TLGProb: Two-Layer Gaussian Process Regression Model for Winning Probability Calculation in Two-Team Sports

Max W. Y. Lam

Abstract: Sports analytics is gaining much attention in the research community nowadays. This paper deals with a prominent problem in sports analytics, namely, winning probability calculation. In particular, we focus on the two-team sports. A novel model called TLGProb is proposed by stacking a non-linear regression model – Gaussian process regression (GPR) to address complex association between match outcomes and players’ performances. For evaluation, we selected a popular sports event around the world – National Basketball Association (NBA) as the domain for experiments. Finally, using TLGProb, we correctly predicted 85.28% of outcomes among 1,230 matches in NBA 2014/2015 season.