Use of Machine Learning to Model Volume Load Effects on Changes in Jump Performance

in International Journal of Sports Physiology and Performance
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Purpose: To use an artificial neural network (ANN) to model the effect of 15 weeks of resistance training on changes in countermovement jump (CMJ) performance in male track-and-field athletes. Methods: Resistance training volume load (VL) of 21 male division I track-and-field athletes was monitored over the course of 15 weeks, which covered their indoor and outdoor competitive season. Weekly CMJ height was also measured and used to calculate the overall 15-week change in CMJ performance. A feed-forward ANN with 5 hidden layers was used to model how the VL from each of the 15 weeks was associated with the overall change in CMJ height. Results: Testing the performance of the developed ANN on 4 separate athletes showed that 15 weeks of VL data could predict individual changes in CMJ height with an average error between 0.21 and 1.47 cm, which suggested that the ANN adequately modeled the relationship between weekly VL and its effects on CMJ performance. In addition, analysis of the relative importance of each week in predicting changes in CMJ height indicated that the VLs during deload or taper weeks were the best predictors (10%–17%) of changes in CMJ performance. Conclusions: ANN can be used to effectively model the effects of weekly VL on changes in CMJ performance. In addition, ANN can be used to assess the relative importance of each week in predicting changes in CMJ height.

Kipp and Krzyszkowski are with Program in Exercise Science, Dept of Physical Therapy, Marquette University, Milwaukee, WI, USA. Kant-Hull is with the Dept of Intercollegiate Athletics, Marquette University, Milwaukee, WI, USA.

Kipp (kristof.kipp@marquette.edu) is corresponding author.
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