Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Journal of sports sciences ; h5-index 52.0

The purpose of this study was to use regularised regression models to identify the most important biomechanical predictors of throwing distance in elite male (M) and female (F) javelin throwers at the 2017 IAAF world championships. Biomechanical data from 13 male and 12 female javelin throwers who competed at the 2017 IAAF world championships were obtained from an official scientific IAAF report. Regularised regression models were used to investigate the associations between throwing distance and release parameters, whole-body kinematic and joint-level kinematic data. The regularised regression models identified two biomechanical predictors of throwing distances in both M and F javelin throwers: release velocity and knee flexion angle of the support leg at the moment of javelin release. In addition, the length of the delivery stride was an important predictor of throwing distance in M throwers, whereas the javelin's attitude angle and the distance between the whole-body centre of mass and the centre of mass of the back foot at the beginning of the delivery phase were important predictors of throwing distance in F throwers.

Krzyszkowski John, Kipp Kristof

2021-Jan-11

Biomechanics, LASSO, machine learning, regularised regression, sports