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  1. 1

    An analysis of the 6-h ultra-marathon race using a machine learning approach

    Thuany, M., Weiss, K., Valero, D., Villiger, E., Andrade, M. S., Nikolaidis, P. T., Scheer, V., de Lira, C. A. B., Vancini, R. L., Cuk, I., Braschler, L., Rosemann, T., Knechtle, B.
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  2. 2

    Beyond playing positions: categorizing soccer players based on match-specific running performance using machine learning

    de Haan, M., van der Zwaard, S., Sanders, J., Beek, P. J., Jaspers, R. T.
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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

    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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  5. 5

    An empirical model for world record running speeds with distance, age, and sex: anaerobic and aerobic contributions to performance

    Roy, T. K., Joyner, M. J., Senefeld, J. W., Wiggins, C. C., Secomb, T. W.
    Published in Journal of Applied Physiology (2024)
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  6. 6

    Running functional threshold versus critical power: Same concept but different values

    Ñancupil-Andrade, A. A., Ruiz-Alias, S. A., Pérez-Castilla, A., Jaén-Carrillo, D., García-Pinillos, F.
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  7. 7

    Comparison of different machine learning models to enhance sacral acceleration-based estimations of running stride temporal variables and peak vertical ground reaction force

    Patoz, A., Lussiana, T., Breine, B., Grindre, C., Malatesta, D.
    Published in Sports Biomechanics (2025)
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  8. 8

    Longitudinal analysis of the 800-m performances of the world's best female long-distance pool swimmer: A case study using critical speed and D'

    Barroso, R., Crivoi do Carmo, E., Foster, C., Skiba, P., Carvalho Barbosa, A.
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  9. 9

    Is Stryd critical power a meaningful parameter for runners?

    Dearing, C. G., Paton, C. D.
    Published in Biology of Sport (2023)
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  10. 10

    Is the use of the coefficient of variation a valid way to assess the swimming intra-cycle velocity fluctuation?

    Gonjo, T., Fernandes, R. J., Vilas-Boas, J. P., Sanders, R.
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  11. 11

    Identification of subject-specific responses to footwear during running

    Horst, F., Hoitz, F., Slijepcevic, D., Schons, N., Beckmann, H., Nigg, B. M., Schöllhorn, W. I.
    Published in Scientific Reports (2023)
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  12. 12

    Prediction of marathon performance using artificial intelligence

    Lerebourg, L., Saboul, D., Clémençon, M., Coquart, J. B.
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  13. 13

    Is maximal lactate accumulation rate promising for improving 5000-m prediction in running?

    Quittmann, O. J., Foitschik, T., Vafa, R., Freitag, F. J., Sparmann, N., Nolte, S., Abel, T.
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  14. 14

    Relationship between body shape and performance in Japanese elite collegiate male swimmers: use of a 3-D homologous body model

    Soma, M., Kashiwagi, Y., Funato, K.
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  15. 15

    TRAP: a predictive framework for the assessment of performance in trail running

    Fogliato, R., Oliveira, N. L., Yurko, R.
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  16. 16

    Deep learning-based energy expenditure estimation in assisted and non-assisted gait using inertial, EMG, and heart rate wearable Sensors

    Lopes, J. M., Figueiredo, J., Fonseca, P., Cerqueira, J. J., Vilas-Boas, J. P., Santos, C. P.
    Published in Sensors (2022)
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  17. 17

    Women`s modern pentathlon scoring systems and predictive modelling for decision support

    Im, S. H., O'Donoghue, P.
    Published in Journal of Sports Analytics (2022)
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  18. 18

    {shorts}: An R package for modeling short sprints

    Jovanovic, M., Vescovi, J.
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  19. 19

    Optimizing the training mode of swimmers based on deep learning and data mining technology

    Liu, S., Sun, S.
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  20. 20

    Mining marathon training data to generate useful user profiles

    Berndsen, J., Smyth, B., Lawlor, A.
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