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  1. 1

    Augmented intelligence for FIFA predictions

    Jethuri, K., Emmadi, S. C., Samudrala, S., Natarajan, J., Ghotkar, P., Natu, M. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2024. Communications in Computer and Information Science…”
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  2. 2

    Mathematical models for off-ball scoring prediction in basketball

    Kono, R., Fujii, K. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2024. Communications in Computer and Information Science…”
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  3. 3

    GraphEIV: A framework for estimating the expected immediate value in basketball using graph neural networks

    Sá-Freire, B. M., Barbosa, G. R. G., Gonçalves, J. L. L., Schuster, J., Rios-Neto, H. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2024. Communications in Computer and Information Science…”
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  4. 4

    Characterizing serves in table tennis

    Eradès, A., Papon, T., Vuillemot, R. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2024. Communications in Computer and Information Science…”
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  5. 5

    Trends in real-time artificial intelligence methods in sports: a systematic review

    Vec, V., Tomažic, S., Kos, A., Umek, A.
    Published in Journal of Big Data (2024)
    “…Journal of Big Data…”
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  6. 6

    The impact of modern information technology on the development of time measuring at the Olympic and Paralympic Games

    Markovic, V., Ratkovic, T., Popovic, J., Miloševic, M. Collective title: “…International Scientific Conference on Information Technology, Computer Science, and Data Science…”
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  7. 7

    Identifying player roles in ice hockey

    Säfvenberg, R., Carlsson, N., Lambrix, P. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2023. Communications in Computer and Information Science…”
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  8. 8

    Elite rugby league players` signature movement patterns and position prediction

    Adeyemo, V. E., Palczewska, A., Jones, B., Weaving, D. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2023. Communications in Computer and Information Science…”
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  9. 9

    Boat speed prediction in SailGP

    Zentai, B., Toka, L. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2023. Communications in Computer and Information Science…”
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  10. 10

    Masked autoencoder pretraining for event classification in elite soccer

    Rudolph, Y., Brefeld, U. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2023. Communications in Computer and Information Science…”
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  11. 11

    Pass receiver and outcome prediction in soccer using temporal graph networks

    Rahimian, P., Kim, H., Schmid, M., Toka, L. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2023. Communications in Computer and Information Science…”
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  12. 12

    Field depth matters: Comparing the valuation of passes in football

    de Sá-Freire, L. M., Vaz-de-Melo, P. O. S. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2023. Communications in Computer and Information Science…”
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  13. 13

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

    Mihalyi, B., Biczók, G., Toka, L. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2023. Communications in Computer and Information Science…”
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  14. 14

    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. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2023. Communications in Computer and Information Science…”
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  15. 15

    What data should be collected for a good handball expected goal model?

    Mortelier, A., Rioult, F., Komar, J. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2023. Communications in Computer and Information Science…”
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  16. 16

    Towards expected counter - using comprehensible features to predict counterattacks

    Biermann, H., Wieland, F. G., Timmer, J., Memmert, D., Phatak, A. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2022. Communications in Computer and Information Science…”
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  17. 17

    Shot analysis in different levels of German football using expected goals

    Raudonius, L., Seidl, T. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2022. Communications in Computer and Information Science…”
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  18. 18

    Analyzing passing sequences for the prediction of goal-scoring opportunities

    McCarthy, C., Tampakis, P., Chiarandini, M., Randers, M. B., Jänicke, S., Zimek, A. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2022. Communications in Computer and Information Science…”
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  19. 19

    Let's penetrate the defense: a machine learning model for prediction and valuation of penetrative passes

    Rahimian, P., da Silva Guerra Gomes, D. G., Berkovics, F., Toka, L. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2022. Communications in Computer and Information Science…”
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  20. 20

    Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction

    Teranishi, M., Tsutsui, K., Takeda, K., Fujii, K. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2022. Communications in Computer and Information Science…”
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