EconPapers    
Economics at your fingertips  
 

Understanding draws in Elo rating algorithm

Szczecinski Leszek () and Djebbi Aymen ()
Additional contact information
Szczecinski Leszek: INRS, Montreal, Canada
Djebbi Aymen: INRS, Montreal, Canada

Journal of Quantitative Analysis in Sports, 2020, vol. 16, issue 3, 211-220

Abstract: This work is concerned with the interpretation of the results produced by the well known Elo algorithm applied in various sport ratings. The interpretation consists in defining the probabilities of the game outcomes conditioned on the ratings of the players and should be based on the probabilistic rating-outcome model. Such a model is known in the binary games (win/loss), allowing us to interpret the rating results in terms of the win/loss probability. On the other hand, the model for the ternary outcomes (win/loss/draw) has not been yet shown even if the Elo algorithm has been used in ternary games from the very moment it was devised. Using the draw model proposed by Davidson in 1970, we derive a new Elo-Davidson algorithm, and show that the Elo algorithm is its particular instance. The parameters of the Elo-Davidson are then related to the frequency of draws which indicates that the Elo algorithm silently assumes games with 50% of draws. To remove this assumption, often unrealistic, the Elo-Davidson algorithm should be used as it improves the fit to the data. The behaviour of the algorithms is illustrated using the results from English Premier League.

Keywords: draws; Elo algorithm; rating in sports (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://doi.org/10.1515/jqas-2019-0102 (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:16:y:2020:i:3:p:211-220:n:5

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

DOI: 10.1515/jqas-2019-0102

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 ().

 
Page updated 2025-03-19
Handle: RePEc:bpj:jqsprt:v:16:y:2020:i:3:p:211-220:n:5