Bayesian modelling of elite sporting performance with large databases
Author:
Affiliation:
1. Department of Statistical Science , University College London , London , UK
2. School of Sport and Exercise Sciences , University of Kent , Canterbury , UK
Abstract
Funder
Partnership for Clean Competition
University of Kent
Publisher
Walter de Gruyter GmbH
Subject
Decision Sciences (miscellaneous),Social Sciences (miscellaneous)
Link
https://www.degruyter.com/document/doi/10.1515/jqas-2021-0112/pdf
Reference37 articles.
1. Albert, J. 1999. “Comment on “Bridging Different Eras in Sports” by Berry, Reese and Larkey.” Journal of the American Statistical Association 94: 677–80. https://doi.org/10.2307/2669974.
2. Allen, S. V., T. J. Vandenbogaerde, and W. G. Hopkins. 2014. “Career Performance Trajectories of Olympic Swimmers: Benchmarks for Talent Development.” European Journal of Sport Science 14: 643–51. https://doi.org/10.1080/17461391.2014.893020.
3. Azzalini, A. 1985. “A Class of Distributions Which Includes the Normal Ones.” Scandinavian Journal of Statistics 12: 171–8.
4. Azzalini, A., and A. Capitanio. 2003. “Distributions Generated by Perturbation of Symmetry with Emphasis on a Multivariate Skew T-Distribution.” Journal of the Royal Statistical Society: Series B 65: 367–89. https://doi.org/10.1111/1467-9868.00391.
5. Berry, S. M., C. S. Reese, and P. D. Larkey. 1999. “Bridging Different Eras in Sports.” Journal of the American Statistical Association 94: 661–76. https://doi.org/10.1080/01621459.1999.10474163.
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