Plush MG, Guppy SN, Nosaka K, Barley OR. Exploring the Physical and Physiological Characteristics Relevant to Mixed Martial Arts. Strength & Conditioning Journal, 2022;44(2): 52–60. https://doi.org/10.1519/SSC.0000000000000649
Podrigalo LV, Shi K, Podrihalo OO, Volodchenko OA, Halashko OI. Main research areas in kickboxing investigations: an analysis of the scientific articles of the Web of Science Core Collection. Pedagogy of Physical Culture and Sports, 2022;26(4): 244–259. https://doi.org/10.15561/26649837.2022.0404
Bourdon PC, Cardinale M, Murray A, Gastin P, Kellmann M, Varley MC, et al. Monitoring Athlete Training Loads: Consensus Statement. International Journal of Sports Physiology and Performance, 2017;12(s2): S2-161-S2-170. https://doi.org/10.1123/IJSPP.2017-0208
Slimani M, Chaabene H, Miarka B, Franchini E, Chamari K, Cheour F. Kickboxing review: anthropometric, psychophysiological and activity profiles and injury epidemiology. Biology of Sport, 2017;2: 185–196. https://doi.org/10.5114/biolsport.2017.65338
Ishihara T, Kuroda Y, Mizuno M. Competitive achievement may be predicted by executive functions in junior tennis players: An 18-month follow-up study. Journal of Sports Sciences, 2019;37(7): 755–761. https://doi.org/10.1080/02640414.2018.1524738
Huang C, Shen W. Characters and development tendency analysis on sports prediction scientific research papers in China. In: Proceedings 2011 International Conference on Human Health and Biomedical Engineering, Jilin, China: IEEE; 2011. p. 814–819. https://doi.org/10.1109/HHBE.2011.6028952
Roberts AH, Greenwood DA, Stanley M, Humberstone C, Iredale F, Raynor A. Coach knowledge in talent identification: A systematic review and meta-synthesis. Journal of Science and Medicine in Sport, 2019;22(10): 1163–1172. https://doi.org/10.1016/j.jsams.2019.05.008
Zhdanova OG, Romanchenko BV, Sperkach MO. Predicting of sports events results. Herald of Advanced Information Technology, 2019;2(4): 278–287. https://doi.org/10.15276/hait.04.2019.4
Pyroh Y. Features of competitive activity in different types of martial arts. Edinoborstva, 2023;(1(27)): 49–66. https://doi.org/10.15391/ed.2023-1.05
Johnson JD. Predicting outcomes of mixed martial arts fights with novel fight variables [Master’s Thesis]. Athens (GA): University of Georgia; 2012.
Spann M, Skiera B. Sports forecasting: a comparison of the forecast accuracy of prediction markets, betting odds and tipsters. Journal of Forecasting, 2009;28(1): 55–72. https://doi.org/10.1002/for.1091
Ihsan N, Hanafi R, Sepriadi S, Okilanda A, Suwirman S, Mario DT. The Effect of Limb Muscle Explosive Power, Flexibility, and Achievement Motivation on Sickle Kick Performance in Pencak Silat Learning. Physical Education Theory and Methodology, 2022;22(3): 393–400. https://doi.org/10.17309/tmfv.2022.3.14
Kalina RM, Jagiełło W. Non-apparatus, Quasi-apparatus and Simulations Tests in Diagnosis Positive Health and Survival Abilities. In: Ahram T (ed.) Advances in Human Factors in Sports, Injury Prevention and Outdoor Recreation, Cham: Springer International Publishing; 2018. p. 121–128. https://doi.org/10.1007/978-3-319-60822-8_12
Podrihalo O, Podrigalo L, Jagiełło W, Iermakov S, Yermakova T. Substantiation of Methods for Predicting Success in Artistic Swimming. International Journal of Environmental Research and Public Health, 2021;18(16): 8739. https://doi.org/10.3390/ijerph18168739
Podrigalo L, Keo S, Podrihalo O, Оlkhovyi O, Paievskyi V, Kraynik Y. Justification of the Selection Techniques in Martial Arts using Wald’s Sequential Analysis. Physical Education Theory and Methodology, 2022;22(4): 576–582. https://doi.org/10.17309/tmfv.2022.4.17
Romanenko V, Cynarski WJ, Tropin Y, Kovalenko Y, Korobeynikov G, Piatysotska S, et al. Methodology for Assessing Spatial Perception in Martial Arts. Applied Sciences, 2025;15(6): 3413. https://doi.org/10.3390/app15063413
Romanenko V, Podrigalo L, Cynarski WJ, Rovnaya O, Korobeynikova L, Goloha V, et al. A comparative analysis of the short-term memory of martial arts’ athletes of different level of sportsmanship. Ido Movement for Culture. Journal of Martial Arts Anthropology, 2020;20(3): 18–24. https://doi.org/10.14589/ido.20.3.3
Podrihalo O, Romanenko V, Podrigalo L, Iermakov S, Оlkhovyi O, Bondar A, et al. Evaluation of the functional state of taekwondo athletes 7-13 years old according to the indicators of the finger-tapping test. Slobozhanskyi Herald of Science and Sport, 2023;27(1): 3–9. https://doi.org/10.15391/snsv.2023-1.001
Romanenko V, Рiatysotska S, Lytvynenko A, Baibikov M, Boychenko N, Ponomarov V. Methodology for assessing the reaction of combat athletes to a moving object. Slobozhanskyi Herald of Science and Sport, 2024;28(2): 69–77. https://doi.org/10.15391/snsv.2024-2.003
Romanenko V, Piatysotska S, Podrigalo L, Baibikov M, Boychenko N, Volodchenko O. Methodology for evaluating the “Go/No-Go” reaction in martial arts. Journal of Physical Education and Sport, 2024;24 (issue 12): 2139–2146. https://doi.org/10.7752/jpes.2024.12312
Romanenko V, Piatysotska S, Tropin Y, Rydzik Ł, Holokha V, Boychenko N. Study of the reaction of the choice of combat athletes using computer technology. Slobozhanskyi Herald of Science and Sport, 2022;26(4): 97–103. https://doi.org/10.15391/snsv.2022-4.001
Antomonov MIu. Processing and analysis of biomedical data. Kiev; 2025. (in Ukranian).
