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Change is Hard: Consistent Player Behavior Across Games with Conflicting Incentives

Emily Chen, Alexander J. Bisberg, Dmitri Williams, Magy Seif El-Nasr, Emilio Ferrara

Abstract

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.

Change is Hard: Consistent Player Behavior Across Games with Conflicting Incentives

Abstract

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.
Paper Structure (52 sections, 7 equations, 3 figures, 11 tables)

This paper contains 52 sections, 7 equations, 3 figures, 11 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Player Behavior in Online Multiplayer Games
  4. Game Design and Behavioral Incentives
  5. League of Legends
  6. Teamfight Tactics
  7. Self-Determination Theory and Player Motivation
  8. Personality Psychology and Cross-Context Behavioral Stability
  9. Agency versus Structure
  10. Methods
  11. User Sampling
  12. Balancing Based on Skill and Rank
  13. Identifying and Tracking Users
  14. Data Collection
  15. Quasi-Real-Time Data Infrastructure
  16. ...and 37 more sections

Figures (3)

  • Figure 1: A 2x2 grid of heatmaps comparing TFT flexibility (x-axis) and League flexibility scores (y-axis), disaggregated by seed game by row (League seeds - top, TFT seeds - bottom) and seed skill by column(elite - left, non-elite - right). This enables us to compare the relative flexibility of the categories of users to each other.
  • Figure 2: SHAP visualization with top 20 features for our best performing model, kernel regression, predicting League flexibility. The features are from top to bottom in the order of highest to lowest feature importance to the model. The X-axis indicates the directionality a feature has on the model output.
  • Figure 3: SHAP visualization with top 20 features for our best performing model, kernel regression, predicting TFT flexibility. The features are from top to bottom in the order of highest to lowest feature importance to the model. The X-axis indicates the directionality a feature has on the model output.