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Theory and Practice of Physical Culture and Sports

Article

The use of innovative technologies and data analysis in calculating indicators of technical and tactical actions during a training macrocycle to predict the success of the competition season

Dmytro Zelenyi
Retrieved from Vol. 4, No. 1, 2025 Pages 9–20
Received
10.11.2024
Revised
25.04.2025
Accepted
23.06.2025
Views
280

Abstract

The purpose of the study was to predict the success of a football team’s competitive season by integrating innovative technologies and data analysis. Data from Global Positioning System/Inertial Measurement Unit sensors (speed, distance, Player Load), smart balls (impact force, accuracy of passes), video analytics (positioning, group actions), wearable devices (heart rate variability, pulse), and external sources (Opta, Wyscout) were used. It was found that the accuracy of passes increased from 78% to 85%, the impact force – by 15% (from 80 N to 92 N), and the number of counterattacks increased by 30%. A correlation was found between a 10% decrease in heart rate variability and a 25% increase in the risk of injury (correlation coefficient r = 0.81). A 12% improvement in maximum oxygen consumption and a 20% reduction in joint load confirmed the effectiveness of training programmes. Based on the XGBoost machine learning algorithm, a predictive model was developed with an accuracy of 92%, which determined the probability of a team entering the top 3 of the leagues (68%) and relegation (12%). Analysis of the opponents’ weaknesses (60% of goals conceded through the flanks) allowed formulating tactical recommendations, in particular, an emphasis on flank attacks (an increase in efficiency by 25%). The results confirmed that integrating data from different sources is a key tool for improving team performance in competitions. Practical significance lies in optimising training by monitoring heart rate variability (reducing injuries by 20%) and adapting game strategies

