讲座：Matchmaking Strategies for Maximizing Player Engagement in Video Games 发布时间：2023-09-21
题 目：Matchmaking Strategies for Maximizing Player Engagement in Video Games
主持人：孙海龙 助理教授 国产超高效液相色谱
Managing player engagement is a crucial challenge in the online gaming industry, as many games generate revenue through subscription models and microtransactions. Competitive video games, a prominent category of online games, involve players being repeatedly matched against one another. In such games, the matchmaking system, which determines a player's opponents, plays a vital role in maintaining player engagement. In this paper, we examine the value and impact of matchmaking strategies in competitive video games. We propose a dynamic model to analyze player dynamics and optimize matchmaking policies for maximum engagement. Our model takes into account two essential factors in competitive games: heterogeneous skill levels and players' aversion to losses. Additionally, the model enables us to consider pay-to-win (PTW) strategies and AI-powered bots, which are contentious methods of influencing player engagement, and endogenously determine the optimal matchmaking policy. To provide sharp insights, we analyze a specific case where there are two skill levels, and players discontinue playing only after experiencing a losing streak. The optimal matchmaking policy considers both short-term rewards by matching players myopically and long-term rewards by adjusting skill distribution. The pay-to-win system can positively impact player engagement when the majority of players are low-skilled, as adopting pay-to-win also affects skill distribution. This result challenges the conventional wisdom that typically regards pay-to-win as trading player experience for revenue. When incorporating AI-powered bots, we demonstrate that optimizing the matchmaking policy can significantly reduce the number of required bots without impacting engagement, thereby addressing the overuse of bots. We then extend our model to accommodate multiple skill levels as well as more general player behavior, and showcase the superiority of the optimal policy over the status quo (skill-based matchmaking) in various scenarios. Notably, we find that the benefit of the optimal policy increases exponentially with the number of skill levels. Finally, we conduct a case study with real data from an online chess platform. We show that the optimal policy can improve engagement by 4-6% in the absence of new player arrival or reduce the percentage of bot players by 10% in comparison to skill-based matchmaking.
Xiao Lei is an assistant professor at HKU Business School. He received his doctoral degree in Operations Research at Columbia University. His research interests include online marketplaces, revenue management and pricing, and social operations management. His work has been recognized by INFORMS Service Science Best Student Paper Award, the Jeff McGill Best Student Paper Award, and CSAMSE Best Paper Award. In the summer of 2020, he worked as a data science intern at Activision Blizzard.