# Predicting Soccer Player Salaries with Both Traditional and Automated Machine Learning Approaches
## 6 Evaluation Results
### Salary Discrepancy Analysis Using GB  
As shown in Section 5, the GB model is the most effective for predicting player salaries. Using this model, we analyze salary discrepancies by comparing actual versus predicted earnings. Players are classified as underestimated (predicted salary higher than actual) or overestimated (actual salary higher than predicted). To improve prediction reliability, we use the top 25 influential features identified via SHAP analysis. This approach highlights potential market inefficiencies and league-specific salary patterns. We also examine factors such as age, league, and position to better understand the drivers behind these discrepancies. Table 10 lists the top five underestimated and overestimated players. Each has an actual salary of GBP 2.1 M, yet their predicted salaries reveal large gaps. [PERSON] shows the largest shortfall with a predicted GBP 26.3 M, a GBP 24.2 M difference. [PERSON] (GBP 21.4 M) and [PERSON] (GBP 11.9 M) also have significant underestimations. These gaps suggest valuation misalignments influenced by contracts, wage policies, salary structures, and aging effects. Among overestimated players, actual salaries exceed predicted values, indicating potential overvaluation. [PERSON] earns GBP 21.95 M compared to a predicted GBP 1.99 M, a GBP 19.96 M gap. [PERSON] (GBP 24.9 M), [PERSON] (GBP 24.7 M), [PERSON] (GBP 16.82 M), and [PERSON] (GBP 20.7 M) follow with similar discrepancies. Furthermore, Table 11 shows that most players (3449) are underestimated, while 1789 are overestimated, indicating that the model generally predicts lower than actual salaries. We analyzed how salary predictions vary across different age groups and player positions to gain deeper insights into the internal factors influencing salary estimations. Both features consistently emerge as significant predictors in our ML models, underscoring their strong influence on salary outcomes. Moreover, age and position are well-established determinants of player compensation in professional football, significantly affecting a player's market value, performance expectations, and career trajectory. These factors are critical for understanding salary disparities. Figure 3a presents a detailed comparison of predicted and actual salary differences across career stages. The results show that the model effectively captures salary trends by age, particularly for mid-career players (26-30 years), where predictions closely match actual salaries. This indicates that the model incorporates key performance factors influencing peak-year salaries. For younger players (15-25 years), some underestimations likely reflect real-world contracts, where emerging talents earn lower base wages before renegotiations. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Player** & **Adj. Salary** & **Pred. Salary (GBP M)** & **Diff. (GBP M)** \\  & **(GBP M)** & **M)** & \\ \hline \multicolumn{4}{c}{**Underestimated players**} \\ \hline [PERSON] & 2.1 & 26.3 & 24.2 \\ [PERSON] & 2.1 & 23.5 & 21.4 \\ [PERSON] & 2.1 & 14.0 & 11.9 \\ [PERSON] & 2.1 & 13.8 & 11.7 \\ [PERSON] & 2.1 & 13.1 & 11.0 \\ \hline \multicolumn{4}{c}{**Overestimated players**} \\ \hline [PERSON] & 21.95 & 1.99 & \(-\)19.96 \\ [PERSON] & 28.1 & 3.3 & \(-\)24.9 \\ [PERSON] & 27.0 & 2.3 & \(-\)24.7 \\ [PERSON] & 19.50 & 2.68 & \(-\)16.82 \\ [PERSON] & 28.1 & 7.4 & \(-\)20.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Top 5 underestimated and overestimated players based on salary difference (in GBP M). \begin{table}
\begin{tabular}{c c} \hline \hline
**Salary Category** & **Number of Unique Players** \\ \hline Overestimated & 1789 \\ Underestimated & 3449 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Distribution of salary categories for unique players. Thus, these predictions may realistically represent early-stage salary trajectories rather than systematic bias.