This thesis presents an investigation regarding the playing styles of football teams in Allsvenskan, the biggest football competition in Sweden, using clustering analysis. The research makes use of data pre-processing, feature engineering, and K-Means clustering to identify different, distinct, clusters that aim to represent different playing philosophies. The dataset undergoes pre-processing, including cleaning and normalization, to ensure good quality for performing clustering analysis. Plenty of features are, with caution, engineered to capture dominance in possession, physical intensity, and defense qualities. The resulting clusters reveal various playing styles, ranging from possession-based teams to physically intense counter-attacking teams. The practical implications of the analysis are discussed, highlighting the value for Football Analytics Sweden and their clients in areas such as team composition and match strategies. Future work suggestions include investigating how playing styles change when teams take the lead or concede, as well as using the model with real-time data for media purposes. The framework delivery to the company includes Python scripts for data processing and visualization, as well as the clustering model implementation. The comprehensive report documents the methodology, results, and practical implications. This thesis contributes to football analytics by uncovering playing styles, empowering decision-making processes, and providing a foundation for future research.