Matchmaking Strategies for Maximizing Player Engagement in Video Games

57 Pages Posted: 24 Sep 2021 Last revised: 14 Sep 2023

See all articles by Mingliu Chen

Mingliu Chen

University of Texas at Dallas - Naveen Jindal School of Management

Adam N. Elmachtoub

Department of Industrial Engineering and Operations Research & Data Science Institute, Columbia University

Xiao Lei

HKU Business School, The University of Hong Kong

Date Written: September 22, 2021

Abstract

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. The matchmaking systems in these games determine players' opponents and thus play a vital role in maintaining player engagement. 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 losing. 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 affect 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, general churning behaviors and different winrates between players. Using the general model, 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% or reduce the percentage of bot players by 10% in comparison to skill-based matchmaking.

Keywords: matchmaking; video games; user retention; customer lifetime value

Suggested Citation

Chen, Mingliu and Elmachtoub, Adam and Lei, Xiao, Matchmaking Strategies for Maximizing Player Engagement in Video Games (September 22, 2021). Available at SSRN: https://ssrn.com/abstract=3928966 or http://dx.doi.org/10.2139/ssrn.3928966

Mingliu Chen

University of Texas at Dallas - Naveen Jindal School of Management ( email )

P.O. Box 830688
Richardson, TX 75083-0688
United States

Adam Elmachtoub

Department of Industrial Engineering and Operations Research & Data Science Institute, Columbia University ( email )

535G S.W. Mudd Building
500 West 120th Street
New York, NY 10027
United States

HOME PAGE: http://www.columbia.edu/~ae2516/

Xiao Lei (Contact Author)

HKU Business School, The University of Hong Kong ( email )

Hong Kong
China

HOME PAGE: http://www.xiao-lei.org

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