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  1. 1

    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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  2. 2

    Machine learning model to study the rugby head impact in a laboratory setting

    Stitt, D., Kabaliuk, N., Spriggs, N., Henley, S., Alexander, K., Draper, N.
    Published in PLOS ONE (2024)
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  3. 3

    School-age talent identification in female handball by a mathematical model: Equation proposed from anthropometry, maturity offset, fitness, and technical field-tests

    Caporal, G., Gaya, A. C. A., Pedretti, A., Mello, J. B.
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  4. 4

    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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  5. 5

    Production functions of NCAA men and women water polo matches

    Gullikson, J., Gale, L. R., Mayberry, J. K., Kim, J., Killick, L.
    Published in Journal of Sports Analytics (2023)
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  6. 6

    Determining the relationship between load markers and non-contact injuries during the competitive season among professional and semi-professional basketball players

    Ferioli, D., La Torre, A., Tibiletti, E., Dotto, A., Rampinini, E.
    Published in Research in Sports Medicine (2021)
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  7. 7

    Swing shift: a mathematical approach to defensive positioning in baseball

    Bouzarth, E., Grannan, B., Harris, J., Hartley, A., Hutson, K., Morton, E.
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  8. 8

    Change the rules, change the game? Offensive tactics in elite men`s water polo from London to Tokyo

    Bernardi, D., Davis, L. L., Graham, J., Mayberry, J. K.
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  9. 9

    Influence of play type on the magnitude and number of head impacts sustained in youth american football

    Vale, A., Post, A., Cournoyer, J., Hoshizaki, T. B., Gilchrist, M. D.
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  10. 10

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

    Liu, S., Sun, S.
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  11. 11

    Optimizing the best play in basketball using deep learning

    Javadpour, L., Blakeslee, J., Khazaeli, M., Schroeder, P.
    Published in Journal of Sports Analytics (2020)
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  12. 12

    You cannot do that Ben Stokes: Dynamically predicting shot type in cricket using a personalized deep neural network

    Gürpinar-Morgan, W., Dinsdale, D., Gallaghar, J., Cherukumudi, A., Lucey, P.
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  13. 13

    Framework for intelligent swimming analytics with wearable sensors for stroke classification

    Costa, J., Silva, C., Santos, M., Fernandes, T., Faria, S.
    Published in Sensors (2021)
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  14. 14

    Sensor-based indices for the prediction and monitoring of anterior cruciate ligament injury: Reliability analysis and a case study in basketball

    Molinaro, L., Taborri, J., Santospagnuolo, A., Vetrano, M., Vulpiani, M. C., Rossi, S.
    Published in Sensors (2021)
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  15. 15

    Automated tackle injury risk assessment in contact-based sports - A Rugby Union example

    Martin, Z., Hendricks, S., Patel, A.
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  16. 16

    The changes in fractal dimension after a maximal exertion in swimming

    Barbosa, T. M., Simin, C., Forte, P., Morais, J. E.
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  17. 17

    Freestyle swimming analysis of symmetry and velocities using a MEMS based IMU: Introducing a symmetry score

    Stamm, A., Shlyonsky, I.
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  18. 18

    Modelling match outcome in Australian football: improved accuracy with large databases

    Young, C., Luo, W., Gastin, P., Tran, J., Dwyer, D.
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  19. 19

    A new mathematical approach to explore the post-exercise recovery process and its applicability in a cold water immersion protocol

    Micheletti, J. K., Vanderlei, F. M., Machado, A. F., de Almeida, A. C., Nakamura, F. Y., Netto Junior, J., Pastre, C. M.
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  20. 20

    Predictive capacity of the MADYMO multibody human body model applied to head kinematics during rugby union tackles

    Tierney, G. J., Simms, C.
    Published in Applied Sciences (2019)
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