Factores psicofisiológicos que determinam o sucesso de lutadores de elite de luta greco-romana utilizando métodos de inteligência artificial
DOI:
https://doi.org/10.47197/retos.v83.119484Palavras-chave:
Atletas de elite, luta greco-romana, indicadores psicofisiológicos, análise multivariada, inteligência artificialResumo
Introdução: A aplicação de métodos de inteligência artificial à análise de indicadores psicofisiológicos permite a identificação dos marcadores mais informativos e a construção de perfis de treino generalizados para lutadores de alto nível, no âmbito da monitorização desportiva.
Objectivo: Determinar os determinantes psicofisiológicos que caracterizam o perfil de treino dos lutadores de elite de luta greco-romana utilizando métodos de análise multivariada e ferramentas de inteligência artificial.
Metodologia: O estudo incluiu 44 lutadores de luta greco-romana: jovens lutadores de elite (n = 21) e lutadores de elite (n = 23). Foi utilizado um conjunto de testes psicofisiológicos para as medições. Foi aplicado um algoritmo de pesquisa para indicadores psicofisiológicos utilizando ferramentas de inteligência artificial, que incluíram as etapas de recolha de dados, processamento estatístico dos resultados no ambiente RStudio e interpretação dos resultados utilizando o modelo de linguagem em larga escala (LLM) GPT-5.2 (OpenAI).
Resultados: Foram identificados determinantes psicofisiológicos que caracterizam o perfil de treino dos lutadores. Não foram encontradas diferenças estatisticamente significativas entre os grupos Juvenil Sub-17 e Sénior em 10 indicadores-chave. Foram determinadas componentes latentes das funções psicofisiológicas dos lutadores e índices abrangentes (ILPs), permitindo a construção de perfis individuais de atletas e a monitorização das funções psicofisiológicas durante o processo de treino.
Conclusões: Os determinantes psicofisiológicos identificados neste estudo podem ser utilizados como base para a individualização da monitorização, a deteção de características específicas das funções psicofisiológicas e a melhoria do sistema de monitorização para lutadores.
Foi confirmada a utilidade de uma abordagem híbrida, em que a inteligência artificial reforça o processo analítico sem comprometer a sua reprodutibilidade científica.
Referências
Ahmadi, H., Yousefizad, M., & Manavizadeh, N. (2024). Smartifying martial arts: lightweight triboelectric nanogenerator as a self-powered sensor for accurate judging and AI-driven performance analysis. IEEE Sensors Journal, 19(24), 30176-30183. https://doi.org/10.1109/JSEN.2024.3443229
Al Ardha, M. A., Putra, K. P., Sahertian, J., Wijaya, A., Bikalawan, S. S., Simanjuntak, F. Y., Ramdhanu, I. K., & Yang, C. B. (2025). Video-based AI system for automatic range of motion assessment in Physical Education and sport. Retos, 74, 221-235. https://doi.org/10.47197/retos.v74.117535
Badau, D., Baydil, B., & Badau, A. (2018). Differences among three measures of reaction time based on hand laterality in individual sports. Sports, 6(2), 45. https://doi.org/10.3390/sports6020045
Barrett, B. T., Cruickshank, A. G., Flavell, J. C., Bennett, S. J., & Buckley, J. G. (2020). Faster visual reaction times in elite athletes are not linked to better gaze stability. Scientific Reports, 10(1), 13216. https://doi.org/10.1038/s41598-020-69975-z
Baca, A., Dabnichki, P., Hu, C. W., Kornfeind, P., & Exel, J. (2022). Ubiquitous Computing in Sports and Physical Activity-Recent Trends and Developments. Sensors, 22(21), 8370. https://doi.org/10.3390/s22218370
Bunyatova, A., Ahmadova, L., & Suleymanova, A. (2026). Personality and cognitive resilience in elite athletes: a HEXACO framework. Retos, 79, 214-227. https://doi.org/10.47197/retos.v79.117918
Cano, L. A., Gerez, G. D., García, M. S., Albarracín, A. L., Farfán, F. D., & Fernández-Jover, E. (2024). Decision-Making Time Analysis for Assessing Processing Speed in Athletes during Motor Reaction Tasks. Sports, 12(6), 151. https://doi.org/10.3390/sports12060151
