An Elo-based rating system for TopCoder SRM
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2019
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| _version_ | 1866911459894624256 |
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| author | Batty, Fred |
| author_facet | Batty, Fred |
| contents | This paper presents an Elo-based rating system for programming contests, specifically Topcoder's Single Round Matches (SRMs). We introduce a logarithmic rank-based performance metric that allows single-round, multi-player contest results to be incorporated into an Elo-style continuous rating framework. Model parameters and adjustment factors are calibrated empirically by minimizing absolute prediction error over historical data, accounting for experience level, initial ratings, and competition characteristics. The resulting system demonstrates improved rank predictions and rating progressions consistent with natural skill development over player careers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1905_00961 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | An Elo-based rating system for TopCoder SRM Batty, Fred Multiagent Systems Machine Learning This paper presents an Elo-based rating system for programming contests, specifically Topcoder's Single Round Matches (SRMs). We introduce a logarithmic rank-based performance metric that allows single-round, multi-player contest results to be incorporated into an Elo-style continuous rating framework. Model parameters and adjustment factors are calibrated empirically by minimizing absolute prediction error over historical data, accounting for experience level, initial ratings, and competition characteristics. The resulting system demonstrates improved rank predictions and rating progressions consistent with natural skill development over player careers. |
| title | An Elo-based rating system for TopCoder SRM |
| topic | Multiagent Systems Machine Learning |
| url | https://arxiv.org/abs/1905.00961 |