Heterogeneous Effects of Software Patches in a Multiplayer Online Battle Arena Game
arXiv:2110.14632 · doi:10.1145/3472538.3472550
Abstract
The popularity of online gaming has grown dramatically, driven in part by streaming and the billion-dollar e-sports industry. Online games regularly update their software to fix bugs, add functionality that improve the game's look and feel, and change the game mechanics to keep the games fun and challenging. An open question, however, is the impact of these changes on player performance and game balance, as well as how players adapt to these sudden changes. To address these questions, we use causal inference to measure the impact of software patches to League of Legends, a popular team-based multiplayer online game. We show that game patches have substantially different impacts on players depending on their skill level and whether they take breaks between games. We find that the gap between good and bad players increases after a patch, despite efforts to make gameplay more equal. Moreover, longer between-game breaks tend to improve player performance after patches. Overall, our results highlight the utility of causal inference, and specifically heterogeneous treatment effect estimation, as a tool to quantify the complex mechanisms of game balance and its interplay with players' performance.
arXiv comment9 pages, 11 figures
References in corpus (5)
- Recursive Partitioning for Heterogeneous Causal Effects
- Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning
- Deep Neural Networks for Optimal Team Composition
- False Discovery Rate Controlled Heterogeneous Treatment Effect Detection for Online Controlled Experiments
- Learning Triggers for Heterogeneous Treatment Effects