Cauraugh, J. H. (1990). Speed-accuracy tradeoff during response preparation. Research Quarterly for Exercise and Sport, 61(4), 331-337. https://doi.org/10.1080/02701367.1990.10607496
Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research, and Evaluation, 10(1), 7. https://doi.org/10.7275/jyj1-4868
Curby, D., Dokmanac, M., Kerimov, F., Tropin, Y., Latyshev, M., Bezkorovainyi, D., & Korobeynikov, G. (2023). Performance of wrestlers at the Olympic Games: gender aspect. Pedagogy of Physical Culture and Sports, 27(6), 487-493. https://doi.org/10.15561/26649837.2023.0607
Dehbane, O., Dziak, A., Gouttebarge, V., Lolli, L., Bury, J., Dahdal, P., Helsen, W., Englert, C., Polglaze, T., Varley, M. C., Williams, A. M., & McCall, A. (2026). Systematic review of different approaches for performance enhancement in elite sport. Frontiers in Artificial Intelligence, 9, 1781958. https://doi.org/10.3389/frai.2026.1781958
Domingue, B. W., Kanopka, K., Stenhaug, B., Sulik, M. J., Beverly, T., Brinkhuis, M., Circi, R., Faul, J., Liao, D., McCandliss, B., Obradović, J., Piech, C., Porter, T., Consortium, P. iLEAD, Soland, J., Weeks, J., Wise, S. L., & Yeatman, J. (2022). Speed-Accuracy Trade-Off? Not So Fast: Marginal Changes in Speed Have Inconsistent Relationships With Accuracy in Real-World Settings. Journal of Educational and Behavioral Statistics, 47(5), 576-602. https://doi.org/10.3102/10769986221099906
Ervilha, U. F., Fernandes, F. D. M., Souza, C. C., & Hamill, J. (2020). Reaction time and muscle activation patterns in elite and novice athletes performing a taekwondo kick. Sports Biomechanics, 19(5), 665-677. https://doi.org/10.1080/14763141.2018.1515244
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272-299. https://doi.org/10.1037/1082-989X.4.3.272
Fernández, D., Moya, D., Cadefau, J. A., & Carmona, G. (2021). Integrating external and internal load for monitoring fitness and fatigue status in standard microcycles in elite rink hockey. Frontiers in Physiology, 12, 698463. https://doi.org/10.3389/fphys.2021.698463
Franchini, E., Lopes-Silva, J. P., dos Santos, D. C. F., Agostinho, M. F., Kons, R. L., & Takito, M. Y. (2023). Prioritizing, making final adjustments or competing for the ultimate glory: The case of World Championships and Olympic Games judo tournaments within 48 days. Science & Sports, 38(3), 318-321. https://doi.org/10.1016/j.scispo.2022.06.005
Gierczuk, D., Lyakh, V., Sadowski, J., & Bujak, Z. (2017). Speed of reaction and fighting effectiveness in elite Greco-Roman wrestlers. Perceptual and Motor Skills, 124(1), 200-213. https://doi.org/10.1177/0031512516672126
Hayton, J. C., Allen, D. G., & Scarpello, V. (2004). Factor retention decisions in exploratory factor analysis: A tutorial on parallel analysis. Organizational Research Methods, 7(2), 191-205. https://doi.org/10.1177/1094428104263675
Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179-185. https://doi.org/10.1007/BF02289447
Hou, Q. (2026). Artificial Intelligence (AI)-Predicted Joint Stress Indices and Overuse Injuries in Martial Arts Training Among Martial Arts Athletes. Pacific International Journal, 9(2), 40–51. https://doi.org/10.55014/pij.v9i2.976
Jianjun, Q., Isleem, H. F., Almoghayer, W. J., & Khishe, M. (2025). Predictive athlete performance modeling with machine learning and biometric data integration. Scientific Reports, 15(1), 16365. https://doi.org/10.1038/s41598-025-01438-9
Junior, M. N., Lopes-Silva, J. P., Takito, M. Y., & Franchini, E. (2024). Cadet and Junior Performance Is Associated With Senior’s World Championship and Olympics Achievement in Judo. Research quarterly for exercise and sport, 95(1), 54-59. https://doi.org/10.1080/02701367.2022.2147477
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36. https://doi.org/10.1007/BF02291575
