Predicting the success of a mobile game is a prime issue in game industry. Thousands of games are being released eachday. However, a few of them succeed while the majority fail. Toward the goal of investigating the potential correlationbetween the success of a mobile game and its specific attributes, this work was conducted. More than 17 thousand gameswere considered for that reason. We show that IAPs (In-App Purchases), genre, number of supported languages, developerprofile, and release month have a clear effect on the success of a mobile game. We also develop a novel success scorereflecting multiple objectives. Furthermore, we show that game icons with certain visual characteristics tend to be asso-ciated with more rating counts. We employ different machine learning models to predict a novel success score metric of amobile game given its attributes. The trained models were able to predict this score, as well as the expected rating averageand rating count for a mobile game with 70% accuracy.