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Who is the greatest team in Liga MX? A dynamic analysis/¿Cuál es el equipo más grande de la Liga MX? Un análisis dinámico

Author

Listed:
  • Francisco Corona

    (Instituto Nacional de Estadística y Geografía)

  • Nelson Muriel

    (Universidad Iberoamericana)

  • Jesús López-Pérez

    (Instituto Nacional de Estadística y Geografía)

Abstract
In this paper, we conduct a statistical procedure to respond a very frequent question in Mexican sport media TV: Who is the greatest team in Liga MX? For this purpose, we apply Principal Components to a historical domestic and international results database along with variables related to the fans and the market value of the franchises’ roster from 2011-2019. The results allow us to analyze the evolution of the “greatness†latent variable over time, concluding that, in the window of time analyzed, Club América is the greatest team, followed by C.D. Guadalajara and C.F. Cruz Azul. Additionally, nowadays, Club Tigres de la Universidad Autónoma de Nuevo León and C.F. Monterrey displace teams like Deportivo Toluca F.C. and Club Universidad Nacional.

Suggested Citation

  • Francisco Corona & Nelson Muriel & Jesús López-Pérez, 2023. "Who is the greatest team in Liga MX? A dynamic analysis/¿Cuál es el equipo más grande de la Liga MX? Un análisis dinámico," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 38(2), pages 225–260-2.
  • Handle: RePEc:emx:esteco:v:38:y:2023:i:2:p:225-260
    as

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    File URL: https://estudioseconomicos.colmex.mx/index.php/economicos/article/view/442
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    References listed on IDEAS

    as
    1. Bai, Jushan & Ng, Serena, 2013. "Principal components estimation and identification of static factors," Journal of Econometrics, Elsevier, vol. 176(1), pages 18-29.
    2. Jushan Bai & Serena Ng, 2004. "A PANIC Attack on Unit Roots and Cointegration," Econometrica, Econometric Society, vol. 72(4), pages 1127-1177, July.
    3. M. J. Maher, 1982. "Modelling association football scores," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 36(3), pages 109-118, September.
    4. Alexei Onatski, 2010. "Determining the Number of Factors from Empirical Distribution of Eigenvalues," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1004-1016, November.
    5. Seung C. Ahn & Alex R. Horenstein, 2013. "Eigenvalue Ratio Test for the Number of Factors," Econometrica, Econometric Society, vol. 81(3), pages 1203-1227, May.
    6. Corona Francisco & Horrillo Juan de Dios Tena & Wiper Michael Peter, 2017. "On the importance of the probabilistic model in identifying the most decisive games in a tournament," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 13(1), pages 11-23, March.
    7. Stefani Ray & Pollard Richard, 2007. "Football Rating Systems for Top-Level Competition: A Critical Survey," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(3), pages 1-22, July.
    8. Rose D. Baker & Ian G. McHale, 2015. "Time varying ratings in association football: the all-time greatest team is.," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(2), pages 481-492, February.
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    More about this item

    Keywords

    financial variables; football; greatness; popularity; principal components analysis;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • Z20 - Other Special Topics - - Sports Economics - - - General

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