Crowd-Ranking: a Markov-based method for ranking alternatives

Baback Vaziri (), Shaunak Dabadghao (), Yuehwern Yih (), Thomas L. Morin () and Mark Lehto ()
Additional contact information
Baback Vaziri: James Madison University
Shaunak Dabadghao: Eindhoven University of Technology
Yuehwern Yih: Purdue University
Thomas L. Morin: Purdue University
Mark Lehto: Purdue University

Operational Research, 2020, vol. 20, issue 1, No 11, 279-295

Abstract: Abstract Many ranking algorithms rank a set of alternatives based on their performance in a set of pairwise comparisons. In this study, a special scenario is observed in which the objective is to rate and rank a set of groups in a traditional recruiting situation, in which the groups extend offers to the set of individuals, and the individuals will select one of their available offers. The new ranking method, Crowd-Ranking, uses collective wisdom and decision-making in conjunction with Markov chains to create competitive matches between alternatives and ultimately provide a ranking of the alternatives. First, the method is evaluated by its performance in a perfect season scenario. Next, it is applied to the case of NCAA football recruiting in the power conferences (ACC, Big Ten, Big 12, Pac 12 and SEC) in the Football Bowl Subdivision. For the Big Ten conference, the method performs significantly better than popular existing services at predicting future team performance based on past recruiting rankings. For a comprehensive national ranking of the power conferences, there is no statistically significant difference between Crowd-Ranking and the other methods.

Keywords: Decision making; Markov chains; Ranking; Crowds (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s12351-017-0324-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:operea:v:20:y:2020:i:1:d:10.1007_s12351-017-0324-7

Ordering information: This journal article can be ordered from
https://www.springer ... search/journal/12351

DOI: 10.1007/s12351-017-0324-7

Access Statistics for this article

Operational Research is currently edited by Nikolaos F. Matsatsinis, John Psarras and Constantin Zopounidis

More articles in Operational Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().