CourtTime : Generating Actionable Insights into Tennis Matches Using Visual Analytics
2020-01, Polk, Tom, Jäckle, Dominik, Häußler, Johannes, Yang, Jing
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.
FDive : Learning Relevance Models Using Pattern-based Similarity Measures
2019, Dennig, Frederik L., Polk, Tom, Lin, Zudi, Schreck, Tobias, Pfister, Hanspeter, Behrisch, Michael
The detection of interesting patterns in large high-dimensional datasets is difficult because of their dimensionality and pattern complexity. Therefore, analysts require automated support for the extraction of relevant patterns. In this paper, we present FDive, a visual active learning system that helps to create visually explorable relevance models, assisted by learning a pattern-based similarity. We use a small set of user-provided labels to rank similarity measures, consisting of feature descriptor and distance function combinations, by their ability to distinguish relevant from irrelevant data. Based on the best-ranked similarity measure, the system calculates an interactive Self-Organizing Map-based relevance model, which classifies data according to the cluster affiliation. It also automatically prompts further relevance feedback to improve its accuracy. Uncertain areas, especially near the decision boundaries, are highlighted and can be refined by the user. We evaluate our approach by comparison to state-of-the-art feature selection techniques and demonstrate the usefulness of our approach by a case study classifying electron microscopy images of brain cells. The results show that FDive enhances both the quality and understanding of relevance models and can thus lead to new insights for brain research.