A state-space perspective on modelling and inference for online skill rating
总结了竞技体育中常用的技能评分方法，并基于序贯蒙特卡洛和离散隐马尔可夫模型提出新方法，强调状态空间模型视角，探讨了滤波、平滑和参数估计三阶段推断，以及扩展到大量选手和比赛时的挑战与近似方法。
Abstract We summarize popular methods used for skill rating in competitive sports, along with their inferential paradigms and introduce new approaches based on sequential Monte Carlo and discrete hidden Markov models. We advocate for a state-space model perspective, wherein players’ skills are represented as time-varying, and match results serve as observed quantities. We explore the steps to construct the model and the three stages of inference: filtering, smoothing, and parameter estimation. We examine the challenges of scaling up to numerous players and matches, highlighting the main approximations and reductions which facilitate statistical and computational efficiency. We additionally compare approaches in a realistic experimental pipeline that can be easily reproduced and extended with our open-source Python package, abile.