Ludometrics: luck, and how to measure it

Gilbert Daniel E. () and Wells Martin T.
Additional contact information
Gilbert Daniel E.: Cornell University, Department of Statistics and Data Science, Ithaca NY, USA
Wells Martin T.: Cornell University, Department of Statistics and Data Science, Ithaca NY, USA

Journal of Quantitative Analysis in Sports, 2019, vol. 15, issue 3, 225-237

Abstract: Game theory is the study of tractable games which may be used to model more complex systems. Board games, video games and sports, however, are intractable by design, so “ludological” theories about these games as complex phenomena should be grounded in empiricism. A first “ludometric” concern is the empirical measurement of the amount of luck in various games. We argue against a narrow view of luck which includes only factors outside any player’s control, and advocate for a holistic definition of luck as complementary to the variation in effective skill within a population of players. We introduce two metrics for luck in a game for a given population – one information theoretical, and one Bayesian, and discuss the estimation of these metrics using sparse, high-dimensional regression techniques. Finally, we apply these techniques to compare the amount of luck between various professional sports, between Chess and Go, and between two hobby board games: Race for the Galaxy and Seasons.

Keywords: Bradley-Terry model; game theory; generalized linear models; luck; ludology; skill (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/jqas-2018-0103 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:jqsprt:v:15:y:2019:i:3:p:225-237:n:2

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/jqas/html

DOI: 10.1515/jqas-2018-0103

Access Statistics for this article

Journal of Quantitative Analysis in Sports is currently edited by Mark Glickman

More articles in Journal of Quantitative Analysis in Sports from De Gruyter
Bibliographic data for series maintained by Peter Golla ().