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  1. 1

    The classification of technical devices in the training process of athletes playing games

    Guba V. P., Rodin A. V., Skripko A. D., Pleshakov A. A.
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  2. 2

    Assessment of 16-year-old female basketball players with chronojump system

    Borukova, M., Kalinov, I.
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  3. 3

    A systematic review of sport-related packages within the R CRAN repository

    Casals, M., Fernández, J., Martínez, V., Lopez, M., Langohr, K., Cortés, J.
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  4. 4

    Spielanalyse im Basketball (Basketball game analysis)

    König, S., Heckel, J.
    Published in Spielanalyse im Sportspiel (2022)
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  5. 5

    Application of deep learning methods for detection and tracking of players

    Ivasic-Kos, M., Host, K., Pobar, M.
    Published 2022
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  6. 6

    Machine learning and the NBA Game

    Georgievski, B., Vrtagic, S.
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  7. 7

    Neuer Service der RTK: DHB-TrainercenterTV - Das digitale Handwerkszeug für alle Trainer (New service of the RTK: DHB-TrainercenterTV - The digital tool for all trainers )

    Beppler, J.
    Published in Handballtraining (2021)
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  8. 8

    As seen on TV: Automatic basketball video production using Gaussian-based actionness and game states recognition

    Quiroga, J., Carrillo, H., Maldonado, E., Ruiz, J., Zapata, L. M.
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  9. 9

    Quantifying throwing load in handball: a method for measuring the number of throws

    Skejø, S. D., Liaghat, B., Jakobsen, C. C., Møller, M., Bencke, J., Papi, G., Kunwald, N. P., Sorensen, H.
    Published in Sports Biomechanics (2021)
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  10. 10

    Descriptive and kinetic analysis of two different vertical jump tests among youth and adolescent male basketball athletes using a supervised machine learning approach

    Gillett, J., De Witt, J., Stahl, C. A., Martinez, D., Dawes, J. J.
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  11. 11

    Innovacionnaja tehnologija komp'juternogo testirovanija psihomotoriki v sportivnyh igrah (Innovative technology in computer testing of psycho-motor in team sports)

    Lisenchuk, G., Tyshchenko, V.
    Published in Science in Olympic Sport (2019)
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  12. 12

    Wer nicht fragt, der nicht gewinnt. Vereins- und mannschaftsinterne Anpassungen auf der Basis einer Saisonauswertung per individuellem Online-Fragebogen (If you don't ask, you don't win. Club- and team-internal adjustments on the basis of a season evaluation with individual online questionnaire)

    Grgic, M.
    Published in Handballtraining (2021)
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  13. 13

    Accuracy and reliability of Sage Analytics tracking system based on UWB technology for indoor team sports

    Hulka, K., Strniste, M., Prycl, D.
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  14. 14

    Performance prediction and evaluation in female handball players using machine learning models

    Oytun, M., Tinazci, C., Sekeroglu, B., Acikada, C., Yavuz, H. U.
    Published in IEEE Access (2020)
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  15. 15

    A graded concept of an information model for evaluating performance in team handball

    Schwenkreis, F.
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  16. 16

    Active player detection in handball videos using optical flow and STIPs based measures

    Ivasic-Kos, M., Pobar, M., Gonzàlez, J.
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  17. 17

    Mask R-CNN and optical flow based method for detection and marking of handball actions

    Pobar, M., Ivasic-Kos, M.
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  18. 18

    Building a labeled dataset for recognition of handball actions using mask R-CNN and STIPS

    Ivasic-Kos, M., Pobar, M.
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  19. 19

    Detection of the leading player in handball scenes using Mask R-CNN and STIPS

    Pobar, M., Ivasic-Kos, M.
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  20. 20

    Model-based behavioral causality analysis of handball with delayed transfer entropy

    Itoda, K., Watanabe, N., Takefuji, Y.
    Published in Procedia - Computer Science (2015)
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