# Training to Compete: Are Basketball Training Loads Similar to Competition Achieved?
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Training (\(n\) = 221)**} & \multicolumn{3}{c}{**Competition (\(n\) = 32)**} & & & & \\  & & \(\mathbf{X}\) & \(\mathbf{SD}\) & \(\mathbf{Median}\) & \(\mathbf{X}\) & \(\mathbf{SD}\) & \(\mathbf{Median}\) & \(\mathbf{U}\) & \(\mathbf{z}\) & \(\mathbf{p}\) & \(\mathbf{r}\) \\ \hline \multirow{10}{*}{eTL cine} & DIST/MIN & 38.77 & 8.24 & 38.20 & 61.70 & 8.47 & 64.15 & 6873.00 & \(-\)8.390 & \(<\)0.01 & 53 \\  & HIA/MIN & 3.50 & 0.92 & 3.40 & 6.33 & 1.30 & 6.46 & 6800.00 & \(-\)8.438 & \(<\)0.01 & 0.53 \\  & ACC + 2/MIN & 1.58 & 0.44 & 1.55 & 2.87 & 1.30 & 2.85 & 6837.00 & \(-\)8.533 & \(<\)0.01 & 0.54 \\  & DEC + 2/MIN & 1.44 & 0.44 & 1.37 & 2.52 & 1.53 & 2.33 & 6541.00 & \(-\)7.767 & \(<\)0.01 & 0.49 \\  & ACC-DEC/MIN & 3.02 & 0.86 & 2.92 & 4.97 & 0.88 & 4.98 & 6733.00 & \(-\)8.263 & \(<\)0.01 & 0.52 \\  & J + 3G/MIN & 0.11 & 0.08 & 0.09 & 0.14 & 0.11 & 0.12 & 4215.50 & \(-\)1.759 & 0.08 & 0.11 \\  & L + 5/MIN & 0.13 & 0.11 & 0.12 & 0.18 & 0.12 & 0.14 & 4459.00 & \(-\)2.388 & 0.02 & 0.15 \\  & IMP + 8/MIN & 0.08 & 0.08 & 0.06 & 1.00 & 0.62 & 0.86 & 6907.00 & \(-\)8.730 & \(<\)0.01 & 0.55 \\  & PL/MIN & 0.66 & 0.15 & 0.64 & 1.10 & 0.20 & 1.12 & 6698.00 & \(-\)8.175 & \(<\)0.01 & 0.51 \\ iTL & HR\({}_{\text{AVG}}\)/MIN & 129 & 13 & 130 & 145 & 33 & 153 & 6112.50 & \(-\)5.767 & \(<\)0.01 & 0.36 \\  & HR\({}_{\text{MAX}}\)/MIN & 177 & 13 & 178 & 178 & 35 & 186 & 4582.50 & \(-\)3.046 & \(<\)0.01 & 0.19 \\ \hline \hline \end{tabular} Note: \(\mathbf{X}\) = mean; SD = standard deviation; U = Mann–Whitney U test; z = z-score; r = Rosenthal’s r; eTL kin = kinematic external load; eTL neu = neuromuscular external load; iTL = internal load.  
\end{table} 
Table 4: Inferential analysis of demand variables by context. Figure 2: Comparison of demand variables based on context. Note: eTL kin = kinematic external load; iTL = internal load. \({}^{*}\)\(p<0.05\); \({}^{**}\)\(p<0.01\). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable of Intensity**} & \multirow{2}{*}{\begin{tabular}{c} **Context** \\ **Q** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Position** \\ **P** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Context * Position** \\ **Q** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **_P_** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **_Q_** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **_P_** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **_Q_** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **_P_** \\ \end{tabular} } \\ \hline DIST/MIN & 306.657 & \textless{}0.01 ** & 0.772 & 0.70 & 3.725 & 0.21 \\ HIA/MIN & 97.280 & \textless{}0.01 ** & 0.973 & 0.66 & 0.784 & 0.71 \\ ACC + 2/MIN & 86.501 & \textless{}0.01 ** & 0.729 & 0.73 & 0.409 & 0.84 \\ DEC + 2/MIN & 125.69 & \textless{}0.01 ** & 7.97 & 0.06 & 2.96 & 0.30 \\ ACC-DEC/MIN & 110.0542 & \textless{}0.01 ** & 1.7206 & 0.49 & 0.0947 & 0.96 \\ J + 3G/MIN & 1.660 & 0.22 & 11.282 & 0.02 ** & 0.159 & 0.93 \\ L + 5/MIN & 5.47 & 0.03 * & 8.53 & 0.04 ** & 1.35 & 0.545 \\ IMP + 8/MIN & 52.40 & \textless{}0.01 ** & 3.12 & 0.28 & 3.58 & 0.24 \\ PH/MIN & 169.717 & \textless{}0.01 ** & 0.623 & 0.76 & 1.921 & 0.45 \\ HR\({}_{\text{NOV}}\)/MIN & 39.559 & \textless{}0.01 ** & 0.546 & 0.78 & 4.729 & 0.17 \\ HR\({}_{\text{MAX}}\)/MIN & 6.144 & 0.03 * & 0.631 & 0.75 & 0.684 & 0.73 \\ \hline \hline \end{tabular} Note: Method of trimmed means (level 0). \({}^{*}p<0.05\); \({}^{**}p<0.01\).  
\end{table} 
Table 6: Differences in demand variables according to the context-specific position interaction. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable of Intensity**} & \multirow{2}{*}{ \begin{tabular}{c} **Specific** \\ **Position** \\ \end{tabular}