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  1. 1

    Real-time performance prediction in long-distance trail running: a practical model based on terrain difficulty and pacing variability

    Gutiérrez, H., Piedrafita, E., Bascuas, P. J., Arbonés, I., Berzosa, C., Bataller-Cervero, A. V.
    Published in Sports (2025)
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  2. 2

    Training load, mileage, and perceived exertion as a predictive model of injury and illness in women`s soccer

    Franks, C., Yockey, A., Bosley, N., Myers, T., Armstrong, K., Valliant, M., Wade, C.
    Published in Sports (2025)
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  3. 3

    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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  4. 4

    From body to combat: Physical characterization of fencers according to their weapon

    Roa, J. E., Arias, N. D., Gaviria, J., Orejuela, D. F., Rojas-Padilla, I. C.
    Published in SportMont (2025)
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  5. 5

    Modelling of performance prediction by analysis of elite swimmers` anthropometry, peak performance age and age-related performance progression

    Nazari Mehrabi, A., Imani, H., Khademnoe, O., Khantan, M., Lundberg, T. R., Gorzi, A.
    Published in PLOS ONE (2025)
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  6. 6

    Physiological predictors of peak velocity in the VAM-EVAL incremental test and the role of kinematic variables in running economy in triathletes

    Montraveta, J., Fernández-Jarillo, I., Iglesias, X., Feldmann, A., Chaverri, D.
    Published in Sports (2025)
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  7. 7

    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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  8. 8

    Utilising competition participation history at youth age to forecast dropout risk and sporting success likelihoods in competitive swimming

    Masismadi, N. A., Wylde, M., Huynh, M., Gastin, P. B., Chia, E., Suppiah, H. T.
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  9. 9

    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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  10. 10

    Beyond pace: predicting 1500m freestyle times with multi-feature random forests

    Russomanno, T.
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  11. 11

    Comparison of the results of the 15-meter sprint swimming (TT-15) with the results of the hundred-meter race for professional swimmers: potential and limits of applicability of the...

    Zaitsev, V. S., Molina, I.
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  12. 12

    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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  13. 13

    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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  14. 14

    What it takes to win: examining predicted versus actual swimming performances at the Paris 2024 Olympic Games, and what comes next

    Powell, C, Pyne, D, Crowley, E, Mujika, I.
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  15. 15

    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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  16. 16

    Accuracy of smartwatches in predicting distance running performance

    Dai, J., Chen, G., Gu, Z., Qi, Y., Xu, K.
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  17. 17

    Longitudinal performance trajectories of young female sprint runners: a new tool to predict performance progression

    Romann, M., Javet, M., Hernandez, J., Heyer, L., Trösch, S., Cobley, S., Born, D.,P.
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  18. 18

    Prediction of vertical ground reaction forces under different running speeds: integration of wearable IMU with CNN-xLSTM

    Chen, T., Xu, D., Zhou, Z., Zhou, H., Shao, S., Gu, Y.
    Published in Sensors (2025)
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  19. 19

    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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  20. 20

    Win your race goal: A generalized approach to prediction of running performance

    Dash, S.,
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