2778. The performance evolution of Counter-Strike teams
Contributed abstract in session WB-40: Sports analytics, stream Sports and Entertainment.
Wednesday, 10:30-12:00Room: Newlyn LG.02
Authors (first author is the speaker)
| 1. | Jan Rejthar
|
| Department of Econometrics, Prague University of Economics and Business |
Abstract
This study examines the performance of newly assembled Counter-Strike: Global Offensive (CS: GO) teams. The study employs data about 789 professional or semiprofessional CS: GO teams from 7,328 matches between 2012 and 2023. Using semiparametric generalized additive mixed-effects models, the study assesses how team performance evolves over time. The results reveal a significant decline in performance during the first three months of a team's formation, followed by a plateau, suggesting that teams initially overperform. These findings support the presence of a novelty effect in CS: GO and underscore the need for strategies to sustain performance beyond the early success phase.
Keywords
- Analytics and Data Science
- Decision Analysis
- Economic Modeling
Status: accepted
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