The Curse of Scoreless Draws in Soccer: The Relationship with a Team's Offensive, Defensive, and Overall Performance

Ben Van Calster, Smits Tim and Sabine Van Huffel
Additional contact information
Ben Van Calster: Department of Electrical Engineering (ESAT-SCD), Katholieke Universiteit Leuven, Belgium
Smits Tim: Center for Ethics, Katholieke Universiteit Leuven, Belgium; and Department of Communication Sciences, Universiteit Antwerpen, Belgium
Sabine Van Huffel: Department of Electrical Engineering (ESAT-SCD), Katholieke Universiteit Leuven, Belgium

Journal of Quantitative Analysis in Sports, 2008, vol. 4, issue 1, 24

Abstract: In soccer, a scoreless draw is typically considered as an unwanted result that often jeopardizes the spectacle value of the game. In the present work, we tried to investigate a) how common scoreless draws are, and b) the relationship between scoreless draws and other indices of a football team's performance using machine learning techniques. Using data from 54 competitions around the world, Bayesian networks, least squares support vector machines and Hybrid Monte Carlo multi-layer perceptrons were used to investigate which combination of indices best predicts a team's season proportion of scoreless draws and to see exactly what predictions these indices make. There was ample variability in the proportion of scoreless draws, both when comparing individual teams and countries. For individual teams, the percentage scoreless draws varied between 0 and 30%. On average, nearly 9% of all games ended in 0-0. Not surprisingly, the most important parameter appeared to be the total number of goals per game. More interestingly, the earned points per game were also linked to the proportion of scoreless draws. Games of average and bad-to-average teams more often resulted in a scoreless draw, in particular when the games of these teams saw few goals. Such a team could end up having 20% scoreless draws in one season. A suggestive result that more spectators may be associated with less scoreless draws is also presented.

Keywords: soccer; scoreless draw; least squares support vector machines; Bayesian neural networks; Hybrid Monte Carlo (search for similar items in EconPapers)
Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://doi.org/10.2202/1559-0410.1089 (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:4:y:2008:i:1:n:4

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

DOI: 10.2202/1559-0410.1089

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 ().