# Optimizing Field Body Fat Percentage Assessment in Professional Soccer Players
## 3 Results  
The characteristics of the participants and descriptive values of %BF estimated from DXA, BIA, SKF-based equations and absolute SKFs are shown in Table 1. Taking DXA as reference, all equations, SKFs and BIA measurements underestimated %BF. For all SKFs, the technical errors of measurements were lower than 3.60%. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Mean** & **SD** & **Minimum** & **Maximum** \\ \hline Age (years) & 26.3 & 3.7 & 22.2 & 35.5 \\ Weight (kg) & 77.4 & 6.1 & 64.9 & 89.2 \\ Height (cm) & 181.2 & 6.7 & 169.0 & 193.0 \\ BMI (kg/m\({}^{2}\)) & 23.6 & 1.2 & 21.0 & 25.6 \\ SKF (mm): & & & & \\ Triceps & 5.7 & 1.9 & 3.0 & 10.50 \\ Subscapular & 8.1 & 2.0 & 6.0 & 15.0 \\ Biceps & 2.5 & 0.8 & 1.5 & 4.5 \\ Iliac crest & 9.6 & 2.8 & 6.5 & 17.0 \\ Supraspinale & 5.2 & 1.7 & 3.5 & 9.0 \\ Abdominal & 11.5 & 5.0 & 5.5 & 26.5 \\ Calf & _7.7_ & 3.0 & 5.0 & 16.5 \\ Thigh & 3.9 & 1.1 & 2.0 & 6.5 \\ DXA (\%BF) & 15.3 & 2.0 & 11.9 & 18.9 \\ BIA (\%BF) & 13.0 & 2.5 & 7.6 & 17.5 \\ EQUATIONS (\%BF) & & & & \\ [PERSON] (1982) & 7.0 & 1.3 & 5.4 & 10.5 \\ [PERSON] et al. (2006) & 10.6 & 2.1 & 8.1 & 15.6 \\ \hline \end{tabular}
\end{table} 
Table 1: Subject characteristics and body fat percentages estimated using the methods DXA, BIA, anthropometric equations and absolute SKFs in elite soccer players (\(n\) = 21). Correlations, biases, limits of agreement and systematic, standardized, and qualitative differences between DXA %BF and other estimates of %BF for elite male soccer players are shown in Table 2. All equation-derived estimates correlated very well with the DXA results (r = 0.72 to 0.84; all \(p<0.05\)). However, BIA %BF data showed only moderate positive correlation (r = 0.42; \(p<0.05\)). Further, only the equation of [PERSON] et al. (2018), which showed no significant or standardized differences in relation to DXA, did not display practically worthwhile differences or substantial differences with the other methods and had the lowest bias. The rest of the equations and BIA data showed high biases. All SKF measurements except for subscapular SKF showed significant good to very good positive correlations with DXA-derived %BF (r = 0.50 to 0.81; all \(p<0.05\)) (Table 3). All sums of SKFs showed significant large positive correlation with DXA-derived %BF (r = 0.78 to 0.82; all \(p<0.001\)) (Table 3). \begin{table}
\begin{tabular}{c c c c c} \hline  & **Mean** & **SD** & **Minimum** & **Maximum** \\ \hline [PERSON] et al. (2005) & 8.8 & 2.3 & 6.2 & 14.3 \\ [PERSON] (1966) & 10.4 & 1.4 & 8.7 & 13.8 \\ [PERSON] and [PERSON] (1973) & 10.6 & 3.6 & 5.8 & 19.6 \\ [PERSON] et al. (2018) & 9.2 & 1.7 & 7.3 & 13.4 \\ [PERSON] and [PERSON] (2000) & 7.8 & 3.2 & 3.7 & 16.4 \\ [PERSON] et al. (2018) & 15.0 & 2.0 & 12.7 & 19.9 \\ [PERSON] et al. (1980) & 7.2 & 1.6 & 5.5 & 11.4 \\ [PERSON] et al. (1987) & 7.9 & 2.3 & 5.1 & 14.0 \\ [PERSON] et al. (2005) & 8.9 & 2.1 & 6.5 & 15.0 \\ [PERSON] and [PERSON] (1967) & 11.6 & 2.6 & 7.9 & 17.8 \\ [PERSON] and [PERSON] (1974) & 11.4 & 2.7 & 7.7 & 16.8 \\ [PERSON] et al. (1996) & 10.7 & 2.7 & 6.4 & 16.3 \\ [PERSON] (1981) & 11.9 & 4.4 & 6.8 & 22.7 \\ [PERSON] et al. (2003) & 14.5 & 2.4 & 11.4 & 19.6 \\ [PERSON] and [PERSON] (1969) & 11.7 & 2.3 & 8.9 & 18.5 \\ \hline \end{tabular} BIA (bioelectric impedance analysis), BMI (body mass index), DXA (dual-energy X-ray absorptiometry).  
\end{table} 
Table 1: _Cont._