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

Author

Listed:
  • Baback Vaziri

    (James Madison University)

  • Shaunak Dabadghao

    (Eindhoven University of Technology)

  • Yuehwern Yih

    (Purdue University)

  • Thomas L. Morin

    (Purdue University)

  • Mark Lehto

    (Purdue University)

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.

Suggested Citation

  • Baback Vaziri & Shaunak Dabadghao & Yuehwern Yih & Thomas L. Morin & Mark Lehto, 2020. "Crowd-Ranking: a Markov-based method for ranking alternatives," Operational Research, Springer, vol. 20(1), pages 279-295, March.
    • Handle: RePEc:spr:operea:v:20:y:2020:i:1:d:10.1007_s12351-017-0324-7
    DOI: 10.1007/s12351-017-0324-7
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    References listed on IDEAS

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    3. Paul Kvam & Joel S. Sokol, 2006. "A logistic regression/Markov chain model for NCAA basketball," Naval Research Logistics (NRL), John Wiley & Sons, vol. 53(8), pages 788-803, December.
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    5. Herm, Steffen & Callsen-Bracker, Hans-Markus & Kreis, Henning, 2014. "When the crowd evaluates soccer players’ market values: Accuracy and evaluation attributes of an online community," Sport Management Review, Elsevier, vol. 17(4), pages 484-492.
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    Cited by:

    1. Phan-Anh-Huy Nguyen & Ping-Yu Hsu, 2023. "Robust and High-Accessibility Ranking Method for Crowdsourcing-Based Decision Making," Group Decision and Negotiation, Springer, vol. 32(5), pages 1211-1236, October.

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