Each athlete performed at least three full-effort sprint starts over a distance of 20 m. The best start (based on NAHBP) was selected for further analysis. To identify potential latent factors affecting SP, we performed an exploratory factor analysis (EFA) using selected force parameters. EFA is a statistical procedure used to analyze variability among measured correlated variables with respect to a potentially lower number of unobserved (unmeasured) or latent variables, which are termed factors. For example, it could be the case that variations in a great number of observed sprint-start kinetic parameters are actually just the result of variability in a much smaller group of underlying parameters (factors) that represent more fundamental sprint-start abilities. EFA is aimed at finding measured parameters that vary as a group in response to latent variables. In our case, the dataset representing the observed variables included average and peak forces in all directions and parameters describing the push-off direction (RHRI, RMLRI). Using Matlab’s (R2015b; Mathworks, Natick, MA, USA) built-in “factoran” function, we calculated the maximum likelihood estimate (MLE) of the factor loadings matrix Λ in the factor-analysis model, x=μ+Λf+e ; where x is a vector of observed force parameters, μ is a constant vector of means, Λ is a matrix of factor loadings, f is a vector of independent, standardized common factors, and e is a vector of independent specific factors. To identify the number of factors to extract for further analysis, the Kaiser criterion  and the scree test  were applied. In a subsequent step, factor loadings and scores were rotated using the “varimax” method  in order to improve interpretability. To identify the relationship between these latent factors and SP (NAHBP), multiple linear regression was performed with latent factor scores as the predictors and the NAHBP as dependent variable in an approach similar to Basilevsky . Using forward selection, models including intercept and quadratic terms were fitted using Matlab’s “fitlm” function. The best model was determined by the Akaike information criterion . Further regression analyses were performed to identify the relationships between force-application parameters, SP and 100 m PRs. For the identification of differences between ASs and NASs, two different approaches were taken. In the first, ASs were matched to NASs with similar absolute PRs, and in the second, athletes were matched with respect to their relative PRs (relative to the current world record in their particular starting class, based on International Paralympic Committee classification rules). The matching procedure was aimed at finding the three closest absolute or relative 100 m PRs for each athlete. If a NAS matched with more than one amputee athlete, s/he was only included once. Comparison between ASs and matching NASs were performed using independent-samples t tests. To address potential problems due to unequal variances, Satterthwaite's approximation for the effective degrees of freedom was used [20, 21]. The significance level, α, was set at 0.05. Due to the low size in the AS sample and related statistical power, we also calculated effect sizes (Cohens d) in order to allow for an estimation of the strength of an observed difference . Effect sizes greater than 0.2 were considered small, greater 0.5 medium and greater than 0.8 large .