Search Results - Applied Mathematical Modelling

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  1. 1

    Artificial intelligence in endurance sports: Metabolic, recovery, and nutritional perspectives

    Grivas, G. V., Safari, K.
    Published in Nutrients (2025)
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  2. 2

    Synthetic data for sharing and exploration in high-performance sport: considerations for application

    Warmenhoven, J., Impellizzeri, F. M., Shrier, I., Vigotsky, A. D., Lolli, L., Menaspà, P., Coutts, A. J., Fanchini, M., Hooker, G.
    Published in Sports Medicine (2025)
    “…Methods: The software package in R, synthpop, was used in seven simulation conditions applied to a professional football dataset, with varying model constraints. …”
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  3. 3

    VO2FITTING software: New insights and practical applications (VO2FITTING software update)

    Monteiro, A. S. M., Azevedo, R. M. S., Zacca, R., Ogonowska-Slodownik, A., Buzzachera, C. F., Vilas-Boas, J. P., Fernandes, R. J.
    Published in Journal of Human Kinetics (2025)
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  4. 4

    Gender-specific predictors of vault performance in gymnastics: a machine learning approach

    Ðordevic, D., Vodicar, J., Kreft, R., Kolar, E., Paunovic, M., Velickovic, S., Marinšek, M.
    Published in Science of Gymnastics Journal (2025)
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  5. 5

    AI chatbots in sport psychology: a paradigm shift or a complementary tool?

    Saraç, H., Polat, I. G.
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  6. 6

    Leveraging artificial intelligence and machine learning in sport sciences: a systematic literature review of applications, outcomes, and future directions

    Kumar, R., Bogia, P., Haq, A. U., Singh, V., Reddy, T. O.
    Published in Sport Sciences for Health (2025)
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  7. 7

    A synthetic data-driven machine learning approach for athlete performance attenuation prediction

    Cordeiro, M. C., Cathain, C. O., Daly, L., Kelly, D. T., Rodrigues, T. B.
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  8. 8

    Comparison of ground reaction forces and net joint moment predictions: skeletal model versus artificial neural network-based approach

    Cordero-Sánchez, J., Bazuelo-Ruiz, B., Pérez-Soriano, P., Serrancolí, G.
    Published in Journal of Applied Biomechanics (2025)
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  9. 9

    Machine learning approaches to injury risk prediction in sport: a scoping review with evidence synthesis

    Leckey, C., van Dyk, N., Doherty, C., Lawlor, A., Delahunt, E.
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  10. 10

    Identification of athletic and sports profiles through machine learning applied to heart rate variability

    Estrella, T., Capdevila, L.
    Published in Sports (2025)
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  11. 11

    Machine learning model to study the rugby head impact in a laboratory setting

    Stitt, D., Kabaliuk, N., Spriggs, N., Henley, S., Alexander, K., Draper, N.
    Published in PLOS ONE (2024)
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  12. 12

    An educational review on machine learning: a swot analysis for implementing machine learning techniques in football

    Beato, M., Jaward, M. H., Nassis, G. P., Figueiredo, P., Clemente, F. M., Krustrup, P.
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  13. 13

    Sensitivity analysis of bicycle characteristics for pavement roughness monitoring by vibration data

    Ghaderi, O., Cafiso, S., Caponnetto, R., Pappalardo, G.
    Published in Journal of Science and Cycling (2024)
    “…Therefore, the mathematical formulation based on the half-car relaxation model was integrated in a genetic algorithm optimization process to identify the bike parameters which minimize the error between the simulated and in-field Root Mean Square (RMS) of vertical accelerations. …”
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  14. 14

    A mathematical model for nordic skiing

    MacDonald, J. S., Cardales, R. O., Stockie, J. M.
    Published in arXiv e-print repository (2024)
    “…Nordic skiing provides fascinating opportunities for mathematical modelling studies that exploit methods and insights from physics, applied mathematics, data analysis, scientific computing and sports science. …”
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  15. 15

    Incorporating the maximal mean power profile in time trial simulations for more efficient optimal pacing strategy calculations

    Zignoli, A., Biral, F.
    Published in Journal of Science and Cycling (2024)
    “…Mathematical modelling in cycling enables retrospective analysis and predictive simulations, crucial for optimizing performance. …”
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  16. 16

    A fast gradient convolution kernel compensation method for surface electromyogram decomposition

    Lin, C., Cui, Z., Chen, C., Liu, Y., Chen, C., Jiang, N.
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  17. 17

    Using video analysis and artificial neural network to explore association rules and influence scenarios in elite table tennis matches

    Liu, J. W., Hsu, M. H., Lai, C. L., Wu, S. K.
    Published in The Journal of Supercomputing (2024)
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  18. 18

    2024: anticipating record-breaking performances in front crawl swimming through mathematical analysis

    Patoz, A., Finiel, L., Borrani, F.
    Published in Frontiers in Physiology (2024)
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  19. 19

    Modelling of a cyclist`s power for time trials on a velodrome

    Bos, L., Slawinski, M. A., Slawinski, R. A., Stanoev, T.
    Published in Sports Engineering (2024)
    “…We formulate a phenomenological model to study the power applied by a cyclist on a velodrome—for individual timetrials—taking into account the straights, circular arcs, connecting transition curves, and banking. …”
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  20. 20

    A novel mathematical model of the badminton smash: simulation and modeling in biomechanics

    Putra, V. G. V., Mohamad, I. N., Mohamad, J. N.
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