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.

翻译：本文提出了一种适用于编程竞赛（特别是Topcoder单轮比赛SRM）的基于Elo评级的评分系统。我们引入了一种基于对数排名的表现度量方法，使得单轮、多选手的竞赛结果能够纳入Elo式连续评级框架。通过最小化历史数据上的绝对预测误差，并结合选手经验水平、初始评级及竞赛特征，我们对模型参数与调整因子进行了经验性校准。实验表明，该评分系统在排名预测方面表现更优，其评级变化趋势也与选手职业生涯中技能的自然发展规律相吻合。