UNDERSTANDING PRECISION: AN ANALYSIS OF MOUSE SENSITIVITY, DPI, AND ROLE PREFERENCES THROUGH FITTS’S LAW IN OVERWATCH 2

Date

2024-6-26

Author

Boyacioglu, Fehmi Cem

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This study explores the dynamics between mouse sensitivity and dots per inch (DPI) settings and player roles in Overwatch 2, applying Fitts's Law to analyze their effects on gameplay performance. In Overwatch 2, players assume specific roles. Damage (DPS) players focus on attacking, Tanks absorb damage and protect teammates, Support players provide healing and buffs, and Flex players adeptly switch roles to meet the team's needs. Professional players aged 16 to 37 years, with a "Master" rank or higher and over 500 hours of gameplay experience, formed a participant base of 545 individuals. These players, who also competed in at least one official tournament, provided a rich dataset for analysis through open-access databases like ProSettings and Liquipedia using Python scripts for API and HTML extraction. Significant findings include role-specific DPI preferences, with damage players favoring higher DPI for agility, reflected in statistical analysis (e.g., Levene's Test indicating variance in sensitivity settings across roles, F(3, 524) = 2.37, p < .05). This study not only highlights the strategic selection of DPI and sensitivity settings but also v applies Fitts's Law to esports, illustrating the critical balance between precision and speed necessary for competitive success. The methodology, emphasizing data collection from publicly verifiable sources, ensures transparency and reproducibility in esports research, contributing valuable insights into optimizing player performance and interface design in gaming.

Subject Keywords

Mouse Sensitivity, First-Person Shooter, Fitts' Law, Esports, Human-Computer Interaction

URI

https://hdl.handle.net/11511/110039

Collections

Graduate School of Social Sciences, Thesis