Abstract

This study investigated the effect of a data analytics-based coaching model on volleyball serve accuracy among adolescent male student-athletes. Forty male volleyball players aged 15–18 years were randomly assigned to either an experimental group or a control group. The experimental group participated in an eight-week coaching intervention incorporating statistical performance analysis, biomechanical evaluation through a Convolutional Neural Network–Long Short-Term Memory model based motion analysis system, and personalized feedback, whereas the control group received traditional coaching without data analytics. Serve accuracy was assessed using the American Alliance for Health, Physical Education, Recreation and Dance Volleyball Serve Test before and after the intervention. The data were analyzed using independent-samples t-tests and analysis of covariance, with Cohen’s d calculated as an effect size. The findings showed that the experimental group demonstrated significantly greater improvement in serve accuracy than the control group, with a large effect size. These findings indicate that a data analytics-based coaching model may enhance volleyball serve accuracy more effectively than traditional coaching. However, the results should be interpreted in relation to the combined components of the intervention and the specific characteristics of the study sample. 

Keywords

References

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