Forecasting Soccer Matches through Distributions

Abstract

Forecasting sporting events encapsulate a compelling intellectual endeavor, underscored by the substantial financial activity of an estimated $80 billion wagered in global sports betting during 2022, a trend that grows yearly. Motivated by the challenges set forth in the Springer Soccer Prediction Challenge, this study presents a method for forecasting soccer match outcomes by forecasting the shot quantity and quality distributions. The methodology integrates established ELO ratings with machine learning models. The empirical findings reveal that, despite the constraints of the challenge, this approach yields positive returns, taking advantage of the established market odds.