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  1. 1

    Initial evidence on the impact of the 2023 World Tennis Number algorithm change for predicting match outcomes

    Krall, N., Maroulis, N., Mayew, R., Mayew, W.
    Published in Coaching and Sport Science Review (2025)
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  2. 2

    Neuromechanical adaptations to EMG-guided SSC training in elite badminton players: a predictive multivariate approach

    Pronczuk, M., Skalski, D., Losinska, K., Maszczyk, A., Golas, A.
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  3. 3

    Comparing modern machine learning approaches and different forecast strategies on Grand Slam tennis tournaments

    Buhamra, N., Groll, A., Gerharz, A.
    Published in Journal of Sports Analytics (2025)
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  4. 4

    A stroke of genius: Predicting the next move in badminton

    Ibh, M., Graßhof, S., Hansen, D. W.
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  5. 5

    Development of sequential winning-percentage prediction model for badminton competitions: applying the expert system sequential probability ratio test

    Jo, E.
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  6. 6

    Psychological predictors of competitive levels in badminton athletes: a gender-stratified analysis

    Wang, Y., Li, Z., Xie, X., Yang, G., Chen, T., Wang, H.
    Published in Frontiers in Psychology (2025)
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  7. 7

    A comparative evaluation of Elo ratings- and machine learning-based methods for tennis match result prediction

    Bunker, R., Yeung, C., Susnjak, T., Espie, C., Fujii, K.
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  8. 8

    Within-match performance fluctuations: Assessment and observed vs. expected extent in table tennis

    Liu, R., Lames, M.
    Published in Journal of Human Kinetics (2024)
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  9. 9

    Prediction model and technical and tactical decision analysis of women`s badminton singles based on machine learning

    Yuan, H., Wang, Y., Yang, K., Bin, Y.
    Published in PLOS ONE (2024)
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  10. 10

    Analyzing and predicting the career trajectory of male elite junior tennis players: A machine learning approach

    Bozdech, M., Zhánel, J.
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  11. 11

    Analysis and prediction of unforced errors in men`s and women`s professional padel

    Conde-Ripoll, R., Muñoz, D., Sánchez-Alcaraz, B. J., Escudero-Tena, A.
    Published in Biology of Sport (2024)
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  12. 12

    Inertial sensors containing device-based match characteristics that act as prognosticators of elite, male badminton players' performance levels

    Abdullahi, Y., Coetzee, B.
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  13. 13

    Neuromuscular fitness is associated with serve speed in young female tennis players

    Bilic, Z., Martic, P., Barbaros, P., Sinkovic, F., Novak, D.
    Published in Sports (2024)
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  14. 14

    Factors that contribute to winning medals in international soft tennis events

    Kusubori, S., Tanaka, T.
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  15. 15

    A novel comparative study of NNAR approach with linear stochastic time series models in predicting tennis player's performance

    Almarashi, A. M., Daniyal, M., Jamal, F.
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  16. 16

    Modeling and prediction of tennis matches at Grand Slam tournaments

    Buhamra, N., Groll, A., Brunner, S.
    Published in Journal of Sports Analytics (2024)
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  17. 17

    Scoring probability model based on service landing location and ranking points in men`s professional tennis matches

    Shimizu, F., Konaka, E.
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  18. 18

    Is anticipation skill learning Bayesian?

    Smeeton, N. J., Meyer, J., Varga, M., Klatt, S.
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  19. 19

    Probability prediction of groundstroke stances among male professional tennis players using a tree-augmented Bayesian network

    Zhou, J. Q., Liu, Y.
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  20. 20

    Exploring the subjective beliefs of expert coaches on `talent` and player selection in German table tennis

    Koopmann, T., Faber, I. R., Lath, F., Loffing, F., Schorer, J.
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