Public Funding and Elite Success in Czech Team Sports: A Penalized Logistic Regression Approach

Vol.19,No.2(2025)

Abstract

International sporting success is widely regarded as a symbol of national prestige and effectiveness in sport policy, motivating many countries to allocate significant public funding to elite and youth sport programs. However, the direct impact of such investments on competitive outcomes remains ambiguous, particularly in the context of team sports where success is infrequent and influenced by multiple factors. This study examines the relationship between public funding and international sporting success in the Czech Republic, focusing on four major team sports: football, basketball, ice hockey, and volleyball. Using a panel dataset aggregated across Olympic cycles (2010–2013, 2014–2017, and 2018–2022) and applying a penalized logistic regression model, we assess whether higher investments in national representation and youth development increase the probability of achieving top 8 placements at major international tournaments. This study aims to evaluate the extent to which public funding for national representation and youth development contributes to elite sporting success, measured by top 8 placements in international competitions. The analysis reveals that, despite considerable financial support, neither national representation nor youth development funding demonstrates a positive association with elite success. These findings raise critical questions regarding the design and strategic targeting of public investment policies in sport and contribute to the broader discourse on the efficiency of resource allocation in sport systems.


Keywords:
funding; performance; youth; teams; regression
References

Andersen, S. S., & Ronglan, L. T. (2012). Nordic elite sport: Same ambitions, different tracks. Copenhagen: Copenhagen Business School Press. ISBN 978-8763002455.

Bailey, R., Ford, P., MacNamara, A., & Pearce, G. (2010). Participant development in sport: An academic review (Vol. 4, pp. 1-134). Leeds: Sports Coach UK.

Bayle, E., & Robinson, L. (2007). A Framework for Understanding the Performance of National Governing Bodies of Sport. European Sport Management Quarterly, 7(3), 249–268. https://doi.org/10.1080/16184740701511037

De Bosscher, V., De Knop, P., van Bottenburg, M., & Shibli, S. (2008). The global sporting arms race: An international comparative study on sports policy factors leading to international sporting success. Oxford/Aachen: Meyer & Meyer Sport. ISBN 978-1-84126-228-4.

De Bosscher, V., Shibli, S., van Bottenburg, M., De Knop, P., & Truyens, J. (2015). Successful elite sport policies: An international comparison of the Sports Policy factors Leading to International Sporting Success (SPLISS 2.0) in 15 nations. Aachen: Meyer & Meyer Sport. ISBN 978-1782550761.

Green, M., & Houlihan, B. (2005). Elite sport development: Policy learning and political priorities. London/New York: Routledge. https://doi.org/10.4324/9780203022245.

Green, M., & Oakley, B. (2001). Elite sport development systems and playing to win: uniformity and diversity in international approaches. Leisure Studies, 20(4), 247–267. https://doi.org/10.1080/02614360110103598

Grix, J., & Carmichael, F. (2012). Why do governments invest in elite sport? A polemic. International Journal of Sport Policy and Politics, 4(1), 73–90. https://doi.org/10.1080/19406940.2011.627358

Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., … Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825), 357–362. https://doi.org/10.1038/s41586-020-2649-2

Heinze, G., & Schemper, M. (2002). A solution to the problem of separation in logistic regression. Statistics in medicine, 21(16), 2409-2419. https://doi.org/10.1002/sim.1047

le Cessie, S., & van Houwelingen, J. C. (1992). Ridge estimators in logistic regression. Journal of the Royal Statistical Society: Series C (Applied Statistics), 41(1), 191–201. https://doi.org/10.2307/2347628

King, G., & Zeng, L. (2001). Logistic regression in rare events data. Political Analysis, 9(2), 137–163. https://doi.org/10.1093/oxfordjournals.pan.a004868

McKinney, W. (2010). Data structures for statistical computing in Python. In S. van der Walt & J. Millman (Eds.), Proceedings of the 9th Python in Science Conference (pp. 51–56).

Oakley, B., & Green, M. (2001). Still playing the game at arm’s length? The selective re-investment in British sport, 1995–2000. Managing Leisure, 6(2), 74–94. https://doi.org/10.1080/13606710110039534

Seabold, S., & Perktold, J. (2010). Statsmodels: Econometric and statistical modeling with Python. In S. van der Walt & J. Millman (Eds.), Proceedings of the 9th Python in Science Conference (pp. 92–96).

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