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

John Krzyszkowski,Kristof Kipp,Daniel Kant-Hull

doi:10.1123/ijspp.2019-0009

John Krzyszkowski, Kristof Kipp + Show 1 more

Open Access

https://doi.org/10.1123/ijspp.2019-0009

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Abstract

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. 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. 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 1.47cm, 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. 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.

Highlights

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
It was hypothesized that the ANN would be able to effectively model the association between training load and changes in CMJ height and be able to identify the relative importance of specific training weeks on the CMJ changes
The relative importance of the weekly training volume in predicting CMJ height change ranged from 1% to 17% (Figure 2)

Summary

Introduction

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. As optimizing neuromuscular adaptations depends on appropriate prescription and progression of training loads, several models have been used to probe and elucidate the association between training loads and performance outcomes.[6,7] Traditionally, work in the field of load monitoring has used a systems-model approach, which aims to facilitate our understanding of how information about the training process can be used to predict an athlete’s readiness and potential for performance.[6,7] More recently, researchers have used artificial neural networks (ANNs) for the same purposes.[8,9,10] For example, ANNs were used to successfully predict swimming performance from 4 weeks of training load data, which included weekly training volume for swim-related activities, resistance exercise, and dryland training.[8]. It was hypothesized that the ANN would be able to effectively model the association between training load and changes in CMJ height and be able to identify the relative importance of specific training weeks on the CMJ changes

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