Studia Kinanthropologica 2023, 24(1):19-29 | DOI: 10.32725/sk.2023.009

Statistical comparison of Czech and Danish football leagues: market value, age, and nationality of players

M. Tomíček, N. Pelloneová
Technická univerzita v Liberci, Ekonomická fakulta, Katedra podnikové ekonomiky a managementu

Football is one of the most popular sports disciplines and the use of statistical methods to analyze sports data is of great interest not only to coaches but also to researchers. Football clubs mainly use the services of specialized companies for data analysis. With the use of statistical analysis, it is possible to find out interesting information and relationships that are not possible by simply observing the game or the players. There is a great interest in sports data analysis among football clubs. The aim of this paper is to use statistical analysis to compare two professional European football competitions and to identify their main similarities and differences in three highlighted areas - age of players, national composition of competitions and market value of players. Statistical data analysis methods were used to test the research questions formulated in this research. The database used was created based on variables obtained from InStat and Transfermarkt. The statistical program IBM SPSS Stastistics 28 was used for the calculations.

Keywords: football; sports statistical analysis; market value; Fortuna:Liga; 3F Superliga

Received: February 23, 2023; Revised: April 14, 2023; Accepted: April 24, 2023; Published: September 27, 2023  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Tomíček M, Pelloneová N. Statistical comparison of Czech and Danish football leagues: market value, age, and nationality of players. Studia Kinanthropologica. 2023;24(1):19-29. doi: 10.32725/sk.2023.009.
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. 3F Superliga. (2021). Superliga.dk: Performance Center. https://www.superliga.dk/performance-centersuperliga-2020-2021
    2. Anděl, J. (2007). Statistické metody. Matfyzpress.
    3. Brase, Ch. H., Brase, C. P., & Iraki, A. (2019). Understanding basics statistics. Cengage. Calleja-González, J., Los Arcos, A., Mejuto, G., Casamichana, D., San Roman Quintana, J., & Yanci, J. (2015). Reproducibilidad de test de aceleración y cambio de dirección en fútbol. Revista Internacional de Ciencias del Deporte, 11 (40), 104-115. http://dx.doi.org/10.5232/ricyde2015.04001 Go to original source...
    4. Felipe, J. L., Fernandez-Luna, A., Burillo, P., de la Riva, L. E., Sanchez-Sanchez, J., & Garcia-Unanue, J. (2020). Money talks: Team variables and player positions that most influence the market value of professional male footballers in Europe. Sustainability, 12 (9), 3709. https://doi.org/10.3390/su12093709 Go to original source...
    5. Fortuna:Liga. (2021). FortunaLiga.cz: Statistiky. https://www.fortunaliga.cz/statistiky?unit=1¶meter=1
    6. Gardasevic, J., & Bjelica, D. (2020). Body Composition Differences between Football Players of the Three Top Football Clubs. International Journal of Morphology, 38 (1), 153-158. https://doi.org/10.4067/S0717-95022020000100153 Go to original source...
    7. Harper, J. (2021). Data experts are becoming football's best signings. BBC News. https://www.bbc.com/news/business-56164159
    8. Hendl, J. (2012). Přehled statistických metod: analýza a metaanalýza dat. Portál.
    9. Hindls, R. (2007). Statistika pro ekonomy. Professional Publishing. IBM. (2023). IBM SPSS software. https://www.ibm.com/spss
    10. InStat. (2020). Objective performance rating for players & teams. https://instatsport.com/football
    11. Kim, Y., Nam Bui, K., & Jung, J. J. (2019). Data-driven exploratory approach on player valuation in foot-ball transfer market. Concurrency and Computation Practice and Experience, 33 (3). https://doi.org/10.1002/cpe.5353 Go to original source...
    12. Kirschstein, T., & Liebscher, S. (2018). Assessing the market values of soccer players - a robust analysis of data from German 1. and 2. Bundesliga. Journal of Applied Statistics, 46 (7), 1336-1349. https://doi.org/10.1080/02664763.2018.1540689 Go to original source...
    13. Konter, E., & Yurdabakan, I. (2010). Nonverbal intelligence of soccer players according to their age, gender and educational level. Social and Behavioral Sciences, 2 (2), 915-921. https://doi.org/10.1016/j.sbspro.2010.03.126 Go to original source...
