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Football analytics is a rapid growing area which utilizes conventional data analysis and computational methods on gathered data from football matches. The results emerging out of this can give insights of performance levels when it comes to individual football players, different teams and clubs. A difficulty football analytics struggles with daily is to translate the analysis results into actual football qualities and knowledge which the wider public can understand. In this master thesis we therefore take on the ball event data collected from football matches and develop a model which classifies individual football player’s playing styles, where the playing styles are well known among football followers. This is carried out by first detecting the playing positions: ’Strikers’, ’Central midfielders’, ’Outer wingers’, ’Full backs’, ’Centre backs’ and ’Goalkeepers’ using K-Means clustering, with an accuracy of 0.89 (for Premier league 2021/2022) and 0.84 (for Allsvenskan 2021). Secondly, we create a simplified binary model which only classifies the player’s playing style as "Offensive"/"Defensive". From the bad results of this model we show that there exist more than just these two playing styles. Finally, we use an unsupervised modelling approach where Principal component analysis (PCA) is applied in an iterative manner. For the playing position ’Striker’ we find the playing styles: ’The Target’, ’The Artist’, ’The Poacher’ and ’The Worker’ which, when comparing with a created validation data set, give a total accuracy of 0.79 (best of all positions and the only one covered in detail in the report due to delimitations).  The playing styles can, for each player, be presented visually where it is seen how well a particular player fits into the different playing styles. Ultimately, the results in the master thesis indicates that it is easier to find playing styles which have clear and obvious on-the-ball-actions that distinguish them from other players within their respective position. Such playing styles, easier to find, are for example "The Poacher" and "The Target", while harder to find playing styles are for example " The Box-to-box" and "The Inverted". Finally, conclusions are that the results will come to good use and the goals of the thesis are met, although there still exist a lot of improvements and future work which can be made.  Developed models can be found in a simplified form on the GitHub repository: https://github.com/Sommarro-Devs/avatar-playing-style. The report can be read stand-alone, but parts of it are highly connected to the models and code in the GitHub repository.