In recent years, statistics play an increasing role in professional football. A controversialtopic inside the emerging field of football data science is the effect of ball possession onmatch...Show moreIn recent years, statistics play an increasing role in professional football. A controversialtopic inside the emerging field of football data science is the effect of ball possession onmatch outcomes. We contribute to this discussion by analyzing the effect of possession onmatch outcomes while controlling for match status and match-up balance. We examinethe importance of the position of possession by comparing the kernel density estimateof winning and losing teams. Based on these findings we split the football pitch intodistinct zones using Voronoi cells based on the centroids of a k-means clustering. We fit amultiple linear regression model that regresses a match’s final goal difference on possessionper match status per zone using a 5x5-fold nested cross-validation. The resulting modelsplits the football pitch into 11 zones. Our metric holds higher predictive power thanthe traditional metric. To demonstrate the potential of this work for both analysts andjournalists, we analyze a teams performance over a whole season as well as individualmatch performances using the metric.Show less
