Prediction of Success of Young Athletes using Integration of Different Databases
Abstract
There are various areas in soccer where prediction models can be utilized. Among them, identifying promising players can have a high cost-benefit ratio. Executive Functions (EF) are related to performance but have not yet been tested as predictors of success in soccer. This article investigates using EFs to select youth players using machine learning methods such as Logistic Regression, Naive Bayes, Decision Tree, and Random Forest to predict which players in the selected database were present and listed on the Transfermarkt platform. The best model was Random Forest combined with imputation, with a precision of 0.77. The present study indicates that EFs can be good predictors of success in soccer with up to 7 years of precedence.
Keywords:
Executive Functions, Soccer, Young Athletes, Market Value
References
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Beal, R., Norman, T. J., and Ramchurn, S. D. (2019). Artificial intelligence for team sports: a survey. Knowledge Engineering Review, 34.
Bezuglov, E., Morgans, R., Butovskiy, M., Emanov, A., Shagiakhmetova, L., Pirmakhanov, B., Waśkiewicz, Z., and Lazarev, A. (2023). The relative age effect is widespread among European adult professional soccer players but does not affect their market value. PLoS ONE, 18(3 March).
Chi, Y. K., Kim, T. H., Han, J. W., Lee, S. B., Park, J. H., Lee, J. J., Youn, J. C., Jhoo, J. H., Lee, D. Y., and Kim, K. W. (2012). Impaired design fluency is a marker of pathological cognitive aging; results from the Korean longitudinal study on health and aging. Psychiatry Investigation, 9(1):59 – 64.
da Silva Muniz, L. and da Silva, M. (2020). Análise das demonstrações contábeis dos clubes brasileiros de futebol: comparação entre a situação econômica e financeira e o aproveitamento nas partidas oficiais de 2015 a 2017. CAFI, 3(1):17–32.
Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64:135 – 168.
Mello, M., Belloni, V., Vasconcellos, F., Soares, J., Ogasawara, E., and Giusti, L. (2021). Funções Executivas e Idade Relativa como Preditores de Sucesso no Futebol. In Anais da Escola Regional de Informática do Rio de Janeiro (ERI-RJ), pages 111–118. SBC.
Scarpina, F. and Tagini, S. (2017). The stroop color and word test. Frontiers in Psychology, 8(APR).
Shibuya-Tayoshi, S., Sumitani, S., Kikuchi, K., Tanaka, T., Tayoshi, S., Ueno, S.-I., and Ohmori, T. (2007). Activation of the prefrontal cortex during the Trail-Making Test detected with multichannel near-infrared spectroscopy. Psychiatry and Clinical Neurosciences, 61(6):616 – 621.
Soliman, G., El-Nabawy, A., Misbah, A., and Eldawlatly, S. (2017). Predicting all star player in the national basketball association using random forest. In 2017 Intelligent Systems Conference, IntelliSys 2017, volume 2018-January, pages 706 – 713.
Van Bulck, D., Vande Weghe, A., and Goossens, D. (2023). Result-based talent identification in road cycling: discovering the next Eddy Merckx. Annals of Operations Research, 325(1):539 – 556.
Verburgh, L., Scherder, E., van Lange, P., and Oosterlaan, J. (2016). The key to success in elite athletes? Explicit and implicit motor learning in youth elite and non-elite soccer players. Journal of Sports Sciences, 34(18):1782 – 1790.
Werneck, R. and Figueiredo, A. (2024). Goldfit Soccer: A Multidimensional Model for Talent Identification of Young Soccer Players. Research Quarterly for Exercise and Sport, 0(0):1–15.
Published
2024-10-14
How to Cite
CALMON, Lucas; FERRO, Rodrigo; PEREIRA, Carlos; SOUZA, Caio; GIUSTI, Lucas; AMORIM, Glauco; OGASAWARA, Eduardo.
Prediction of Success of Young Athletes using Integration of Different Databases. In: BRAZILIAN SYMPOSIUM ON DATABASES (SBBD), 39. , 2024, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 855-861.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2024.243187.
