Athlete rating in multi-competitor games with scored outcomes via monotone transformations

TL;DR

This paper introduces a Bayesian dynamic linear model with learned monotone transformations to accurately estimate athlete abilities from non-normal scored game data, demonstrated on Olympic sports datasets.

Contribution

It proposes a novel non-normality correction method using flexible monotone transformations within a Bayesian framework for athlete rating.

Findings

Effective modeling of non-normal score data.

Applicable to various Olympic sports datasets.

Easily implemented with standard tools in R.

Abstract

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.