Keywords

sports; football; machine learning; heart rate variability; workout optimisation

References

  1. Andrienko, G.S. (2024). Preparation of highly qualified cheerleading athletes for the main competitions of the year (based on the discipline of cheerdance freestyle duet). Kyiv: National University of Ukraine on Physical Education and Sport.
  2. Baptista, I. (2019). Football training specificity. Training individualization within the collective periodization. Tromso: UiT the Arctic University of Norway.
  3. Biró, A., Cuesta-Vargas, A.I., & Szilágyi, L. (2024). AI-assisted fatigue and stamina control for performance sports on IMU-generated multivariate times series datasets. Sensors, 24(1), article number 132. doi: 10.3390/s24010132.
  4. Calle, O., López-Sierra, P., Feu, S., & Ibáñez, S.J. (2025). Analysis of tasks and training load during pre-season training in professional basketball. Apunts Sports Medicine, 60(225), article number 100466. doi: 10.1016/j.apunsm.2024.100466.
  5. Crespo, M., Martínez-Gallego, R., & Filipcic, A. (2024). Determining the tactical and technical level of competitive tennis players using a competency model: A systematic review. Frontiers in Sports and Active Living, 6, article number 1406846. doi: 10.3389/fspor.2024.1406846.
  6. Declaration of Helsinki. (2025). Retrieved from https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/.
  7. Díez, A., Lozano, D., Arjol-Serrano, J.L., Mainer-Pardos, E., Castillo, D., Torrontegui-Duarte, M., Nobari, H., Jaén-Carrillo, D., & Lampre, M. (2021). Influence of contextual factors on physical demands and technical-tactical actions regarding playing position in professional soccer players. BMC Sports Science, Medicine and Rehabilitation, 13, article number 157. doi: 10.1186/s13102-021-00386-x.
  8. Ferraz, A., Duarte-Mendes, P., Sarmento, H., Valente-Dos-Santos, J., & Travassos, B. (2023). Tracking devices and physical performance analysis in team sports: A comprehensive framework for research-trends and future directions. Frontiers in Sports and Active Living, 5, article number 1284086. doi: 10.3389/fspor.2023.1284086.
  9. Goes, F.R., et al. (2021). Unlocking the potential of big data to support tactical performance analysis in professional soccer: A systematic review. European Journal of Sport Science, 21(4), 481-496. doi: 10.1080/17461391.2020.1747552.
  10. Gonçalves, G., Neta, P., Ribeiro, J., & Guimarães, E. (2025). Internal and external loads during formal training and competition, physical capacities, and technical skills in youth basketball: A comparison between starters and rotation players. Journal of Human Kinetics, 96, 53-67. doi: 10.5114/jhk/201159.
  11. Guerrero-Calderón, B., Klemp, M., Castillo-Rodriguez, A., Morcillo, J.A., & Memmert, D. (2021). A new approach for training-load quantification in elite-level soccer: Contextual factors. International Journal of Sports Medicine, 42(8), 716-723. doi: 10.1055/a-1289-9059.
  12. Kostiukevych, V., Imas, Y., Borysova, O., Dutchak, M., Shynkaruk, O., Kogut, I., Voronova, V., Shlonska, O., & Stasiuk, I. (2018). Modeling of the athletic training process in team sports during an annual macrocycle. Journal of Physical Education and Sport, 18(11), 327-334. doi: 10.7752/jpes.2018.s144.
  13. Kvasnytsia, O., & Tyshchenko, O. (2024). Annual macrocycle of training in rugby-15. Problems and Prospects of the Development of Sports Games and Martial Arts in Higher Education Institutions, 1, 80-85.
  14. Leão, C., Clemente, F.M., Lemos, J., Mota, D., Roriz, P., Camões, M., & Carral, J.M.C. (2025). Dietary intake and training load in youth male soccer players during a microcycle: Analysis of intra-week variation and examination of associations between measures. International Journal of Sports Science & Coaching, 20(3), 1059-1070. doi: 10.1177/17479541251333953.
  15. Liu, Y., Cheng, L., Xu, J., Wang, L., & Cheng, W. (2025). Exploring the training philosophy and practical strategies of Chen Wenbing, the gold medal coach of Chinese weightlifting. International Journal of Sports Science & Coaching. doi: 10.1177/17479541251333891.
  16. Lochman, V., Tyshchenko, V., Tovstopiatko, F., Pyptiuk, P., Ivanenko, S., & Pozmogova, N. (2021). Use of innovative technical means to increase the training process effectiveness in handball. Journal of Physical Education and Sport, 21(4), 1695-1704. doi: 10.7752/jpes.2021.04215.
  17. Mangan, S. (2019). An investigation into the technical and running performance metrics of Gaelic football training and match-play to inform the design of a novel tactical periodisation training model. Cork: Cork Institute of Technology. doi: 10.34719/SEVS8182.
  18. Milheiro, A., Baptista, I., Nakamura, F.Y., Sarmento, H., Clemente, F.M., Silva, J.R., & Afonso, J. (2024). The influence of competition time in soccer players performance factors: A scoping review. SportRxiv. doi: 10.51224/SRXIV.387.
  19. Opta. (n.d.). Retrieved from https://optaplayerstats.statsperform.com/en_GB/soccer.
  20. Padrón-Cabo, A., Solleiro-Duran, D., Lorenzo-Martínez, M., Nakamura, F.Y., Campos-Vázquez, M., & Rey, E. (2024). Application of arbitrary and individualized load quantification strategies over the weekly microcycle in professional soccer players. Biology of Sport, 41(1), 153-161. doi: 10.5114/biolsport.2024.129481.
  21. Plakias, S., Kokkotis, C., Giakas, G., Tsaopoulos, D., & Moustakidis, S. (2024). Can artificial intelligence revolutionize soccer tactical analysis? Trends in Sport Sciences, 31(3), 151-160. doi: 10.23829/TSS.2024.31.3-3.
  22. Popov, S., & Sovenko, S. (2024). Experimental verification of the effectiveness of the training program for qualified ultramarathon runners specializing in road running at a distance of 100 km. Sport Science Spectrum, 3, 60-67. doi: 10.32782/spectrum/2024-3-9.
  23. Roa, J.A. (2024). The science of peak sports performance: A guide to the assessment, control, and monitoring of training. Retrieved from https://books.google.com.ua/books.
  24. Simbolon, G.F., & Marcelino, F. (2024). Optimizing football tournament predictions: A decision support system utilizing the ELECTRE method for multi-criteria outcome forecasting. International Journal of Enterprise Modelling, 18(2), 73-82.
  25. Staśkiewicz-Bartecka, W., Kardas, M., Zydek, G., Zając, A., & Chycki, J. (2024). Changes in body composition and nutritional periodization during the training macrocycle in football – a narrative review. Nutrients, 16(9), article number 1332. doi: 10.3390/nu16091332.
  26. Teixeira, J.E., et al. (2024). Analyzing key factors on training days within a standard microcycle for young sub-elite football players: A principal component approach. Sports, 12(7), article number 194. doi: 10.3390/sports12070194.
  27. Teixeira, J.E., Forte, P., Ferraz, R., Leal, M., Ribeiro, J., Silva, A.J., Barbosa, T.M., & Monteiro, A.M. (2021). Monitoring accumulated training and match load in football: A systematic review. International Journal of Environmental Research and Public Health, 18(8), article number 3906. doi: 10.3390/ijerph18083906.
  28. Torres-Ronda, L., Beanland, E., Whitehead, S., Sweeting, A., & Clubb, J. (2022). Tracking systems in team sports: A narrative review of applications of the data and sport specific analysis. Sports Medicine-Open, 8, article number 15. doi: 10.1186/s40798-022-00408-z.
  29. Vladovic, J., Versic, S., Foretic, N., Morgans, R., & Modric, T. (2023). Quantification of external training load among elite-level goalkeepers within competitive microcycle. Applied Sciences, 13(19), article number 10880. doi: 10.3390/app131910880.
  30. Webb, M. (2024). Training load and technical performance. Dunedin: Otago Polytechnic. doi: 10.34074/thes.6514.
  31. Wenpeng, T. (2024). Modeling of technical and tactical activities of highly qualified athletes in 3x3 basketball. Kyiv: National University of Physical Education and Sports of Ukraine.
  32. Wyscout. (n.d.). Retrieved from https://wyscout.com.
  33. Zhigon, S. (2023). Selection of basketball players at the stage of specialized basic training taking into account pedagogical and morphological criteria. Kyiv: National University of Physical Education and Sports of Ukraine.

Suggested citation

Zelenyi, D. (2025). The use of innovative technologies and data analysis in calculating indicators of technical and tactical actions during a training macrocycle to predict the success of the competition season. Theory and Practice of Physical Culture and Sports, 4(1), 9-20. https://doi.org/10.69587/tppcs/1.2025.09