On the linear ordering problem and the rankability of data

Thomas R. Cameron; Sebastian Charmot; Jonad Pulaj

doi:10.3934/fods.2021010

Article Contents

2021, Volume 3, Issue 2: 133-149. Doi: 10.3934/fods.2021010

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On the linear ordering problem and the rankability of data

1.
Penn State Behrend, 1 Prischak Building, Erie, PA, 16563, USA
2.
Davidson College, P.O. Box 7129, Davidson, NC, 28035, USA

Received: November 30, 2020

Revised: February 28, 2021

Early access: April 2021

Published: June 2021

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Abstract

In 2019, Anderson et al. proposed the concept of rankability, which refers to a dataset's inherent ability to be meaningfully ranked. In this article, we give an expository review of the linear ordering problem (LOP) and then use it to analyze the rankability of data. Specifically, the degree of linearity is used to quantify what percentage of the data aligns with an optimal ranking. In a sports context, this is analogous to the number of games that a ranking can correctly predict in hindsight. In fact, under the appropriate objective function, we show that the optimal rankings computed via the LOP maximize the hindsight accuracy of a ranking. Moreover, we develop a binary program to compute the maximal Kendall tau ranking distance between two optimal rankings, which can be used to measure the diversity among optimal rankings without having to enumerate all optima. Finally, we provide several examples from the world of sports and college rankings to illustrate these concepts and demonstrate our results.

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Mathematics Subject Classification: 52B12, 90C09, 90C27, 90C35.

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Figure 1. Linear Ordering Polytope: $ P^{3}_{ \rm{LO}} $

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Figure 2. Simple Digraphs on $ 3 $ vertices

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Figure 3. Optimal Solution(s) on Linear Ordering Polytope

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Figure 4. College Features from U.S. World News 2013 Rankings

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Figure 5. The degree of linearity and the hindsight accuracy of NFL rankings

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Figure 6. Correlation between the degree of linearity and the hindsight accuracy of NFL rankings

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Figure 7. The absolute difference between the foresight accuracy of two optimal rankings

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Figure 8. Diversity among NFL playoff teams in two optimal rankings for years 1992 and 1999

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Figure 9. NFL playoff progression in 1992; the opaque team lost the indicated matchup

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Figure 10. NFL playoff progression in 1999; the opaque team lost the indicated matchup

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