## 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.

Original language | English (US) |
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Pages (from-to) | 133-149 |

Number of pages | 17 |

Journal | Foundations of Data Science |

Volume | 3 |

Issue number | 2 |

DOIs | |

State | Published - Jun 2021 |

## All Science Journal Classification (ASJC) codes

- Computational Theory and Mathematics
- Analysis
- Statistics and Probability
- Applied Mathematics