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A computationally intensive ranking system for paired comparison data

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  • Beaudoin, David
  • Swartz, Tim

Abstract

In this paper, we introduce a new ranking system where the data are preferences resulting from paired comparisons. When direct preferences are missing or unclear, then preferences are determined through indirect comparisons. Given that a ranking of n subjects implies (2n) paired preferences, the resultant computational problem is the determination of an optimal ranking where the agreement between the implied preferences via the ranking and the data preferences is maximized. Comparisons are carried out via simulation studies where the proposed rankings outperform Bradley–Terry in a particular predictive comparison.

Suggested Citation

  • Beaudoin, David & Swartz, Tim, 2018. "A computationally intensive ranking system for paired comparison data," Operations Research Perspectives, Elsevier, vol. 5(C), pages 105-112.
    • Handle: RePEc:eee:oprepe:v:5:y:2018:i:c:p:105-112
    DOI: 10.1016/j.orp.2018.03.002
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    References listed on IDEAS

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    1. Mease D., 2003. "A Penalized Maximum Likelihood Approach for the Ranking of College Football Teams Independent of Victory Margins," The American Statistician, American Statistical Association, vol. 57, pages 241-248, November.
    2. R. L. Plackett, 1975. "The Analysis of Permutations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 24(2), pages 193-202, June.
    3. Mark Glickman, 2001. "Dynamic paired comparison models with stochastic variances," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(6), pages 673-689.
    4. Gandar, John M. & Zuber, Richard A. & Lamb, Reinhold P., 2001. "The home field advantage revisited: a search for the bias in other sports betting markets," Journal of Economics and Business, Elsevier, vol. 53(4), pages 439-453.
    5. Mark E. Glickman, 1999. "Parameter Estimation in Large Dynamic Paired Comparison Experiments," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(3), pages 377-394.
    6. Swartz Tim B. & Tennakoon Aruni & Nathoo Farouk & Tsao Min & Sarohia Parminder, 2011. "Ups and Downs: Team Performance in Best-of-Seven Playoff Series," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(4), pages 1-17, October.
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