2021/05/28 by Arman Dehpanah, Muheeb Faizan Ghori, Dehpanah, Arman +5
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Performance (cs.PF) #Simulation Techniques and Applications #Sports Analytics and Performance #cs.AI #cs.IR #cs.PF
paper · pdf · doi:10.48550/arxiv.2105.14069
Updated references -- 10 pages, 1 figure, Accepted in the 23rd International Conference on Artificial Intelligence (ICAI'21)
openalex publication_date 2021/05/28 · arxiv created 2021/06/29 · arxiv updated 2021/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Online competitive games have become a mainstream entertainment platform. To create a fair and exciting experience, these games use rating systems to match players with similar skills. While there has been an increasing amount of research on improving the performance of these systems, less attention has been paid to how their performance is evaluated. In this paper, we explore the utility of several metrics for evaluating three popular rating systems on a real-world dataset of over 25,000 team battle royale matches. Our results suggest considerable differences in their evaluation patterns. Some metrics were highly impacted by the inclusion of new players. Many could not capture the real differences between certain groups of players. Among all metrics studied, normalized discounted cumulative gain (NDCG) demonstrated more reliable performance and more flexibility. It alleviated most of the challenges faced by the other metrics while adding the freedom to adjust the focus of the evaluations on different groups of players.