论文标题
通过单调转换，具有得分成果的多竞争者游戏中的运动员评分
Athlete rating in multi-competitor games with scored outcomes via monotone transformations
论文作者
论文摘要
体育组织通常想估计运动员的优势。对于具有得分效果的游戏，一种常见的方法是假设观察到的游戏得分遵循正常的分配条件，这可能会随着时间的推移而变化。但是，在许多游戏中，这种有条件正态性的假设不存在。为了使用非正常游戏得分数据来估算运动员的时间变化潜在能力，我们提出了具有灵活的单调响应转换的贝叶斯动态线性模型。我们的模型学习了非线性单调转换以解决运动员得分的非正常性，并且可以使用标准回归和优化程序轻松拟合。我们在R中的DLMT软件包中实现了。我们在R.我们的几种奥运会的数据中演示了我们的几种奥运会数据的方法，包括Biathlon，Diving，Rugby，Rugby和Fencing。
Sports organizations often want to estimate athlete strengths. For games with scored outcomes, a common approach is to assume observed game scores follow a normal distribution conditional on athletes' latent abilities, which may change over time. In many games, however, this assumption of conditional normality does not hold. To estimate athletes' time-varying latent abilities using non-normal game score data, we propose a Bayesian dynamic linear model with flexible monotone response transformations. Our model learns nonlinear monotone transformations to address non-normality in athlete scores and can be easily fit using standard regression and optimization routines, which we implement in the dlmt package in R. We demonstrate our method on data from several Olympic sports, including biathlon, diving, rugby, and fencing.
