A Multi-Phased Co-Design Of An Interactive Analytics System For Moba Game Occurrences

To ensure the playability of Multiplayer Online Battle Arena (MOBA) games, designers strive to balance different game occurrences. Although machine learning (ML) can help classify matches into different occurrence categories, designers demand more flexible input, interpretable output, and interactive collaboration with ML to facilitate analysis in breadth and depth. To this end, we work closely with a game company to design a visual occurrence analytics system through a stepwise co-design process. We first identify bottlenecks in game designers' conventional practices and their concerns about ML via an observational study. Then, we develop the single-match module of the visualization system to familiarize users with interactive analytics. Next, we incorporate ML models to recommend match segments of interest during occurrence classification and streamline the cross-match analysis. Empirical studies confirm the efficacy of our system. Experts' feedback suggests that our stepwise co-design process indeed helps them better embrace collaboration with machines.