In modern days, predicting sports outcomes has gained significant attention from researchers and spectators all around the world. Accurate game result prediction is important not only for competitive purposes but also for improving fan engagement and supporting decision-making for sports teams. Soccer, also known as football, is one of the most popular sports in the world. In this paper, we present a data science solution, which compares and analyzes different combinations of features that are essential for soccer match result prediction. For evaluation of our solution, we applied it to data from English Premier League seasons. We compared and analyzed the results to find out which features individually, or which combinations of features, have the most impact on the outcome of a game. The findings aim to advance the understanding of sports analytics and provide insights into practical applications in the soccer domain.