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  1. 1

    Multidisciplinary prediction of running-related injuries using machine learning

    Wu, H., Brooke-Wavell, K., Barnes, M. R., Awan, Z., Mastana, S., Allen, S., Blagrove, R. C.
    Published in npj digital medicine (2026)
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  2. 2

    Predicting high-performance decathlon career best

    Battles, P., Noble, T. J., Chapman, R. F.
    Published in Experimental Physiology (2025)
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  3. 3

    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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  4. 4

    Using multilevel models to compare performance prediction and characterization abilities between power-law and critical-speed models in middle- and long-distance running

    Walt, M., Casado, A., Le Goff, J.-M.
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  5. 5

    Muscle typology of youth track-and-field athletes predicts their adult performance: a prospective study

    Vandecauter, J., Van Vossel, K., Van de Casteele, F., Derave, W., Lievens, E.
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  6. 6

    Evaluating the validity of tests to predict sprint and change of direction speed in para-athletes with brain impairments

    Reina, R., Beckman, E. M., Connick, M. J., Spathis, J. G., Tweedy, S. M.
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  7. 7

    The fourth dimension: physiological resilience as an independent determinant of endurance exercise performance

    Jones, A. M.
    Published in Journal of Physiology (2024)
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  8. 8

    Can ground reaction force variables preidentify the probability of a musculoskeletal injury in collegiate distance runners?

    Stewart, H. E., Alcantara, R. S., Farina, K. A., Hahn, M. E., Grabowski, A. M., Kram, R., Wilcox, R. R., McNitt-Gray, J. L.
    Published in Journal of Applied Biomechanics (2025)
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  9. 9

    Accuracy of smartwatches in predicting distance running performance

    Dai, J., Chen, G., Gu, Z., Qi, Y., Xu, K.
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  10. 10

    Predicting the potential of middle- and long-distance track runners by evaluating the performance improvement rate

    Lerebourg, L., Guignard, B., L´Hermette, M., Coquart, J. B.
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  11. 11

    Valid and accurate simple equation to predict 3,000-m steeplechase performance

    Barbry, A., Lerebourg, L., Racil, G., Jlid, M. C., Coquart, J.
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  12. 12

    Analysis of the 72-h ultramarathon using a predictive XG Boost model

    Knechtle, B., Villiger, E., Weiss, K., Valero, D., Gajda, R., Scheer, V., Barbosa de Lira, C. A., Braschler, L., Nikolaidis, P. T., Vancini, R. L., Cuk, I., Rosemann, T., Thuany, M.
    Published in Sport Sciences for Health (2024)
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  13. 13

    "Falling behind," "letting go," and being "outsprinted" as distinct features of pacing in distance running

    Foster, C., Barroso, R., Bok, D., Boullosa, D., Casado Alda, A., Cortis, C., Fusco, A., Hanley, B., Skiba, P., de Koning, J. J.
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  14. 14

    Reliability and validity of predicted performance in the severe-intensity domain from the 3-minute all-out running test

    Busso, T., Lloria-Varella, J., Sabater-Pastor, F.
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  15. 15

    Advancing 100m sprint performance prediction: A machine learning approach to velocity curve modeling and performance correlation

    Tam, C. K., Yao , Z.-F.
    Published in PLOS ONE (2024)
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  16. 16

    Female National Collegiate Athletic Association Division-I athlete injury prediction by vertical countermovement jump force-time metrics

    Merrigan, J. J., Stone, J. D., Kraemer, W. J., Vatne, E. A., Onate, J., Hagen, J. A.
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  17. 17

    Are pre-race serum blood biomarkers associated with the 24-h ultramarathon race performance?

    Amatori, S., Sisti, D., Bertuccioli, A., Rocchi, M. B. L., Luchetti, F., Nasoni, M. G., Papa, S., Citarella, R., Perroni, F., Benedetti, S.
    Published in European Journal of Sport Science (2024)
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  18. 18

    Perceptions and beliefs on sports injury prediction as an injury risk reduction strategy: An online survey on elite athletics (track and field) athletes, coaches, and health profes...

    Dandrieux, P.-E., Navarro, L., Chapon, J., Tondut, J., Zyskowski, M., Hollander, K., Edouard, P.
    Published in Physical Therapy in Sport (2024)
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  19. 19

    A fuzzy-based software tool used to predict 110m hurdles results during the annual training cycle

    Przednowek, K., Wiktorowicz, K., Krzeszowski, T., Iskra, J.
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  20. 20

    Training process modeling of hammer throwers taking into account peculiarities of stress adaptation

    Bolotin, A., Bakayev, V.
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