# Predicting Soccer Player Salaries with Both Traditional and Automated Machine Learning Approaches
## 7 Limitations and Discussion  
While this study provides useful insights into soccer salary prediction, several limitations should be noted. First, the model uses historical data and does not account for real-time factors like form, injuries, or transfers, which can affect salary assessments. Second, the lack of physical and biometric data, whcih are often unavailable or confidential, limits the model's ability to reflect fitness and injury risks. Third, differences in salary structures across leagues may reduce generalizability. Additionally, the salary data, sourced from Capology via the Edd Webster repository, includes both verified and estimated values without clear distinction, introducing some uncertainty. While this may affect individual accuracy, the dataset still allows for a meaningful analysis of overall salary patterns. A further challenge arises from discrepancies in reported salary values for some high-profile players, with certain adjusted figures appearing implausibly low, likely due to imputed or placeholder entries where exact amounts were unavailable or unverified. These inconsistencies can lead to notable gaps between reported and predicted salaries. While such anomalies reflect limitations in publicly available salary data, our model is designed to capture broader salary trends rather than validate individual cases. Importantly, the predicted salaries align reasonably well with expected market values, supporting the robustness of the results despite imperfections in the input data. \begin{table}
\begin{tabular}{l l l c} \hline \hline
**Related Work** & **Key Features** & **Used Methods** & **Results** \\ \hline [PERSON] et al. (2021) [47] & Goals, assists, appearances & \begin{tabular}{l} Linear Regression, \\ Random Forest \\ \end{tabular} & \(R^{2}\): 0.71, MAE: \(8.40\times 10^{4}\), RMSE: N/A \\ \hline [PERSON], [PERSON] (2023) [10] & Streamlined features & \begin{tabular}{l} Gradient Boosting \\ Decision Tree \\ \end{tabular} & \(R^{2}\): 0.90, MAE: N/A, RMSE: \(3.22\times 10^{6}\) \\ \hline [PERSON] et al. (2024) [19] & Goals, assists, minutes & Tree-based models & Not reported \\ \hline [PERSON] et al. (2022) [6] & \begin{tabular}{l} Current age, position, \\ achievements \\ \end{tabular} & \begin{tabular}{l} ML with \\ GridSearchCV \\ \end{tabular} & \(R^{2}\): 0.60, MAE: N/A, RMSE: N/A \\ \hline [PERSON] et al. (2007) [13] & \begin{tabular}{l} Player performance, goals, \\ national team membership \\ \end{tabular} & \
\begin{tabular}{l} Not specified \\ \end{tabular} & Not reported \\ \hline [PERSON] \& [PERSON] (2017) [18] & Age, height, minutes played &
\begin{tabular}{l} Statistical \\ modeling \\ \end{tabular} & \(R^{2}\): N/A, MAE: N/A, RMSE: \(1.80\times 10^{7}\) \\ \hline
**Proposed Work** & \
\begin{tabular}{l} xGg, aGg, league/position \\ weights \\ \end{tabular} &
\begin{tabular}{l} Gradient Boosting, \\ Linear Regression \\ \end{tabular} & \(R^{2}\): 0.91, MAE: \(1.28\times 10^{4}\), RMSE: \(1.71\times 10^{6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison of existing studies with the proposed model. R\({}^{2}\) is a unitless metric representing the proportion of variance explained, while all monetary error metrics (MAE, MSE, RMSE) are reported in pounds (GBP). N/A indicates that the metric is not available or not applicable in the respective study. The validity of this analysis may be influenced by the reliability of the salary data employed in the study. Due to the lack of verified or standardized salary records and the absence of quantitative measures of uncertainty, precisely assessing the accuracy of the salary information remains challenging. While our dataset includes players from all positions, the engineered features are primarily based on attacking metrics due to their wider availability and consistency. This may introduce some positional bias, particularly for defenders and goalkeepers, whose contributions are better reflected through defensive statistics such as tackles, interceptions, and saves.