Farahani J, Soltani P, Rezlescu C, Walsh V. Assessing decision making using 2D animations in elite academy footballers. In: Progress in Brain Research, Elsevier; 2020. p. 71–85. https://doi.org/10.1016/bs.pbr.2020.06.016
Silva T, Martins N, Cunha P, Soares F, Carvalho V. The Role of Design and Digital Media in Monitoring and Improving the Performance of Taekwondo Athletes. Designs, 2023;7(6): 130. https://doi.org/10.3390/designs7060130
Zhong J, Xu J. Taekwondo Action Design Combining CAD and Virtual Reality Technology. Computer-Aided Design and Applications, 2021;19(S5): 132–142. https://doi.org/10.14733/cadaps.2022.S5.132-142
Yao WX, Fischman MG, Wang YT. Motor Skill Acquisition and Retention as a Function of Average Feedback, Summary Feedback, and Performance Variability. Journal of Motor Behavior, 1994;26(3): 273–282. https://doi.org/10.1080/00222895.1994.9941683
Hughes M, Franks IM, Dancs H, [eds.]. Essentials of Performance Analysis in Sport.. 3rd ed. Routledge; 2019. https://doi.org/10.4324/9780429340130
Muiños M, Ballesteros S. Peripheral vision and perceptual asymmetries in young and older martial arts athletes and nonathletes. Attention, Perception, & Psychophysics, 2014;76(8): 2465–2476. https://doi.org/10.3758/s13414-014-0719-y
Wu Y, Zeng Y, Zhang L, Wang S, Wang D, Tan X, et al. The role of visual perception in action anticipation in basketball athletes. Neuroscience, 2013;237: 29–41. https://doi.org/10.1016/j.neuroscience.2013.01.048
Zwierko T, Osinski W, Lubinski W, Czepita D, Florkiewicz B. Speed of Visual Sensorimotor Processes and Conductivity of Visual Pathway in Volleyball Players. Journal of Human Kinetics, 2010;23(2010): 21–27. https://doi.org/10.2478/v10078-010-0003-8
Litwiniuk A, Knas M, Grants J. The diagnostic value of the ‘Rational Test’ in preclinical studies – an example of combat and non-combat sports athletes research before and after an alpine skiing course. Archives of Budo, 2021;17:357–370.
Podrigalo L, Cynarski WJ, Rovnaya O, Volodchenko O, Halashko O, Volodchenko J. Studying of physical development features of elite athletes of combat sports by means of special indexes. Ido Movement for Culture. Journal of Martial Arts Anthropology, 2019;(19): 51–57. https://doi.org/10.14589/ido.19.1.5
Sung YC, Liao YH, Chen CY, Chen YL, Chou CC. Acute changes in blood lipid profiles and metabolic risk factors in collegiate elite taekwondo athletes after short-term de-training: a prospective insight for athletic health management. Lipids in Health and Disease, 2017;16(1): 143. https://doi.org/10.1186/s12944-017-0534-2
Holmes B, McHale IG, Żychaluk K. A Markov chain model for forecasting results of mixed martial arts contests. International Journal of Forecasting, 2023;39(2): 623–640. https://doi.org/10.1016/j.ijforecast.2022.01.007
Velichkov B, Koychev I, Boytcheva S. Deep Learning Contextual Models for Prediction of Sport Events Outcome from Sportsmen Interviews. In: Proceedings - Natural Language Processing in a Deep Learning World, Incoma Ltd., Shoumen, Bulgaria; 2019. p. 1240–1246. https://doi.org/10.26615/978-954-452-056-4_142
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