Investigating the Sketchplan: A Novel Way of Identifying Tactical Behavior in Massive Soccer Datasets.

Coaches and analysts prepare for upcoming matches by identifying common patterns in the positioning and movement of the competing teams in specific situations. Existing approaches in this domain typically rely on manual video analysis and formation discussion using whiteboards; or expert systems that rely on state-of-the-art video and trajectory visualization techniques and advanced user interaction. We bridge the gap between these approaches by contributing a light-weight, simplified interaction and visualization system, which we conceptualized in an iterative design study with the coaching team of a European first league soccer team. Our approach is walk-up usable by all domain stakeholders, and at the same time, can leverage advanced data retrieval and analysis techniques: a virtual magnetic tactic-board. Users place and move digital magnets on a virtual tactic-board, and these interactions get translated to spatio-temporal queries, used to retrieve relevant situations from massive team movement data. Despite such seemingly imprecise query input, our approach is highly usable, supports quick user exploration, and retrieval of relevant results via query relaxation. Appropriate simplified result visualization supports in-depth analyses to explore team behavior, such as formation detection, movement analysis, and what-if analysis. We evaluated our approach with several experts from European first league soccer clubs. The results show that our approach makes the complex analytical processes needed for the identification of tactical behavior directly accessible to domain experts for the first time, demonstrating our support of coaches in preparation for future encounters.

CourtTime: Generating Actionable Insights into Tennis Matches Using Visual Analytics.

Tennis players and coaches of all proficiency levels seek to understand and improve their play. Summary statistics alone are inadequate to provide the insights players need to improve their games. Spatio-temporal data capturing player and ball movements is likely to provide the actionable insights needed to identify player strengths, weaknesses, and strategies. To fully utilize this spatio-temporal data, we need to integrate it with domain-relevant context meta-data. In this paper, we propose CourtTime, a novel approach to perform data-driven visual analysis of individual tennis matches. Our visual approach introduces a novel visual metaphor, namely 1-D Space-Time Charts that enable the analysis of single points at a glance based on small multiples. We also employ user-driven sorting and clustering techniques and a layout technique that aligns the last few shots in a point to facilitate shot pattern discovery. We discuss the usefulness of CourtTime via an extensive case study and report on feedback from an amateur tennis player and three tennis coaches.

Visual Analytics of Volunteered Geographic Information: Detection and Investigation of Urban Heat Islands.

Urban heat islands are local areas where the temperature is much higher than in the vicinity and are a modern phenomenon that occurs mainly in highly developed areas, such as large cities. This effect has a negative impact on energy management in buildings, and also has a direct impact on human health, especially for elderly people. With the advent of volunteered geographic information from private weather station networks, more high-resolution data are now available within cities to better analyze this effect. However, such datasets are large and have heterogeneous characteristics requiring visual-interactive applications to support further analysis. We use machine learning methods to predict urban heat islands occurrences and utilize temporal and spatio-temporal visualizations to contextualize the emergence of urban heat islands to comprehend the influencing causes and their effects. Subsequently, we demonstrate the analysis capabilities of our application by presenting two use cases.

TenniVis: Visualization for Tennis Match Analysis.

Existing research efforts into tennis visualization have primarily focused on using ball and player tracking data to enhance professional tennis broadcasts and to aid coaches in helping their students. Gathering and analyzing this data typically requires the use of an array of synchronized cameras, which are expensive for non-professional tennis matches. In this paper, we propose TenniVis, a novel tennis match visualization system that relies entirely on data that can be easily collected, such as score, point outcomes, point lengths, service information, and match videos that can be captured by one consumer-level camera. It provides two new visualizations to allow tennis coaches and players to quickly gain insights into match performance. It also provides rich interactions to support ad hoc hypothesis development and testing. We first demonstrate the usefulness of the system by analyzing the 2007 Australian Open men's singles final. We then validate its usability by two pilot user studies where two college tennis coaches analyzed the matches of their own players. The results indicate that useful insights can quickly be discovered and ad hoc hypotheses based on these insights can conveniently be tested through linked match videos.