, boredom,valence, arousal and dominance at a determined time. We obtained results with an accuracy of 83.64 % when predicting boredom, around 70 % of accuracy classifying frustration, fun, difficulty and dominance.