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  1. 1

    Machine learning in sports: Open approach for next play analytics

    Fujii, K.
    Published 2025
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  2. 2

    Investigating the influence of head kinematics on head injury metrics and factors to consider for football helmet design

    Chatham, L. S., Young, T. S., Wojcik, R. M., Bell, L. A., Torbati, A. H., Carpenter, R. D., Jenkins, P. E., Poddar, S. K., Yakacki, C. M.
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  3. 3

    Parametric design methods development for the comparison of American football faceguards using validated structural stiffness models

    Ferriell, W. D., Wright, N. P., DesJardins, J. D., Batt, G. S.
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  4. 4

    Advancing NFL win prediction: from Pythagorean formulas to machine learning algorithms

    Weirich, C., Kim, J. W., Yoon, Y., Jeong, S.
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  5. 5

    The impact of localized muscle mass on sprint mechanics during the swing and stance phases of Division I collegiate American football players

    Metoyer, C. J., Lever, J. R., McGinty, M., Landow, L., Hunt, R., Hauenstein, J. D., Huebner, A., Stone, M. H., Wagle, J. P.
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  6. 6

    Homography based player identification in live sports

    Pandya, Y., Nandy, K., Agarwal, S.
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  7. 7

    Under pressure: Integrating machine learning to quantify clutch moments in American football

    Aich, A., Bhattacharjee, D., Saikia, H.
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  8. 8

    The reliability of 20 m sprint time using a novel assessment technique

    Holmberg, P. M., Olivier, M. H., Kelly, V. G.
    Published in Sensors (2025)
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  9. 9

    Model development for an artificial intelligence-based NCAA football ranking system: applying the PageRank algorithm

    Lee, S., Kang, M., Park, J.-H., Yun, H.-J.
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  10. 10

    Predicting sprint performance from the vertical and horizontal jumps in National Football League Combine athletes

    Agar-Newman, D. J., MacRae, F., Tsai, M.-C., Klimstra, M.
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  11. 11

    Entwicklung und Validierung einer videobasierten Trainingsmethode zur Verbesserung der Antizipation im American Football (Development and validation of a video-based training method to improve anticipation in American football)

    Miller, A., Künzell, S., Gegenfurtner, A.
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  12. 12

    Reliability and validity of sprint performance using the Alex7 motorized device

    Skujyte, A., Lukonaitiene, I., Stanislovaitiene, J., Šilinskas, V., Bradauskiene, K., Mamkus, G., Kamandulis, S.
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  13. 13

    Effect of data-processing methods on acceleration summary metrics of GNSS devices in elite australian football

    Ellens, S., Carey, D. L., Gastin, P. B., Varley, M. C.
    Published in Sensors (2024)
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  14. 14

    Smart textile impact sensor for e-helmet to measure head injury

    Saikia, M. J., Alkhader, A. S.
    Published in Sensors (2024)
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  15. 15

    Influence of infill depth and fibre height of artificial turf on rotational traction

    Ruschkowski, J., Varughese, J. M., Stefanyshyn, D. J., Wannop, J. W.
    Published in Sports Engineering (2024)
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  16. 16

    Workload monitoring tools in field-based team sports, the emerging technology and analytics used for performance and injury prediction: a systematic review

    Keys, G., Ryan, L., Faulkner, M., McCann, M.
    Published 2023
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  17. 17

    Barriers to coach decision-making during Australian football matches and how it can be supported by artificial intelligence

    Aarons, M. F., Vickery, W., Bruce, L., Young, C. M., Dwyer, D. B.
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  18. 18

    Kinematic assessment of the NOCSAE headform during blunt impacts with a pneumatic linear impactor

    Edwards, E. D., Landry, T., Jesunathadas, M., Plaisted, T. A., Neice, R. J., Gould, T. E., Kleinberger, M., Piland, S. G.
    Published in Sports Engineering (2023)
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  19. 19

    Validation of a multi-camera videogrammetry approach for quantifying helmet impact velocity in football

    Gyemi, D. L., Jadischke, R., Andrews, D. M.
    Published in Sports Engineering (2023)
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  20. 20

    Lower extremity injury rates on artificial turf versus natural grass playing surfaces: A systematic review

    Gould, H. P., Lostetter, S. J., Samuelson, E. R., Guyton, G. P.
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