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

B. Vaziri (Corresponding author), S. Dabadghao, Y. Yih, T.L. Morin, M. Lehto

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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.

Original languageEnglish
Pages (from-to)279-295
Number of pages17
JournalOperational Research
Volume20
Issue number1
Early online date18 May 2017
DOIs
Publication statusPublished - 1 Mar 2020

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Keywords

  • Crowds
  • Decision making
  • Markov chains
  • Ranking

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