Khan, M. M., Cruz, C., O’Connor, S. M., Brennan, B., & Pomeroy, V. M. (2025). Explainable artificial intelligence for gait analysis: Advances, pitfalls, and challenges - A systematic review. Frontiers in Bioengineering and Biotechnology, 13, 1671344. https://doi.org/10.3389/fbioe.2025.1671344
Korobeynikov, G., Korobeinikova, L., Raab, M., Korobeinikova, I., Danko, T., Kokhanevich, A., Cynarski, W. J., & Mytskan, T. (2022). Psychophysiological state and decision making in wrestlers. Ido Movement for Culture. Journal of Martial Arts Anthropology, 22(5), 1-9. https://doi.org/10.14589/ido.22.5.2
Korobeynikov, G., Korobeinikova, L., Raab, M., Baić, M., Borysova, O., Korobeinikova, I., Shengpeng, G., & Khmelnitska, I. (2023). Cognitive functions and special working capacity in elite boxers. Pedagogy of Physical Culture and Sports, 27(1), 84-90. https://doi.org/10.15561/26649837.2023.0110
Kurak, K., İlbak, İ., Stojanović, S., Bayer, R., İlbak, Y. E., Kasicki, K., Ambroży, T., Rydzik, Ł., & Czarny, W. (2025). The effect of time of day on visual reaction time performance in boxers: Evaluation in terms of chronotype. Frontiers in Physiology, 16, 1589740. https://doi.org/10.3389/fphys.2025.1589740
Lai, X., Chen, J., Lai, Y., Huang, S., Cai, Y., Sun, Z., Wang, X., Pan, K., Gao, Q.,& Huang, C. (2025). Using Large Language Models to Enhance Exercise Recommendations and Physical Activity in Clinical and Healthy Populations: Scoping Review. JMIR Med Inform, 13(1), e59309. https://doi.org/10.2196/59309
Latyshev, M., Tropin, Y., Pryimakov, O., Curby, D., Dokmanac, M., Baic, M., Korobeynikov, G., Kerimov, F., Khamidjonov, A., & Mirzolim, M. (2024). Greco-Roman Wrestling on the World Stage: Performance Trends and Country Comparisons. Journal of Martial Arts Anthropology, 24(4), 33-39. https://doi.org/10.14589/ido.24.4.5
Lerche, V., & Voss, A. (2018). Speed-accuracy manipulations and diffusion modeling: Lack of discriminant validity of the manipulation or of the parameter estimates? Behavior Research Methods, 50(6), 2568-2585. https://doi.org/10.3758/s13428-018-1034-7
Littman, R., & Takács, Á. (2017). Do all inhibitions act alike? A study of go/no-go and stop-signal paradigms. PLOS ONE, 12(10), e0186774. https://doi.org/10.1371/journal.pone.0186774
Lloret-Segura, S., Ferreres-Traver, A., Hernández-Baeza, A., & Tomás-Marco, I. (2014). Exploratory item factor analysis: A practical guide revised and updated. Anales de Psicología, 30(3), 1151-1169. https://doi.org/10.6018/analesps.30.3.199361
MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample size in factor analysis. Psychological Methods, 4(1), 84-99. https://doi.org/10.1037/1082-989X.4.1.84
MacCallum, R. C., Widaman, K. F., Preacher, K. J., & Hong, S. (2001). Sample size in factor analysis: The role of model error. Multivariate Behavioral Research, 36(4), 611-637. https://doi.org/10.1207/S15327906MBR3604_06
Manescu, D. C. (2025). Artificial Intelligence in elite sports training and prospects for integration into school sports. Retos, 73, 128-141. https://doi.org/10.47197/retos.v73.117261
Mănescu, D. C., & Mănescu, A. M. (2025). Artificial Intelligence in the Selection of Top-Performing Athletes for Team Sports: A Proof-of-Concept Predictive Modeling Study. Applied Sciences, 15(18), 9918. https://doi.org/10.3390/app15189918
Oleksy, Ł., Królikowska, A., Mika, A., Kuchciak, M., Szymczyk, D., Rzepko, M., Bril, G., Prill, R., Stolarczyk, A., & Reichert, P. (2022). A compound hop index for assessing soccer players’ performance. Journal of Clinical Medicine, 11(1), 255. https://doi.org/10.3390/jcm11010255
Oliva-Lozano, J. M., Cefis, M., Fortes, V., López-Del Campo, R., & Resta, R. (2024). Summarizing physical performance in professional soccer: Development of a new composite index. Scientific Reports, 14(1), 14453. https://doi.org/10.1038/s41598-024-65581-5
