# Predicting Soccer Player Salaries with Both Traditional and Automated Machine Learning Approaches
## 5 Comparative Evaluation
### Traditional ML vs. AutoML: Performance Comparison  
Table 7 presents a comparison between traditional ML and AutoML approaches, before and after hyperparameter tuning, highlighting the relative increases and decreases in \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multicolumn{1}{c}{**Model**} & \multicolumn{1}{c}{**Approach**} & \multicolumn{1}{c}{**Tuning**} & \multicolumn{1}{c}{**R\({}^{2}\)**} & \multicolumn{1}{c}{**MAE (GBP)**} & \multicolumn{1}{c}{**MSE (GBP)**} & \multicolumn{1}{c}{
\begin{tabular}{c} **RMSE** \\ **(GBP)** \\ \end{tabular} } \\ \hline \multirow{4}{*}{XGBoost (XGB)} & Traditional & Pre & 0.65 & \(9.96\times 10^{5}\) & \(3.49\times 10^{12}\) & \(1.87\times 10^{6}\) \\  & Traditional & Post & 0.83 & \(9.95\times 10^{5}\) & \(3.66\times 10^{12}\) & \(1.91\times 10^{6}\) \\  & AutoML & Pre & 0.69 & \(9.55\times 10^{5}\) & \(2.94\times 10^{12}\) & \(1.72\times 10^{6}\) \\  & AutoML & Post & 0.60 & \(9.84\times 10^{5}\) & \(3.18\times 10^{12}\) & \(1.78\times 10^{6}\) \\ \hline \multirow{4}{*}{Gradient Boosting (GB)} & Traditional & Pre & 0.56 & \(1.17\times 10^{6}\) & \(4.41\times 10^{12}\) & \(2.10\times 10^{6}\) \\  & Traditional & Post & 0.91 & \(1.28\times 10^{4}\) & \(6.50\times 10^{8}\) & \(2.55\times 10^{4}\) \\  & AutoML & Pre & 0.46 & \(1.15\times 10^{6}\) & \(4.21\times 10^{12}\) & \(2.05\times 10^{6}\) \\  & AutoML & Post & 0.46 & \(1.16\times 10^{6}\) & \(4.27\times 10^{12}\) & \(2.07\times 10^{6}\) \\ \hline \multirow{4}{*}{Decision Tree (DT)} & Traditional & Pre & 0.47 & \(1.01\times 10^{6}\) & \(5.33\times 10^{12}\) & \(2.31\times 10^{6}\) \\  & Traditional & Post & 0.49 & \(1.13\times 10^{6}\) & \(5.10\times 10^{12}\) & \(2.26\times 10^{6}\) \\  & AutoML & Pre & 0.35 & \(9.90\times 10^{5}\) & \(5.13\times 10^{12}\) & \(2.25\times 10^{6}\) \\  & AutoML & Post & 0.31 & \(1.03\times 10^{6}\) & \(5.42\times 10^{12}\) & \(2.33\times 10^{6}\) \\ \hline \multirow{2}{*}{Linear Regression (LR)} & Traditional & Pre & 0.28 & \(1.37\times 10^{6}\) & \(7.24\times 10^{12}\) & \(2.69\times 10^{6}\) \\  & AutoML & Pre & 0.20 & \(1.39\times 10^{6}\) & \(6.32\times 10^{12}\) & \(2.50\times 10^{6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison of traditional ML and AutoML models (pre vs. post tuning). \begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **Hyperparameter** & **Optimized Value** \\ \hline \multirow{4}{*}{Decision Tree} & max\_depth & 15 \\  & min\_samples\_leaf & 6 \\  & min\_samples\_split & 15 \\ \hline \multirow{4}{*}{XGBoost} & subsample & 1.0 \\  & n\_estimators & 100 \\  & max\_depth & 9 \\  & learning\_rate & 0.05 \\  & colsample\_bytes & 0.6 \\ \hline \multirow{4}{*}{Gradient Boosting} & n\_estimators & 200 \\  & min\_samples\_split & 15 \\ \cline{1-1}  & min\_samples\_leaf & 2 \\ \cline{1-1}  & max\_depth & 9 \\ \cline{1-1}  & learning\_rate & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Hyperparameter optimization results for different models. predictive performance metrics. Traditional models show substantially greater gains in \(R^{2}\), with GB showing a 97.8% relative increase, followed by DT (58.1%) and XGB (38.3%). In contrast, the LR model shows a 28.6% lower R\({}^{2}\) under the AutoML approach compared to the traditional approach, as no hyperparameter tuning is applied, reflecting baseline differences rather than tuning effects and some AutoML models exhibit minimal gains or declines in \(R^{2}\) after tuning. GB also outperforms AutoML's XGB in MAE and RMSE, indicating greater accuracy and robustness in limiting large prediction errors. This demonstrates the advantage of expert-guided hyperparameter tuning and customization in traditional ML, which better captures complex, domain-specific relationships in soccer salary data. Although AutoML aims to automate model selection and tuning, our results show that traditional ML models outperform AutoML in this context. Several factors may explain this outcome.