Abstract The paper presents a computational approach to Availability of soccer players. Availability is defined as the probability that a pass reaches the target player without being intercepted by opponents. Clearly, a computational model for this probability grounds on models for ball dynamics, player movements, and technical skills of the pass giver. Our approach aggregates these quantities for all possible passes to the target player to compute a single Availability value. Empirically, our approach outperforms state-of-the-art competitors using data from 58 professional soccer matches. Moreover, our experiments indicate that the model can even outperform soccer coaches in assessing the availability of soccer players from static images.

We study a setting in which Poisson processes generate sequences of decision-making events. The optimization goal is allowed to depend on the rate of decision outcomes; the rate may depend on a potentially long backlog of events and decisions. We model the problem as a Poisson process with a throttling policy that enforces a data-dependent rate limit and reduce the learning problem to a convex optimization problem that can be solved efficiently. This problem setting matches applications in which damage caused by an attacker grows as a function of the rate of unsuppressed hostile events. We report on experiments on abuse detection for an email service.

We address the problem of learning decision functions from training data in which some attribute values are unobserved. This problem can arise, for instance, when training data is aggregated from multiple sources, and some sources record only a subset of attributes. We derive a generic joint optimization problem in which the distribution governing the missing values is a free parameter. We show that the optimal solution concentrates the density mass on finitely many imputations, and provide a corresponding algorithm for learning from incomplete data. We report on empirical results on benchmark data, and on the email spam application that motivates our work. 1.