Valuing passes in football using ball event data
This master thesis introduces and evaluates several models for valuing passes in football. We use event data from five seasons of matches from the top five leagues in Europe. The most simplistic model considers the value of possessing the ball in a certain area of the pitch. The other models use clustering methods to find similar passes and similar attacks to value passes. The comparison of attacks also yields the opportunity to find teams with similar playing styles. The proposed pass valuing models make it possible to rank players based on the values that were assigned to their passes. This ranking of players may help clubs to scout potential new players as well as to analyze the upcoming opponent. We show that the pass values can be used to estimate player market values and to predict match outcomes.