Startseite A characterization of the degree of weak and strong links in doubles sports
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

A characterization of the degree of weak and strong links in doubles sports

  • Paramjit S. Gill und Tim B. Swartz EMAIL logo
Veröffentlicht/Copyright: 26. März 2019
Veröffentlichen auch Sie bei De Gruyter Brill

Abstract

This paper proposes a model that characterizes the degree to which a doubles sport (i.e. two team members) is a weak or a strong link game. The model is applied to the sport of pickleball where interest is focused on the doubles version of the sport. As a byproduct of the analysis, individual player rankings are obtained.

Acknowledgement

Swartz has been partially supported by grants from the Natural Sciences and Engineering Research Council of Canada. The authors thank the Associate Editor and two reviewers for their comments on an earlier version of the manuscript.

References

Anderson, C. and D. Sally. 2013. The Numbers Game: Why Everything You Know about Soccer is Wrong. New York: Penguin Books.Suche in Google Scholar

Carlin, B. P. and T. A. Louis. 2008. Bayesian Methods for Data Analysis. New York: Chapman and Hall/CRC.10.1201/b14884Suche in Google Scholar

de Silva, B. M., G. R. Pond, and T. B. Swartz. 2001. “Estimation of the Magnitude of Victory in One-Day Cricket.” The Australian and New Zealand Journal of Statistics 43:259–268.10.1111/1467-842X.00172Suche in Google Scholar

Fearnhead, P. and B. M. Taylor. 2011. “On Estimating the Ability of NBA Players.” Journal of Quantitative Analysis in Sports 7(3):Article 11.10.2202/1559-0410.1298Suche in Google Scholar

Gladwell, M. 2016. “My little Hundred Million.” Revisionist History. Podcast – Season 1, Episode 6 accessed on July 13/17 at http://revisionisthistory.com/episodes/06-my-little-hundred-million.Suche in Google Scholar

Hastie, T., R. Tibshirani, and J. Friedman. 2001. The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York: Springer.10.1007/978-0-387-21606-5Suche in Google Scholar

Karlis, D. and I. Ntzoufras. 2000. “On Modelling Soccer Data.” Student 3:229–244.Suche in Google Scholar

Laud, P. and J. Ibrahim. 1995. “Predictive Model Selection.” Journal of the Royal Statistical Society B 57:247–262.10.1111/j.2517-6161.1995.tb02028.xSuche in Google Scholar

Lykou, A. and I. Ntzoufras. 2011. “WinBUGS: a Tutorial.” Wiley Interdisciplinary Reviews: Computational Statistics 3:385–396.10.1002/wics.176Suche in Google Scholar

Macdonald, B. 2011. “A Regression-Based Adjusted Plus-Minus Statistic for NHL Players.” Journal of Quantitative Analysis in Sports 7(3):Article 4.10.2202/1559-0410.1284Suche in Google Scholar

Novet, A. 2017. “Strong and Weak Links: Talent Distribution Within Teams.” Hockey Graphs. accessed online July 13/17 at https://hockey-graphs.com/2017/03/14/strong-and-weak-links-talent-distribution-within-teams/#more-16199.Suche in Google Scholar

Ntzoufras, I. 2009. Bayesian Modeling Using WinBUGS. Volume 698 of Wiley Series in Computational Statistics. New York: Wiley.10.1002/9780470434567Suche in Google Scholar

Spiegelhalter, D. J., A. Thomas, N. Best, and D. J. Lund. 2003. WinBUGS (Version 1.4.3) User Manual. MRC Biostatistics Unit. UK: Cambridge.Suche in Google Scholar

Published Online: 2019-03-26
Published in Print: 2019-06-26

©2019 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 20.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/jqas-2018-0080/html
Button zum nach oben scrollen