This paper examines how player flexibility – a player’s willingness to engage in a breadth of options or specialize – manifests across two gaming environments: League of Legends (League) and Teamfight Tactics (TFT). We analyze the gameplay decisions of 4,830 players who have played at least 50 competitive games in both titles and explore cross-game dynamics of behavior retention and consistency. Our work introduces a novel cross-game analysis that tracks the same players’ behavior across two different environments, reducing self-selection bias. Our findings reveal that while games incentivize different behaviors (specialization in League versus flexibility in TFT) for performance-based success, players exhibit consistent behavior across platforms. This study contributes to long-standing debate about agency versus structure, showing individual agency may be more predictive of cross-platform behavior than game-imposed structure in competitive settings. These insights offer implications for game developers, designers and researchers interested in building systems to promote behavior change.

STRIVE is a taxonomy for multiplayer games organized around player behavior features such as sociality, communication, and experience. We extend STRIVE from Sky: Children of the Light, a relationship-focused social game, to World of Tanks (WoT), a game centered on team combat, player clans, and competitive performance. Using three player data snapshots from 2020 and the original STRIVE framework, we recover four stable player types in WoT: Veterans, Socialites, Squad Players, and Newbies. These segments persist across time and align with the original STRIVE dimensions, while adding a WoT-specific performance dimension. Prediction experiments illustrate that future battle participation varies in predictability across player types: Veterans and Squad Players are consistently more predictable than Socialites and Newbies. Together, these findings support STRIVE as a generalizable framework for comparing social play across multiple games.

We introduce STRIVE, a framework to build socio-behavioral taxonomies in multiplayer online games using unsupervised learning on common features across many social games.This work demonstrates this framework on "Sky: Children of the Light," a social adventure game by Thatgamecompany, using features such as cooperative play, social bonding, and in-game communication.After performing descriptive statistics, clustering and dimensionality reduction, we assign semantic categories to these behavior clusters.Next we perform a behavior prediction experiment where the most social cluster's behavior has a higher correlation with future play time and chats sent than predicting on the full dataset.These results suggest the importance of customized, or personalized, player behavior prediction models.Moreover, this framework could be easily extended to other games and further augment our understanding of human behavior in virtual worlds, ultimately aiding social scientists and game designers to better match players together for healthier online interactions.

The present research on team collaboration is typically performed through qualitative interview based studies or social network measurements of connectedness through co-play. In this study, we take the unique approach to build networks from direct messages between players in the massive online game World of Tanks where players self-organize into clans with specific roles assigned from military rankings (from Private to Commander). We explore the relationship between team communication volume and skill level, the impact of communication features on clan rating, and the differences in communication hierarchy between high and low-rated clans. Our findings reveal that higher-rated clans send more pre-battle chat messages, suggesting that effective communication and strategic planning are key to team performance. Evidence shows teams who use voice chat during battle are significantly higher ranked. Finally, we reveal that the highest rated clans have more connected lower-ranked members emphasizing that these teams are ''only as strong as their weakest link.'' This research is guided by the Transactive Memory Systems and Collective Intelligence theories which serve to expand the contribution of this research outside of games to other forms of virtual collaboration.

This study investigates the concept of flexibility within League of Legends, a popular online multiplayer game, focusing on the relationship between user adaptability and team success. Utilizing a dataset encompassing players of varying skill levels and play styles, we calculate two measures of flexibility for each player: overall flexibility and temporal flexibility. Our findings suggest that the flexibility of a user is dependent upon a user’s preferred play style, and flexibility does impact match outcome. This work also shows that skill level not only indicates how willing a player is to adapt their play style but also how their adaptability changes over time. This paper highlights the duality and balance of specialization versus flexibility, providing insights that can inform strategic planning, collaboration and resource allocation in competitive environments.

Win prediction is crucial to understanding skill modeling, teamwork and matchmaking in esports. In this paper we propose GCN-WP, a semi-supervised win prediction model for esports based on graph convolutional networks. This model learns the structure of an esports league over the course of a season (1 year) and makes predictions on another similar league. This model integrates over 30 features about the match and players and employs graph convolution to classify games based on their neighborhood. Our model achieves state-of-the-art prediction accuracy when compared to machine learning or skill rating models for LoL. The framework is generalizable so it can easily be extended to other multiplayer online games.

Understanding social interactions and generous behaviors have long been of considerable interest in the social sciences community. While the contagion of generosity is documented in the real world, less is known about such phenomenon in virtual worlds and whether it has an actionable impact on user behavior and retention. In this work, we analyze social dynamics in the virtual world of the popular massively multiplayer online role-playing game (MMORPG) Sky: Children of Light. We develop a framework to reveal the patterns of generosity in such social environments and provide empirical evidence of social contagion and contagious generosity. Players become more engaged in the game after playing with others and especially with friends. We also find that players who experience generosity first-hand or even observe other players conduct generous acts become more generous themselves in the future. Additionally, we show that both receiving and observing generosity lead to higher future engagement in the game. Since Sky resembles the real world from a social play aspect, the implications of our findings also go beyond this virtual world.