title: Standardization and validation of the body weight adjustment regression equations in Olympic weightlifting.
abstract: The problems in comparing the performances of Olympic weightlifters arise from the fact that the relationship between body weight and weightlifting results is not linear. In the present study, this relationship was examined by using a nonparametric curve fitting technique of robust locally weighted regression (LOWESS) on relatively large data sets of the weightlifting results made in top international competitions. Power function formulas were derived from the fitted LOWESS values to represent the relationship between the 2 variables in a way that directly compares the snatch, clean-and-jerk, and total weightlifting results of a given athlete with those of the world-class weightlifters (golden standards). A residual analysis of several other parametric models derived from the initial results showed that they all experience inconsistencies, yielding either underestimation or overestimation of certain body weights. In addition, the existing handicapping formulas commonly used in normalizing the performances of Olympic weightlifters did not yield satisfactory results when applied to the present data. It was concluded that the devised formulas may provide objective means for the evaluation of the performances of male weightlifters, regardless of their body weights, ages, or performance levels.
authors.affiliation: LIKES-Research Center for Sport and Health Sciences, Jyväskylä, Finland. heikki.kauhanen@likes.fi.
authors.forename: Heikki
authors.initials: H
authors.lastname: Kauhanen
authors.forename: Paavo V
authors.initials: PV
authors.lastname: Komi
authors.forename: Keijo
authors.initials: K
authors.lastname: Häkkinen
mesh_terms: D001835:Body Weight; D006801:Humans; D008297:Male; D008954:Models, Biological; D012015:Reference Standards; D012044:Regression Analysis; D015203:Reproducibility of Results; D018709:Statistics, Nonparametric; D014891:Weight Lifting / Q000592:standards* / Q000706:statistics & numerical data