Human Team Behavior and Predictability in the Massively Multiplayer Online Game WOT Blitz

Frank Emmert-Streib, Shailesh Tripathi, Matthias Dehmer

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Massively multiplayer online games (MMOGs) played on the Web provide a new form of social, computer-mediated interactions that allow the connection of millions of players worldwide. The rules governing team-based MMOGs are typically complex and nondeterministic giving rise to an intricate dynamical behavior. However, due to the novelty and complexity of MMOGs, their behavior is understudied. In this article, we investigate the MMOG World of Tanks Blitz by using a combined approach based on data science and complex adaptive systems. We analyze data on the population level to get insights into organizational principles of the game and its game mechanics. For this reason, we study the scaling behavior and the predictability of system variables. As a result, we find a power-law behavior on the population level revealing long-range interactions between system variables. Furthermore, we identify and quantify the predictability of summary statistics of the game and its decomposition into explanatory variables. This reveals a heterogeneous progression through the tiers and identifies only a single system variable as key driver for the win rate.

Original languageEnglish
Article number5
JournalACM Transactions on the Web
Issue number1
Publication statusPublished - 11 Oct 2023


  • complex system
  • computational social science
  • human behavior
  • Massively multiplayer online games
  • prediction
  • statistical model


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