Predicting Margin of Victory in NFL Games: Machine Learning vs. the Las Vegas Line

In this study we describe efforts to use machine learning to out-perform the expert Las Vegas line-makers at predicting the outcome of NFL football games. The statistical model we employ for inference is the Gaussian process, a powerful tool for supervised learning applications. With predictions for the margin of victory and associated confidence intervals from the Gaussian process model, we propose a simple framework which recommends a bet on a given game when it is deemed statistically favorable. The training dataset we consider in this study includes a wide variety of offensive and defensive NFL statistics from about 2,000 games between 2000 and 2009. We also explore the impact of including additional novel features previously unstudied: the temperature difference between competing team’s cities and a team’s computed strength according to [10]. We show that our predictions for margin of victory result in an error just 2 % higher than that of the Las Vegas line and that we can successfully pick the game winner over 64 % of the time. The bet-recommendation scheme we propose is shown to provide a win rate just under 51 % but falls short of the mark of 52.4 % needed to break even in the NFL gambling system. 1