Ranking dynamics and volatility

García-Zorita, Carlos; Rousseau, Ronald; Marugán-Lázaro, Sergio; Casado, Elías Sanz

doi:10.1016/j.joi.2018.04.005

Cited by 9 publications

(7 citation statements)

References 11 publications

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“…Here, we report on the existence of generic features of rank dynamics over a wide array of systems, from individuals to countries, and spatio-temporal scales, from minutes to centuries. By measuring the flux of elements across ranking lists 46 – 48 , we identify a continuum ranging from systems where highly ranked elements are more stable than the rest, to systems where the least relevant elements are also stable. We show that simple mechanisms relying on fluxes generated by displacement and replacement of elements can account for all observed patterns of rank stability.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of ranking

et al. 2022

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Virtually anything can be and is ranked; people, institutions, countries, words, genes. Rankings reduce complex systems to ordered lists, reflecting the ability of their elements to perform relevant functions, and are being used from socioeconomic policy to knowledge extraction. A century of research has found regularities when temporal rank data is aggregated. Far less is known, however, about how rankings change in time. Here we explore the dynamics of 30 rankings in natural, social, economic, and infrastructural systems, comprising millions of elements and timescales from minutes to centuries. We find that the flux of new elements determines the stability of a ranking: for high flux only the top of the list is stable, otherwise top and bottom are equally stable. We show that two basic mechanisms — displacement and replacement of elements — capture empirical ranking dynamics. The model uncovers two regimes of behavior; fast and large rank changes, or slow diffusion. Our results indicate that the balance between robustness and adaptability in ranked systems might be governed by simple random processes irrespective of system details.

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Section: Introductionmentioning

confidence: 99%

Dynamics of ranking

et al. 2022

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“…We also note that the ranking may change between years, including not only the rank of a university but also the set of universities included in the ranking. The evolution of ranking over time may merit further investigations (Garcia-Zorita, Rousseau et al, 2018). We accept that the value 38% for the rank boost would definitely change when including a new ranking list or using the data from another year.…”

Section: Discussionmentioning

confidence: 99%

The rank boost by inconsistency in university rankings: Evidence from 14 rankings of Chinese universities

Chen

Zhang-Qian

Jia

2021

Quantitative Science Studies

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University ranking has become an important indicator for prospective students, job recruiters, and government administrators. The fact that a university rarely has the same position in different rankings motivates us to ask: To what extent could a university’s best rank deviate from its “true” position? Here we focus on 14 rankings of Chinese universities. We find that a university’s rank in different rankings is not consistent. However, the relative positions for a particular set of universities are more similar. The increased similarity is not distributed uniformly among all rankings. Instead, the 14 rankings demonstrate four clusters where rankings are more similar inside the cluster than outside. We find that a university’s best rank strongly correlates with its consensus rank, which is, on average, 38% higher (towards the top). Therefore, the best rank usually advertised by a university adequately reflects the collective opinion of experts. We can trust it, but with a discount. With the best rank and proportionality relationship, a university’s consensus rank can be estimated with reasonable accuracy. Our work not only reveals previously unknown patterns in university rankings, but also introduces a set of tools that can be readily applied to future studies.

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“…In the competing network, some network measures (average degree, average weighted degree, and clustering coefficient) are able to measure the competitive balance of a sport's ranking. Garcia-Zorita et al also investigated the dynamics of the competing network, including ranking with ties, entrants, and leavers [53].…”

Section: Network-based Ranking Of Clubsmentioning

confidence: 99%

A survey on football network analysis

Li,

Xu,

Zhou

2023

EPL

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Being the world's most popular sport, football research has traditionally concentrated on empirical summaries or statistics, with only limited data available in the past. In recent years, social network analysis has been applied to a variety of fields, which also brings new perspectives to the study of football spots. In this paper, we survey the literature related to football networks and discuss the use of network measures to analyze the performance of footballers and teams in different types of football networks. We aim to find out how to construct appropriate football networks based on different perspectives on football research. Various studies on football network analysis, including team performance, player interactions, and club behavior, are reviewed. The findings provide insights into team performance, player roles, and social dynamics within football teams and clubs.

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Ranking dynamics and volatility

Abstract: Scientific journals are ordered by their impact factor while countries, institutions or researchers can be ranked by their scientific production, impact or by other simple or composite indicators as in the case of university rankings. In this paper, the theoretical framework proposed in

Cited by 9 publications

References 11 publications

Dynamics of ranking

Dynamics of ranking

The rank boost by inconsistency in university rankings: Evidence from 14 rankings of Chinese universities

A survey on football network analysis

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