Applications of regularized regression models in sports biomechanics research

Abstract

ABSTRACT Research in sports biomechanics often relies on the use of ordinary least squares (OLS) regression. However, since sports biomechanics research is often characterised by high-dimensional data sets with many predictor variables and few observations, use of OLS regression can sometimes be problematic from a statistical perspective. Statistical learning methods may provide alternate ways to deal with high-dimensional data sets and partially address these problems. For example, regularisation adds penalties to the cost function of OLS regression models, which shrinks large regression coefficients and decreases the model’s sensitivity to noise in the data. Regularised regression models also protect against overfitting, improve generalisability, and can be used for variable selection. A short review of biomechanics research studies illustrates how these models provided ways to reduce the number of variables within a model and select only the primary predictors of performance, which helped with the interpretation of results and identified distinct combinations of key predictors of performance. In addition, we illustrate how these models are applied to two sports biomechanics datasets. Given the advantages, sports biomechanists may want to consider the use of regularised regression models in their research design and statistical analyses. Careful consideration should be given, however, to the construction, validation, and interpretation of these models considering their underpinning assumptions and limitations.