Extending the Attribute Explorer to Support Professional Team-Sport Analysis

Abstract

Advances in interactive systems and the ability to manage increasing amounts of high-dimensional data provide new opportunities in numerous domains. Information visualization techniques are especially useful in situations where analysts seek patterns and information of interest in massive data sets. In this article, we propose an extension of the original Attribute Explorer (AE) technique by Spence and colleagues to take on the challenges presented in the domain of professional team-sport analysis. We describe the implementation of an extended AE and use football game-event data to highlight the new possibilities.