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Predicting Bobsled Pushing Ability From Various Combine Testing Events
Curtis L Tomasevicz1,2, Jack W Ransone2, Christopher W Bach2
1Department of Biological Systems Engineering, University of Nebraska, Lincoln, Nebraska; and.
Abstract:
Tomasevicz, CL, Ransone, JW, and Bach, CW. Predicting bobsled pushing ability from various combine testing events. J Strength Cond Res 34(9): 2618-2626, 2020-The requisite combination of speed, power, and strength necessary for a bobsled push athlete coupled with the difficulty in directly measuring pushing ability makes selecting effective push crews challenging. Current practices by USA Bobsled and Skeleton use field combine testing to assess and identify specifically selected performance variables in an attempt to best predict push performance abilities. Combine data consisting of 11 physical performance variables were collected from 75 subjects across 2 winter Olympic qualification years (2009 and 2013). These variables were sprints of 15, 30, and 60 m, a flying 30-m sprint, a standing broad jump, a shot toss, squat, power clean, body mass, and dry-land brake and side bobsled pushes. Discriminant analysis (DA) in addition to principle component analysis (PCA) was used to investigate 2 cases (case 1: Olympians vs. non-Olympians; case 2: National Team vs. non-National Team). Using these 11 variables, DA led to a classification rule that proved capable of identifying Olympians from non-Olympians and National Team members from non-National Team members with 9.33 and 14.67% misclassification rates, respectively. The PCA was used to find similar test variables within the combine that provided redundant or useless data. After eliminating the unnecessary variables, DA on the new combinations showed that 8 (case 1) and 20 (case 2) other combinations with fewer performance variables yielded misclassification rates as low as 6.67 and 13.33%, respectively. Using fewer performance variables can allow governing bodies in many other sports to create more appropriate combine testing that maximize accuracy while minimizing irrelevant and redundant strategies.
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