Pang, Y., Wang, Y., Wang, Q., Li, F., Zhang, C., & Ding, C. (2025). Applications of AI in martial arts: A survey. Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology, 239(2), 301-331. https://doi.org/10.1177/17543371241273827
Pietraszewski, P., Terbalyan, A., Roczniok, R., Maszczyk, A., Ornowski, K., Manilewska, D., Kuliś, S., Zając, A., & Gołaś, A. (2025). The Role of Artificial Intelligence in Sports Analytics: A Systematic Review and Meta-Analysis of Performance Trends. Applied Sciences, 15(13), 7254. https://doi.org/10.3390/app15137254
Podrihalo, O., Podrigalo, L., Kiprych, S., Galashko, M., Alekseev, A., Tropin, Y., Deineko, A., Marchenkov, M., & Nasonkina, O. (2021). The comparative analysis of morphological and functional indicators of armwrestling and street workout athletes. Pedagogy of Physical Culture and Sports, 25(3), 188-193. https://doi.org/10.15561/26649837.2021.0307
Podrigalo, L., Iermakov, S., Romanenko, V., Baibikov, M., Galimskyi, V., Shutieiev, V., & Merdov, S. (2025). Prediction of success in taekwondo based on psychophysiological testing results. Pedagogy of Physical Culture and Sports, 29(4), 350-6. https://doi.org/10.15561/26649837.2025.0412
Romanenko, V., Cynarski, W. J., Tropin, Y., Kovalenko, Y., Korobeynikov, G., Piatysotska, S., Mikhalskyi, V., Holokha, V., & Gaziyev, S. (2025a). Methodology for assessing spatial perception in martial arts. Applied Sciences, 15(6), 3413. https://doi.org/10.3390/app15063413
Romanenko, V., Piatysotska, S., Podrigalo, L., Baibikov, M., Boychenko, N., & Volodchenko, O. (2024а). Methodology for evaluating the “Go/No-Go” reaction in martial arts. Journal of Physical Education and Sport, 24(12), 2139-2146. https://doi.org/10.7752/jpes.2024.12312
Romanenko, V., Piatysotska, S., Tropin, Y., Rydzik, Ł., Holokha, V., & Boychenko, N. (2022). Study of the reaction of the choice of combat athletes using computer technology. Slobozhanskyi Herald of Science and Sport, 26(4), 97-103. https://doi.org/10.15391/snsv.2022-4.001
Romanenko, V., Piatysotska, S., Litvinenko, A., Baibikov, M., Boychenko, N., & Ponomarov, V. (2024b). Methodology for assessing the reaction of combat athletes to a moving object. Slobozhanskyi Herald of Science and Sport, 28(2), 69-77. https://doi.org/10.15391/snsv.2024-2.003
Romanenko, V., Podrigalo, L., Cynarski, W. J., Rovnaya, O., Korobeynikova, L., Goloha, V., & Robak, I. (2020). A comparative analysis of the short-term memory of martial arts athletes of different levels of sportsmanship. Ido Movement for Culture. Journal of Martial Arts Anthropology, 20(3), 18-24. https://doi.org/10.14589/ido.20.3.3
Romanenko, V., Tropin, Y., Podrigalo, L., Boychenko, N., Abdula, A., Sereda, N., & Yatsiv, Y. (2025b). Specific features of cognitive skill development in athletes of situational sports. Pedagogy of Physical Culture and Sports, 29(3), 194-203. https://doi.org/10.15561/26649837.2025.0305
Sanabria Navarro, J. R., Niebles Núñez, W. A., & Silveira Pérez, Y. (2024). Análisis bibliométrico de la inteligencia artificial en el deporte (Bibliometric analysis of artificial intelligence in sport). Retos, 54, 312-319. https://doi.org/10.47197/retos.v54.103531
Scharfen, H.-E., & Memmert, D. (2019). Measurement of cognitive functions in experts and elite athletes: A meta-analytic review. Applied Cognitive Psychology, 33(5), 843-860. https://doi.org/10.1002/acp.3526
Schulz, K. P., Fan, J., Tang, C. Y., Newcorn, J. H., Buchsbaum, M. S., & Cheung, A. M. (2007). Does the emotional Go/No-Go task really measure behavioral inhibition? Convergence with measures on a non-emotional analog. Archives of Clinical Neuropsychology, 22(2), 151-160. https://doi.org/10.1016/j.acn.2006.12.001
Seli, P., Cheyne, J. A., & Smilek, D. (2013). A methodological note on evaluating performance in a sustained-attention-to-response task. Behavior Research Methods, 45(2), 355-363. https://doi.org/10.3758/s13428-012-0266-1
