Game Plan: What AI can do for Football, and What Football can do for AI

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players’ and coordinated teams’ behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-theart and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual)

Information value in nonparametric Dirichlet-process Gaussian-process (DPGP) mixture models

© 2016 Elsevier Ltd This paper presents tractable information value functions for Dirichlet-process Gaussian-process (DPGP) mixture models obtained via collocation methods and Monte Carlo integration. Quantifying information value in tractable closed form is key to solving control and estimation problems for autonomous information-gathering systems. The properties of the proposed value functions are analyzed and then demonstrated by planning sensor measurements so as to minimize the uncertainty in DPGP target models that are learned incrementally over time. Simulation results show that sensor planning based on expected KL divergence outperforms algorithms based on mutual information, particle filters, and randomized methods