As player demand for information about esports games surges, live companion tools were developed to help them understand game-related knowledge, make decisions, and review post-game data.Despite growing interest, little is currently known about how different users perceive and use commercially available apps and their different features.However, this is important for creating tools that actually meet players' needs.To help fill this gap, we selected two companion apps for two different competitive game genres and explored them with respect to four dimensions -understandability, informativeness, decision making, and trustworthiness -across professional and nonprofessional players.Towards this end, we first conducted an online survey to assess users' perception around these dimensions.This was followed by online interviews to gain further insights.Through statistical and thematic analysis, our results highlight commonalities and disparities in user behavior and preferences across user groups, contributing to our understanding and future design of live companion tools.

With the growth of competitive gaming and esports, training support tools that offer feedback on players’ performance to assist in skill development have witnessed increased demand. These tools increasingly not only provide prospective and retrospective analyses but also live feedback during gameplay itself. Thus, such ’live companions’ provide overarching training support across the different phases of play through a variety of features. To understand how these tools work, we carried out an analysis of the features offered by commercially available live companions. For our analysis, we selected 15 live companion tools for two popular competitive games, namely Valorant and League of Legends. These games are representatives of first person shooters and multiplayer online battle arena games. Based on our analysis, we provide an overview of the various features offered by such tools, how frequent these features are, and if there are differences between the two games. Finally, we reflect on potential future research directions for this emerging topic of study.

Players increasingly adopt a data-driven approach to review and improve their gaming skills. In the wake of this, spatio-temporal visualizations gained popularity but remain challenging to design. Storyline visualizations are unique in the way they integrate time and location information into a single view to show how entity relationships develop over time. We adopt the storyline visualization technique to summarize gameplay for the purpose of post-play review. We demonstrate the method by applying it to League of Legends matches and evaluated it with 39 players of the game in a task-based online study using the triad framework for spatio-temporal queries by Peuquet. Results indicate that players responded positively to the approach and could, by and large, solve tasks well but that time-based tasks proved most challenging and least efficient to solve. Based on our findings, we reflect on possibilities for enhancing the design of storyline visualizations for game-related data analysis.