Predicting the result of a football game is challenging due to the complexity and uncertainties of many possible influencing factors involved. Genetic Programming (GP) has been shown to be very successful at evolving novel and unexpected ways of solving problems. In this work, we apply GP to the problem of predicting the outcomes of English Premier League games with the result being either win, lose or draw. We select 25 features from each game as the inputs to our GP system, which will then generate a function to predict the result. The experimental test on the prediction accuracy of a single GP-generated function is promising. One advantage of our GP system is, by implementing different runs or using different settings, it can generate as many high quality functions as we want. It has been showed that combining the decisions of a number of classifiers can provide better results than a single one. In this work, we combine 43 different GP-generated functions together and achieve significantly improved system performance.