Abstract Number:
3127
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Jonathan Che (1)
Institutions:
(1) N/A, N/A
First Author:
Presenting Author:
Abstract Text:
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.
Keywords:
Sports statistics|Athlete rating|Bayesian statistics|Dynamic linear model|Kalman filter|Non-normal data
Sponsors:
Section on Statistics in Sports
Tracks:
Miscellaneous
Can this be considered for alternate subtype?
No
Are you interested in volunteering to serve as a session chair?
Yes
I have read and understand that JSM participants must abide by the Participant Guidelines.
Yes
I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.
I understand