Serrien, D. J., Ivry, R. B., & Swinnen, S. P. (2006). Dynamics of hemispheric specialization and integration in the context of motor control. Nature Reviews Neuroscience, 7(2), 160-166. https://doi.org/10.1038/nrn1849
Siebert, L., Reichert, L., Musculus, L., Will, L., Al-Ghezi, A., Raab, M., & Zentgraf, K. (2025). How fast-and-frugal trees can inform diagnostic and intervention decisions for enhancing elite athlete performance. PloS one, 20(8), e0329395. https://doi.org/10.1371/journal.pone.0329395
Stöckel, T., & Weigelt, M. (2012). Plasticity of human handedness: Decreased one-hand bias and inter-manual performance asymmetry in expert basketball players. Journal of Sports Sciences, 30(10), 1037-1045. https://doi.org/10.1080/02640414.2012.685087
Tobias, S., & Carlson, J. E. (1969). Bartlett’s test of sphericity and chance findings in factor analysis. Multivariate Behavioral Research, 4(3), 375-377. https://doi.org/10.1207/s15327906mbr0403_8
Tropin, Y., Podrigalo, L., Boychenko, N., Podrihalo, O., Volodchenko, O., Volskyi, D., & Roztorhui, M. (2023). Analyzing predictive approaches in martial arts research. Pedagogy of Physical Culture and Sports, 27(4), 321–330. https://doi.org/10.15561/26649837.2023.0408
Valero, C. S. (2018). Aplicación de métodos de aprendizaje automático en el análisis y la predicción de resultados deportivos (Application of automated learning methods for analyzing and predicting sports outcomes). Retos, 34, 377-382. https://doi.org/10.47197/retos.v0i34.58506
Voigt, L., & Raab, M. (2024). Mind in action: the interplay of cognition and action in skilled performance. International Journal of Sport and Exercise Psychology, 22(2), 307-313. https://doi.org/10.1080/1612197X.2023.2289328
Volgemute, K., Ulme, G., Abele, A., Vazne, Z., Ansons, K., Licis, R., Lavins, R., & Klonova, A. (2026). Multi-factorial profiling of athletes integrating personality, psychological skills, and psychophysiological performance indicators. Scientific Reports, 16, 4949. https://doi.org/10.1038/s41598-026-35809-7
Vona, M., de Guise, É., Leclerc, S., Deslauriers, J., & Romeas, T. (2024). Multiple domain-general assessments of cognitive functions in elite athletes: Contrasting evidence for the influence of expertise, sport type and sex. Psychology of Sport and Exercise, 75, 102715. https://doi.org/10.1016/j.psychsport.2024.102715
Witkowski, M., Tomczak, M., Karpowicz, K., Solnik, S., & Przybyla, A. (2020). Effects of fencing training on motor performance and asymmetry vary with handedness. Journal of Motor Behavior, 52(5), 554-565. https://doi.org/10.1080/00222895.2019.1579167
Yang, H., Huang, B., & Li, X. (2025). Augmentation or substitution: defining role of large language model in physical education. Frontiers in Sports and Active Living, 7, 1662056. https://doi.org/10.3389/fspor.2025.1662056
Zhang, Y., Qu, R., & Girard, O. (2025). Faster, more accurate? A feasibility study on replacing human judges with artificial intelligence in video review for the Paris Olympics Taekwondo competition. Frontiers in Sports and Active Living, 7, 1632326. https://doi.org/10.3389/fspor.2025.1632326
Zhang, Z., Piras, A., Chen, C., Kong, B., & Wang, D. (2022). A comparison of perceptual anticipation in combat sports between experts and non-experts: A systematic review and meta-analysis. Frontiers in Psychology, 13, 961960. https://doi.org/10.3389/fpsyg.2022.961960
Zhu, P., & Sun, F. (2019). Sports athletes’ performance prediction model based on machine learning algorithm. In International Conference on Applications and Techniques in Cyber Security and Intelligence, 498-505. https://doi.org/10.1007/978-3-030-25128-4_62
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Direitos de Autor (c) 2026 Georgiy Korobeynikov, Vyacheslav Romanenko, Yrui Tropin, Leonid Podrigalo, Volodymyr Shatskykh, Markus Raab, Oleksandr Pryimakov, Lesia Korobeinikova, Denis Volsky
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