How do soccer teams coordinate consecutive passes? A visual analytics system for analysing the complexity of passing sequences using soccer flow motifs

The analysis of passing strategies plays a major role in soccer. Soccer managers use scouting, video footage, and soccer data feed to collect information about tactics and player performance. However, the nature of passing strategies is complex enough to reflect what is happening in the match and makes it hard to understand its dynamics. Furthermore, there exists a growing demand for pattern detection and passing analysis popularized by FC Barcelona's tiki-taka. In this paper, we describe a visual analytics system to analyze the sequence and trajectory of consecutive passing sequences. We describe a two-phase clustering algorithm that extracts typical trajectory clusters in passing sequences, which result in eight predominant clusters. The combined analysis of the sequence and trajectory clusters allow experts to perform multi or single-game analysis in various ways. We show the potential of our approach in case studies using data from the Brazilian and Turkish leagues and report feedback from soccer experts

Visual exploration of rating datasets and user groups

International audienceThe increasing availability of rating datasets (i.e., datasets containing user evaluations on items such as products and services) constitutes a new opportunity in various applications ranging from behavioral analytics to recommendations. In this paper, we describe the design of VugA, a visual enabler for the exploration of rating data and user groups. VugA helps analysts, be they novice analysts or domain experts, acquire an understanding of their data through a seamless integration between exploring users and exploring their collective behavior via group analysis. VugA is data-driven and does not require analysts to know the value distributions in their data. While automated systems can identify and suggest potentially interesting groups, they can do that for well-specified needs (e.g., through SQL QUERIES or constrained mining). VugA helps analysts filter and refine their exploration as they discover what lies in the data. VugA enables analysts to easily acquire statistics about their data, form groups, and find similar and dissimilar groups. While most visual analytics systems are data-dependent, VugA relies on a data model that captures user data in such a way that a variety of group formation and exploration approaches can be used