# Predicting Soccer Player Salaries with Both Traditional and Automated Machine Learning Approaches
## 6 Evaluation Results  
### SHAP-Based Feature Importance Analysis  
To improve interpretability and performance, (SHAP) analysis was employed for feature selection. SHAP assigns importance values to features based on their contribution to model predictions, ensuring consistent and locally accurate attributions [45; 46]. We applied SHAP to four models: LR, DT, GB, and XGB, identifying the top 25 features influencing salary prediction. Moreover, for all SHAP analyses, the feature importance \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Approach** & \(\mathbf{R^{2}}\) & \begin{tabular}{c} **MAE** \\ **(GBP)** \\ \end{tabular} & \
\begin{tabular}{c} **MSE (GBP)** \\ \end{tabular} &
\begin{tabular}{c} **RMSE** \\ **(GBP)** \\ \end{tabular} \\ \hline Without new features \& methods & 0.78 & \(1.84\times 10^{4}\) & \(8.69\times 10^{8}\) & \(2.95\times 10^{4}\) \\ With new features \& methods & 0.91 & \(1.28\times 10^{4}\) & \(6.50\times 10^{8}\) & \(2.55\times 10^{4}\) \\ Improvement (\%) & 16.67\% & 30.43\% & 25.20\% & 13.56\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance comparison of the GB model without and with new features and methods. values are computed using the test dataset to ensure unbiased and reliable interpretation of the models' predictive behavior on unseen data. Figure 2a-d show these features ranked by SHAP values. LR serves as a baseline, highlighting only three meaningful features: _goals_, _xG_, and _xGdiff_. This reflects its limitation to direct linear relationships, ignoring collinear or weaker predictors. DT captures non-linear interactions, identifying _xGChain_, _age_, and _league_weight_ as top features. It emphasizes players' involvement in buildup play, career stage, and league strength, with _xGBuildup_ and _assistsRatio_ also being influential. Traditional metrics like _goals_, _xG_, and _key_passes_ show little impact, indicating that DT values broader offensive and career factors over isolated scoring stats. Figure 2: Comparison of feature importance using SHAP values across ML models. (**a**) Linear Regression (LR); (**b**) Decision Tree (DT); (**c**) Gradient Boosting (GB); (**d**) XGBoost Regressor (XGB). GB similarly ranks _xGChain_, _age_, and _league_weight_ highest, reinforcing the importance of dynamic offensive roles and league quality. Features like _assistsRatio_, _xGBuildup_, and _xGg_ support the significance of playmaking and scoring potential. Moderate influence appears for shooting and passing metrics (_kppm_, _shpg_, _gpm_), while defensive and discipline metrics have less effect. Lower-ranked features include _key_passes_ and _npg_, highlighting a focus on overall attacking contribution. XGB, an optimized GB variant, also prioritizes _xGChain_, _age_, and _league_ratio_, emphasizing passing sequences, career stage, and league competitiveness. Additional important features include _assistsRatio_, _aGgRatio_, _xGg_, and encoded categorical variables such as _teamEtcoded_, and _positionen_, reflecting team strength and positional roles, respectively. Overall, SHAP analysis consistently identifies league competitiveness (_league_weight_), goal-related contributions, and positional context as key drivers in predicting player salaries. The growing importance of _league_weight_ across more complex models highlights their enhanced capacity to capture contextual performance factors. Notably, _league_weight_ ranks among the top three contributors in all models analyzed, with SHAP values of approximately 0.20, 0.31, and 0.35 for DT, GB, and XGB, respectively. This demonstrates that our newly engineered league weighting feature significantly improves the models' explanatory power regarding salary discrepancies, affirming its relevance in capturing league-specific salary effects. Individual goal contributions, specifically _goals_ and _assists_, consistently rank as key predictors across all models due to their fundamental role in evaluating player performance and value.