Predicting the Success of NFL Teams using Complex Network Analysis

Abstract

The NFL (National Football League) is the most popular sports league in the United States and has the highest average attendance of any professional sports league in the world, moving billions of dollars annually through licensing agreements, sponsorships, television deals, ticket and product sales. In addition, it moves a billionaire betting market, which heavily consumes statistical data on games to produce forecasts. Moreover, game statistics are also used to characterize players performance, dictating their salaries. Thus, the discovery of implicit knowledge in the NFL statistics becomes a challenging problem. In this article, we model the behavior of NFL players and teams using complex network analysis. In particular, we represent quarterbacks and teams as nodes in a graph and labor relationships among them as edges to compute metrics from the graph, using them to discover implicit properties of the NFL social network and predict team success. Experimental results show that this social network is a scale-free and small-world network. Furthermore, node degree and clustering coefficient can be effectively used to predict team success, outperforming the usual passer rating statistic.