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  1. 1

    From cycling 1.0 and 2.0 to cycling 3.0: The evolution of a sport through science, ethics, and artificial intelligence

    Zabala, M.
    Published in Journal of Science and Cycling (2025)
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  2. 2

    Comparative evaluation of ChatGPT versions in training program design: scientific approach, accuracy, and practical applicability

    Genç, A., Aydin, G. R., Kasap, M., Özkan, A., Rakhymzhanov, A., Gürcan, H. H., Güllü, S., Demirhan, B.
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  3. 3

    Causal effect analysis of serving performance using double machine learning

    Ma, J., Zou, F.
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  4. 4

    Emerging sports science technologies in decoding and preventing joint injuries: A new era for athletics in China and Asia

    Xiao, S., Liu, J.
    Published in Journal of Human Sport & Exercise (2026)
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  5. 5

    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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  6. 6

    An observational study of the reliability and concurrent validity of heart rate variability devices in athletes

    Johansson, H., Adderley, E., Clarke, S., McIntyre, P., Reilly, G., Caulfield, B., Holden, S.
    Published in Frontiers in Physiology (2025)
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  7. 7

    Gamechanger KI im Sport und der Trainingswissenschaft - Können wir der Technologie heute schon vertrauen? (Game changer AI in sports and training science - Can we already trust the technology today?)

    Fröhlich, M., Dindorf, C., Pieter, A., Bartaguiz, E., Kemmler, W.
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  8. 8

    Classic action recognition of cross-country skiing sports combining ant colony and clustering algorithm

    Yu, H., Li, Y.
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  9. 9

    Footwork recognition and trajectory tracking of football goalie using Multi-Sensor technology

    Ye, G., Liang, L.
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  10. 10

    Quantifying kinematic and coordinated movement patterns during pumping in windsurfing: the impact of footstraps use

    Chun, S., Park, J.
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  11. 11

    Maschinelles Lernen im Hochleistungsskateboarding (Machine learning in high-performance skateboarding)

    Musa, R. M., Rasid, A. M. A., Samsudin, C. N. C., Majeed, A. P. P. A., Abdullah, M. R.
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  12. 12

    Machine learning in sports: Open approach for next play analytics

    Fujii, K.
    Published 2025
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  13. 13

    Künstliche Intelligenz und maschinelles Lernen in der Sportwissenschaft (Artificial intelligence and machine learning in sports science)

    D. Memmert
    Published 2025
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  14. 14

    Using machine learning to determine the positions of professional soccer players in terms of biomechanical variables

    Yagin, F. H., Hasan, U. C. H., Clemente, F. M., Eken, O., Badicu, G., Gulu, M.
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  15. 15

    Determining critical power and W' in running: Accuracy of different two-point models using the power metric

    Ruiz-Alias, S. A., Ñancupil-Andrade, A. A., Pérez-Castilla, A., García-Pinillos, F.
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  16. 16

    Test-retest reliability and comparison of single- and dual-beam photocell timing system with video-based applications to measure linear and change of direction sprint times

    Thapa, R. K., Sarmah, B., Singh, T., Kushwah, G. S., Akyildiz, Z., Ramirez-Campillo, R.
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  17. 17

    Comparison of ground reaction force and contact time during various-direction lunges between badminton shoes without and with a lateral forefoot wedge sole

    Chen, W.-H., Chen, C.-H., Yang, W.-W., Lin, G.-Y., Hsu, W.-C., Shiang, T.-Y., Liu, C.
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  18. 18

    The informative power of heart rate along with machine learning regression models to predict maximal oxygen consumption and maximal workload capacity

    Gentilin, A.
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  19. 19

    How are the impact attenuation properties of men`s and women`s helmets affected after one season in professional ice hockey?

    Swarén, M., Fahlstedt, M.
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  20. 20

    Multi-sensor fusion based optimized deep convolutional neural network for boxing punch activity recognition

    Jayakumar, B., Govindarajan, N.
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