A common practice in general linear model (GLM) analyses is to interpret regression coefficients (e.g., standardized ß weights) as indicators of variable importance. However, focusing solely on standardized beta weights may provide limited or erroneous information. For example, ß weights become increasingly unreliable when predictor variables are correlated, which is often the case in the social sciences. To address this issue, structure coefficients, which are simply the bivariate correlation between a predictor and the synthetic Y variable, should also be interpreted. By examining ß weights and structure coefficients in conjunction, the predictive worth of each independent variable can be more accurately judged. Despite this benefit, researchers in the field of sport and exercise science have rarely reported structure coefficients when conducting multiple regression analysis. Thus, the purpose of the present article is to discuss problems associated with the sole interpretation of ß weights and to demonstrate how structure coefficients can be incorporated to improve accuracy of interpretation. Additionally, a content analysis was conducted to examine current trends in reporting multiple regression results within sport and exercise science research.