Gillett, J, De Witt, J, Stahl, CA, Martinez, D, and Dawes, JJ. Descriptive and kinetic analysis of two different vertical jump tests among youth and adolescent male basketball athletes using a supervised machine learning approach. J Strength Cond Res 35(10): 2762-2768, 2021-The countermovement jump (CMJ) is a functional movement in basketball and is also frequently used as an assessment of lower-body power. The CMJ can be performed in a variety of manners, and multiple variables can be extracted, and calculated, from the ground reaction force (GRF) time curve. The purpose of this article is to present kinematic and kinetic data collected from adolescent male basketball players during performance of the CMJ with hands on hips (HOH) or with an arm swing while reaching overhead to a target (i.e., vertical jump reach [VJR]). This study also sought to determine the effectiveness of a machine learning algorithm to identify the most important features that relate to jump height. Bilateral GRF data were collected on 89 right-handed male basketball athletes (age: 13.19 +/- 0.72 year old, mass: 60.44 +/- 13.35 kg, standing reach height: 228.49 +/- 16.79 cm) using force platforms (Forcedecks, Vald Performance, Newstead, Queensland, Australia) and their associated software. Fifty-six bilateral kinematic and kinetic variables from each condition were analyzed using supervised machine learning to identify the top 10 important features to predict jump height in each condition, and to predict VJR height using HOH data. Vertical center of mass flight height was greater during VJR trials than during HOH trials (38.9 +/- 6.8 cm vs. 32.6 +/- 5.5 cm, respectively). The only common predictor variables between the conditions were concentric impulse and peak power. HOH jump data were able to predict VJR height with a mean error of 7.13 cm. These data suggest that important force platform data relating to jump height differ depending on test condition, and that data from CMJ performed with HOH, particularly peak power, concentric impulse, and concentric rate of power development, can be used to predict jump height during functional performance.
