# Revolutionizing Soccer Injury Management: Predicting Muscle Injury Recovery Time Using ML
## 3 Results
### Joint Performance of Expert and ML
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Iteration**} & \multicolumn{5}{c}{**LOO Scores**} & \multicolumn{5}{c}{**CV Scores**} \\ \hline  & & \(\mathbf{R^{2}}\) & **MAPE** & **MAE** & **MSE** & **RMSE** & \(\mathbf{\mu}\) **MSE** & \(\mathbf{\sigma}\) **MSE** \\ \hline \multirow{8}{*}{**2015**} & **1** & **0.6355** & **0.40236** & **3.47055** & **22.51088** & **4.74456** & **24.18168** & **11.73257** \\  & 2 & 0.6355 & 0.40236 & 3.47055 & 22.51088 & 4.74456 & 24.18168 & 11.73257 \\  & 3 & 0.6355 & 0.40236 & 3.47055 & 22.51088 & 4.74456 & 24.18168 & 11.73257 \\  & 4 & 0.6355 & 0.40236 & 3.47055 & 22.51088 & 4.74456 & 24.18168 & 11.73257 \\  & 5 & 0.6355 & 0.40236 & 3.47055 & 22.51088 & 4.74456 & 24.18168 & 11.73257 \\  & 6 & 0.6355 & 0.40236 & 3.47055 & 22.51088 & 4.74456 & 24.18168 & 11.73257 \\  & 7 & 0.6355 & 0.40236 & 3.47055 & 22.51088 & 4.74456 & 24.18168 & 11.73257 \\  & 8 & 0.6355 & 0.40236 & 3.47055 & 22.51088 & 4.74456 & 24.18168 & 11.73257 \\  & 9 & 0.6355 & 0.40236 & 3.47055 & 22.51088 & 4.74456 & 24.18168 & 11.73257 \\  & 10 & 0.6355 & 0.40236 & 3.47055 & 22.51088 & 4.74456 & 24.18168 & 11.73257 \\ \hline \multirow{8}{*}{**2015**} & \(\mu\) & 0.6355 & 0.40236 & 3.47055 & 22.51088 & 4.74456 & 24.18168 & 11.73257 \\  & \(\sigma\) & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ \hline \multirow{8}{*}{**2015**} & **R2** & **MAPE** & **MAE** & **MSE** & **RMSE** & \(\mathbf{\mu}\) **MSE** & \(\mathbf{\sigma}\) **MSE** \\ \cline{2-9}  & 1 & 0.56135 & 0.43025 & 4.06224 & 27.09026 & 5.20483 & 24.23774 & 8.00767 \\  & 2 & 0.56588 & 0.40654 & 4.07746 & 26.8105 & 5.17789 & 24.58617 & 9.96156 \\  & **3** & **0.65213** & **0.38963** & **3.55799** & **21.4841** & **4.63509** & **23.13874** & **12.39801** \\  & 4 & 0.48511 & 0.41564 & 4.13162 & 31.79884 & 5.63905 & 24.88780 & 10.97593 \\  & 5 & 0.56682 & 0.40528 & 4.06732 & 26.75267 & 5.1723 & 24.58617 & 9.96156 \\  & 6 & 0.63713 & 0.41954 & 3.74522 & 22.41051 & 4.73397 & 29.09281 & 12.13646 \\  & 7 & 0.55367 & 0.4334 & 3.98921 & 27.56484 & 25.25022 & 72.33906 & 12.30718 \\  & 8 & 0.55991 & 0.43168 & 4.07508 & 27.17943 & 5.21339 & 25.46586 & 8.81431 \\  & 9 & 0.65213 & 0.38963 & 3.55799 & 21.4841 & 4.63509 & 23.13874 & 12.39801 \\  & 10 & 0.53225 & 0.40429 & 4.1087 & 28.88768 & 5.37473 & 24.83022 & 5.37688 \\ \hline \multirow{8}{*}{**2015**} & \(\mu\) & 0.57664 & 0.41259 & 3.93728 & 26.14629 & 5.10366 & 25.12396 & 10.23376 \\  & \(\sigma\) & 0.05442 & 0.01626 & 0.22746 & 3.36112 & 0.33164 & 1.8156 & 2.3225 \\ \cline{1-1} \cline{2-9}  & \(\mathbf{R^{2}}\) & **MAPE** & **MAE** & **MSE** & **RMSE** & \(\mathbf{\mu}\) **MSE** & \(\mathbf{\sigma}\) **MSE** \\ \hline \multirow{8}{*}{**2015**} & **1** & 0.68989 & 0.33421 & 3.36068 & 19.15167 & 4.37626 & 16.69036 & 3.66068 \\  & 2 & 0.73061 & 0.31059 & 3.11775 & 16.63706 & 4.07886 & 15.77372 & 4.0823 \\  & 3 & 0.71298 & 0.30976 & 3.16809 & 17.72571 & 4.21019 & 16.12723 & 3.91683 \\  & **4** & **0.72953** & **0.32833** & **3.27812** & **16.70385** & **4.08703** & **15.03208** & **3.47678** \\  & 5 & 0.71938 & 0.29577 & 3.07532 & 17.33056 & 4.163 & 16.66 & 5.41802 \\  & 6 & 0.74768 & 0.30955 & 3.07108 & 15.58318 & 3.94755 & 15.51867 & 4.03146 \\  & 7 & 0.70573 & 0.32659 & 3.2798 & 18.17383 & 4.26308 & 15.43757 & 3.808289 \\  & 8 & 0.74478 & 0.32052 & 3.06184 & 15.76225 & 3.97017 & 15.33776 & 5.10925 \\  & 9 & 0.72551 & 0.32377 & 3.19032 & 16.95234 & 4.11732 & 15.88516 & 3.67151 \\  & 10 & 0.7425 & 0.3084 & 3.08914 & 15.90284 & 3.98784 & 15.33761 & 4.20763 \\ \hline \multirow{8}{*}{**2015**} & \(\mu\) & 0.72486 & 0.31495 & 3.16318 & 16.99233 & 4.12013 & 15.78002 & 4.13828 \\  & \(\sigma\) & 0.01839 & 0.01249 & 0.10021 & 1.13567 & 0.13686 & 0.56481 & 0.63608 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of LR, DT and XGB models during Bayesian CV search and LOO evaluation, however, this time with the inclusion of the expert’s estimation as a feature.