Estimating the effect of ‘diverse’ team compositions on Dota 2 game outcomes using Inverse Probability Weighting

Bachelor thesis (2022)

Authors

C.N. Goedhart Electrical Engineering, Mathematics and Computer Science

Contributors

J.H. Krijthe Pattern Recognition and Bioinformatics - EEMCS (mentor)
R.K.A. Karlsson Pattern Recognition and Bioinformatics - EEMCS (mentor)
K.A. Hildebrandt Computer Graphics and Visualisation - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Christof Goedhart
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Published Date

24-06-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Commonly, when researchers are figuring out the effect of a putative cause, additional variables influence the cause and the effect. These are called confounders, and they obfuscate causal relationships. Inverse Probability Weighting is a method that can be applied to remove confounding and show a causal effect. This study aims to determine if Dota 2 game outcomes can be predicted based on team composition diversity and if 'Inverse Probability Weighing is a helpful tool for this. First, metrics to assess team diversity were determined, and the two confounders, "Player skill" and "Hero skill", to the 'is-diverse' treatment and game outcome were identified. Next, a dataset with Dota 2 games was gathered containing all the relevant variables to measure the confounders. Finally, inverse probability weighting was applied to measure the Average Treatment Effect of the putative cause.
The results suggest that team diversity significantly impacts the game outcome. However, the results were close in most cases when comparing the results with data that was not corrected for by confounders. A possible explanation may be that the confounders didn't have enough influence on some diversity metrics since they were too vague. For example, the most complex diversity metric, which covered the most team-composition characteristics, showed a clear difference between the average treatment effect with inverse probability weighting being applied and not applied.