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Lookup NU author(s): Dr Pete PhilipsonORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
A truncated, mean-parameterized Conway-Maxwell-Poisson model is developed to handle under- and overdispersed count data owing to individual heterogeneity. The truncated nature of the data allows for a more direct implementation of the model than is utilized in previous work without too much computational burden. The model is applied to a large dataset of Test match cricket bowlers, where the data are in the form of small counts and range in time from 1877 to the modern day, leading to the inclusion of temporal effects to account for fundamental changes to the sport and society. Rankings of sportsmen and women based on a statistical model are often handicapped by the popularity of inappropriate traditional metrics, which are found to be flawed measures in this instance. Inferences are made using a Bayesian approach by deploying a Markov Chain Monte Carlo algorithm to obtain parameter estimates and to extract the innate ability of individual players. The model offers a good fit and indicates that there is merit in a more sophisticated measure for ranking and assessing Test match bowlers.
Author(s): Philipson P
Publication type: Article
Publication status: Published
Journal: Statistical Modelling
Year: 2023
Pages: Epub ahead of print
Online publication date: 19/09/2023
Acceptance date: 31/01/2023
Date deposited: 13/03/2024
ISSN (print): 1471-082X
ISSN (electronic): 1477-0342
Publisher: Sage Publications Ltd
URL: https://doi.org/10.1177/1471082X231178584
DOI: 10.1177/1471082X231178584
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