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  1. 1

    Positional contrasts in key techniques between men's and women's basketball at the Olympic level

    Zhang, Y., Wang, J., Tan, S.
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  2. 2

    Identification of lower limb muscle fatigue in basketball players based on sEMG signals

    Ma, X., Chen, S., Li, Q.
    Published in Frontiers in Physiology (2025)
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  3. 3

    Playing style identification in team sports: a systematic review from statistical dimensionality reduction to unsupervised machine learning

    Gu, L., Wang, W., Plakias, S., Zhang, S.
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  4. 4

    The application of artificial intelligence techniques in predicting game outcomes of professional basketball league: A systematic review

    Li, C., Zhang, H., Zhang, Y., Shen, J., An, R.
    Published in PLOS ONE (2025)
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  5. 5

    Early specialization in formative basketball: A machine learning analysis of shooting patterns in U14 and professional players

    Contreras-García, J. M., Courel-Ibáñez, J., Piñar-López, M. I., Ibáñez, S. J.
    Published in Journal of Sports Sciences (2024)
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  6. 6

    Game-related statistics for distinguishing winning and losing teams in Olympic basketball: The impact of game pace

    Chen, R., Zhang, M., Xu, X., Liu, Y.
    Published in Journal of Sports Sciences (2024)
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  7. 7

    Monitoring external load during real competition in male handball players through big data analytics: Differences by playing positions

    Manchado, C., Tortosa-Martinez, J., Marcos-Jorquera, D., Gilart-Iglesias, V., Pueo, B., Chirosa-Rios, L. J.
    Published in Kinesiology (2024)
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  8. 8

    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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  9. 9

    A goal-aligned coordinate system for invasion games

    Brandes, U.
    Published in Journal of Sports Analytics (2023)
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  10. 10

    Participation in the under-18 Euroleague Next Generation Tournament does not predict attaining NBA, Euroleague, or Eurocup

    Carvalho, H. M., Morais, C. Z., Lima, A. B., Galvão, L. G., Grosgeorge, B., Gonçalves, C. E.
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  11. 11

    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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  12. 12

    Estimating team possessions in high-level European basketball competition

    Charamis, E., Marmarinos, C., Ntzoufras, I.
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  13. 13

    Examining positional difference in basketball players` field goal accuracy using Bayesian hierarchical model

    Wang, F., Zheng, G.
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  14. 14

    Predictions of European basketball match results with machine learning algorithms

    Lampis, T., Ioannis, N., Vasilios, V., Stavrianna, D.
    Published in Journal of Sports Analytics (2023)
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  15. 15

    Predicting elite NBA lineups using individual player order statistics

    Martonosi, S. E., Gonzalez, M., Oshiro, N.
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  16. 16

    Handball offensive analysis: comparative evolution of linear tendency lines between finalist teams in the EHF CL

    Vaz, V., Ferrari, W., Sarmento, H., Couceiro, M., Sousa, T., Dias, G.
    Published in Applied Sciences (2023)
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  17. 17

    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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  18. 18

    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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  19. 19

    PIVOT: A parsimonious end-to-end learning framework for valuing player actions in handball using tracking data

    Müller, O., Caron, M., Döring, M., Heuwinkel, T., Baumeister, J.
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  20. 20

    Predicting season outcomes for the NBA

    Teno, G. D. S., Wang, C., Carlsson, N., Lambrix, P.
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