Interplay of Visual Analytics and Topic Modeling in Gameplay Analysis

Spatio-temporal event sequences consist of activities or occurrences involving various interconnected elements in space and time. Exploring these sequences with topic modeling is a relatively new and evolving research area. We use topic modeling to analyze football games, as an example of complex and under-explored spatio-temporal event data. A key challenge in topic modeling is selecting the most suitable number of topics for the downstream application. Selecting too few topics oversimplifies the data, merging distinct patterns, whereas selecting too many can fragment coherent themes into overlapping categories. We propose a visual analytics technique that uses dimensionality reduction on topics derived from multiple topic modeling runs, each with a different number of topics. Our technique organizes all the topics in a hierarchical layout based on their spatial similarity, making it easier to make an informed decision about selecting the most expressive set of topics that represent distinctive spatial patterns. We apply our visual analytics technique to a football dataset, illustrating how it can be used to select an appropriate set of topics for this data. We then use these topics to represent game episodes, which help us summarize game dynamics and uncover insights into the games.