Rating Players of Counter-Strike: Global Offensive Based on Plus/Minus value
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909308645539840 |
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| author | Xu, Hongyu Moka, Sarat |
| author_facet | Xu, Hongyu Moka, Sarat |
| contents | We propose a player rating mechanism for Counter-Strike: Global Offensive (CS ), a popular e-sport, by analyzing players' Plus/Minus values. The Plus/Minus value represents the average point difference between a player's team and the opponent's team across all matches the player has participated in. Using models such as regularized linear regression, logistic regression, and Bayesian linear models, we examine the relationship between player participation and team point differences. The most commonly used metric in the CS community is "Rating 2.0," which focuses solely on individual performance and does not account for indirect contributions to team success. Our approach introduces a new rating system that evaluates both direct and indirect contributions of players, prioritizing those who make a tangible impact on match outcomes rather than those with the highest individual scores. This rating system could help teams distribute rewards more fairly and improve player recruitment. We believe this methodology will positively influence not only the CS community but also the broader e-sports industry. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05052 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Rating Players of Counter-Strike: Global Offensive Based on Plus/Minus value Xu, Hongyu Moka, Sarat Applications We propose a player rating mechanism for Counter-Strike: Global Offensive (CS ), a popular e-sport, by analyzing players' Plus/Minus values. The Plus/Minus value represents the average point difference between a player's team and the opponent's team across all matches the player has participated in. Using models such as regularized linear regression, logistic regression, and Bayesian linear models, we examine the relationship between player participation and team point differences. The most commonly used metric in the CS community is "Rating 2.0," which focuses solely on individual performance and does not account for indirect contributions to team success. Our approach introduces a new rating system that evaluates both direct and indirect contributions of players, prioritizing those who make a tangible impact on match outcomes rather than those with the highest individual scores. This rating system could help teams distribute rewards more fairly and improve player recruitment. We believe this methodology will positively influence not only the CS community but also the broader e-sports industry. |
| title | Rating Players of Counter-Strike: Global Offensive Based on Plus/Minus value |
| topic | Applications |
| url | https://arxiv.org/abs/2409.05052 |