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  1. 1

    Momentum matters: Investigating high-pressure situations in the NBA through scoring probability

    Mihalyi, B., Biczók, G., Toka, L.
    Published in Machine Learning and Data Mining for Sports Analytics. MLSA 2023. Communications in Computer and Information Science (2024)
    Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2023. Communications in Computer and Information Science…”
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  2. 2

    The big three: A practical framework for designing decision support systems in sports and an application for basketball

    Bautiste, F. J. S., Brunner, D., Koch, J., Laborie, T., Yang, L., El-Assady, M.
    Published in Machine Learning and Data Mining for Sports Analytics. MLSA 2023. Communications in Computer and Information Science (2024)
    Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2023. Communications in Computer and Information Science…”
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  3. 3

    Detecting swimmers in unconstrained videos with few training data

    Jacquelin, N., Vuillemot, R., Duffner, S.
    Published in Machine Learning and Data Mining for Sports Analytics. MLSA 2021. Communications in Computer and Information Science (2022)
    Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2021. Communications in Computer and Information Science…”
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  4. 4

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

    Schwenkreis, F.
    Published in Proceedings of the 8th International Conference on Data Science, Technology and Applications (2019)
    Collective title: “…Proceedings of the 8th International Conference on Data Science, Technology and Applications…”
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  5. 5

    A data snapshot approach for making real-time predictions in basketball

    Kayhan, V. O., Watkins, A.
    Published in Big data (2018)
    “…Big data…”
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  6. 6

    How to make sense of team sport data: From acquisition to data modeling and research aspects

    Stein, M., Janetzko, H., Seebacher, D., Jäger, A., Nagel, M., Hölsch, J., Kosub, S., Schreck, T., Keim, D. A., Grossniklaus, M.
    Published in Data (2017)
    “…Data…”
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