    14. Konter, E. (2010). Nonverbal intelligence of soccer players according to their level of play. Social and Behavioral Sciences, 2 (2), 1114-1120. https://doi.org/10.1016/j.sbspro.2010.03.126 Go to original source...
    15. Lago-Ballesteros, J., & Lago-Peñas, C. (2010). Performance in Team Sports: Identifying the Keys to Success in Soccer. Journal of Human Kinetics, 25 (2010), 85-91. https://doi.org/10.2478/v10078-010-0035-0T Go to original source...
    16. Littlewood, M., Mullen, C., & Richardson, D. (2011). Football labour migration: An examination of the player recruitment strategies of the 'Big Five' European Football Leagues 2004-5 to 2008-9. Soccer & Society, 12 (6), 788-805. https://doi.org/10.1080/14660970.2011.609680 Go to original source...
    17. Maderer, D., Holtbrügge, D., & Schuster, T. (2014). Professional Football squads as multicultural teams. International Journal of Cross Cultural Management, 14 (2), 215-238. https://doi.org/10.1177/1470595813510710 Go to original source...
    18. Metelski, A. (2021). Journal of Physical Education and Sport, 21 (2), 1150-1155. https://doi.org/10.7752/jpes.2021.s2145 Go to original source...
    19. Moura, F. A., Barreto Martins, L. E., & Cunha, S. A. (2014). Analysis of football game-related statistics using multivariate techniques. Journal of Sports Sciences, 32 (20), 1881-1887. https://doi.org/10.1080/02640414.2013.853130 Go to original source... Go to PubMed...
    20. Oberstone, J. (2009). Differentiating the Top English Premier League Football Clubs from the Rest of the Pack: Identifying the Keys to Success. Journal of Quantitative Analysis in Sports, 5 (3). https://doi.org/10.2202/1559-0410.1183 Go to original source...
    21. Parker, S. D. G. (2016). Correlation Between the National Football League Draft and Player Performance. Robert D. Clark Honors College.
    22. Pelloneová, N., & Tomíček, M. (2022). Ranking players by DEA: An analysis of Czech and Danish football. Studia Sportiva, 16 (1), 76-90. https://doi.org/10.5817/sts2022-1-8 Go to original source...
    23. Penn, R. (2018). From comparative analysis to final publication: Nationalism and Globalization in English and Italian football since 1930. European Journal for Sport and Society, 10 (4), 345-364. https://doi.org/10.4135/9781526445308 Go to original source...
    24. Poli, R., Ravenel, L., & Besson, R. (2016). CIES Football Observatory Monthly Report - Foreign players in football teams. https://football-observatory.com/IMG/pdf/mr12_eng.pdf
    25. Redkva, P. E., Paes, M. R., Fernandez, R., & da-Silva, S. G. (2018). Correlation between Match Performance and Field Tests in Professional Soccer Players. Journal of Human Kinetics, 62 (1), 213-219. https://doi.org/10.1515/hukin-2017-0171 Go to original source... Go to PubMed...
    26. Sandgren E., Karlsson, M., & Ji-Guo Y. (2013). Correlation Analysis Between SoccerGame World Ranking and Player League Distribution. Sport and Art, 1 (2), 34-40. https://doi.org/10.13189/saj.2013.010202 Go to original source...
    27. Swanepoel, M. J., & Swanepoel, J. (2016). The correlation between player valuation and the bargaining position of clubs in the English premier league (EPL). International Journal of Economics and Finance Studies, 8 (1), 209-225.
    28. Tomíček, M., & Pelloneová, N. (2022). Sports data analysis: Comparison of the Czech and Danish top football competitions. Tělesná Kultura, 45. https://doi.org/10.5507/tk.2022.002 Go to original source...
    29. Tomíček, M. (2021). Aplikace statistických metod ve sportovních klubech [Diplomová práce, Technická univerzita v Liberci].
    30. Transfermarkt. (2021). Transfermarkt.com: Intern. https://www.transfermarkt.com/intern/stellenangebote
    31. UEFA. (2021). UEFA Country Coefficients. 2021, UEFA Rankings. https://www.uefa.com/memberassociations/uefarankings/country/#/yr/2021
    32. Yi, Q., Gómez, M. A., Liu, H., & Sampaio, J. (2019). Variation of match statistics and football teams' match performance in the group stage of the UEFA Champions League from 2010 to 2017. Kinesiology, 51 (2), 170-181. https://doi.org/10.26582/k.51.2.4 Go to original source...
    33. Yue, Z., Broich, H. & Mester, J. (2014). Statistical Analysis for the Soccer Matches of the First Bundesliga. International Journal of Sports Science & Coaching, 9 (3), 553-560. https://doi.org/10.1260/1747-9541.9.3.553 Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content