Importance of weightlifting performance analysis in anti-doping

Abstract

We examined the potential roles of the athlete's performance passport (APP) for doping detection by analyzing the relationship between weightlifting performance and sanction status. For the present study, performance data of 'not-sanctioned' (26740 datasets) and 'sanctioned' (289 datasets) male athletes were acquired from the website of the International Weightlifting Federation (www.iwf.net). One-way ANOVA, correlation analysis, and t-tests were used to analyze the relationship between athletes' use of doping and their performances across age and body weight. Athletic performance was significantly greater for athletes in the sanctioned group than those of the same age group who were not sanctioned, and this performance difference between the two groups was the greatest in their late thirties at 20.6% (not-sanctioned 292.0kg vs. sanctioned 352.3kg) (p < 0.05). From the age group analysis, out of 289 sanctioned cases, 84 cases, which was the largest proportion, were found within the top 10-25% of their performances. When stratified by body weight, athletic performance was significantly greater for the sanctioned group than the not-sanctioned group, and this performance gap was the greatest in the bodyweight category of 96 at 18.6% (not-sanctioned 310.1kg vs. sanctioned 367.8kg) (p < 0.05). From the body weight category analysis, out of 289 sanctioned cases, 75 cases, which was the largest proportion, were found within the top 10-25% of their performances. Additionally, the mean difference in performance between not-sanctioned and sanctioned groups was the largest in the body weight category of 67kg in the ages of 15-19 at 20% (not-sanctioned 234.6kg vs. sanctioned 281.5kg). These results are interpreted to mean that in male weightlifters 1) sanctioned athletes were detected in all ranges of performances regardless of age and body weight, 2) there were even higher rates of sanctioned athletes who performed within the top 10-25% of each age group and body weight category, 3) there were significant differences in performance between not-sanctioned and sanctioned group for all body weight categories, excluding +109, in the ages of 15-19 and 20-24, 4) therefore, performance data can be effectively used to better target suspected athletes for doping testing.

Fig 1. Experimental design of the study.

Fig 2. Male weightlifters’ performance in total weight lifted (kg) across age (26,740 results of not-sanctioned, 289 results of sanctioned).

(A) Scatter plot showing the overall performance distribution for all athletes. Gray open circles represent results of not-sanctioned athletes, and black asterisks represent results of sanctioned athletes. (B) Age group analysis of performance (total weight lifted, kg) by age group for male weightlifters. Solid line with open circles represents performance of the top athlete, solid line with filled circles represents athletes in the top 1%, dashed line with filled circles represents athletes in the top 5%, solid line with open triangles represents athletes in the top 10%, solid line with filled triangles represents athletes in the top 25%, dashed line with filled triangles represents athletes in the top 50%, solid line with open squares represents athletes in the top 75%, and solid line with filled squares represents athletes in the top 90%. Blue X’s represent results of sanctioned athletes. (C) Line graph showing the trend of performance change. Solid line represents the mean of not-sanctioned athletes and dashed line represents the mean of sanctioned athletes, and shaded area indicates standard error.

Fig 3. Male weightlifters’ performance in total weight lifted (kg) across body weight (26,740 results of not-sanctioned, 289 results of sanctioned).

(A) Scatter plot showing the overall performance distribution for all athletes. Gray open circles represent results of not-sanctioned athletes, and black asterisks represent results of sanctioned athletes. (B) Body weight category analysis of performance (total weight lifted, kg) for male weightlifters. Solid line with open circles represents performance of the top athlete, solid line with filled circles represents athletes in the top 1%, dashed line with filled circles represents athletes in the top 5%, solid line with open triangles represents athletes in the top 10%, solid line with filled triangles represents athletes in the top 25%, dashed line with filled triangles represents athletes in the top 50%, solid line with open squares represents athletes in the top 75%, and solid line with filled squares represents athletes in the top 90%. Blue X’s represent results of sanctioned athletes. (C) Box plots of the average performance and outliers of each bodyweight category grouped by sanction status (not-sanctioned or sanctioned) for comparison. Boxes indicate the 25^th and 75^th percentiles. Whiskers indicate 10^th and 90^th percentiles with the middle horizontal line representing the mean. The outliers are indicated by dots.

Fig 4

Male weightlifters’ performance in total weight lifted (kg) for each body weight category in the ages of 15–19 (A), 20–24 (B), 25–29 (C), 30–34 (D), 35–39 (E), and 40–44 (F) (26,740 results of not-sanctioned, 289 results of sanctioned). Box plots of the average performance and outliers of each body weight category are grouped by sanction status (not-sanctioned or sanctioned) for comparison. Boxes indicate the 25^th and 75^th percentiles. Whiskers indicate 10^th and 90^th percentiles, with the middle horizontal line representing the mean. Dots indicate the outliers.