# Predicting Soccer Player Salaries with Both Traditional and Automated Machine Learning Approaches
## 2 Literature Review and Study Overview
### Literature Review  
Traditional econometric and statistical methods remain foundational in analyzing and predicting soccer player salaries. These approaches typically use regression models, correlation analysis, and human capital theory to explain how individual and institutional factors influence wages. [PERSON] (2007) employed multiple regression analysis on German football data, identifying player productivity and career longevity as key salary determinants ([PERSON], 2007). Similarly, [PERSON] and [PERSON] (2009) studied the Italian Serie A and showed that experience, past season performance (e.g., goals scored), and international appearances significantly explain wage variation, reinforcing the role of ability and seniority in compensation ([PERSON], 2007). [PERSON] (2007) expanded the analysis by incorporating club-level economic factors, such as financial resources and league prestige, framing salary structures within the broader market and institutional environment ([PERSON], 2007). Meanwhile, [PERSON] et al. (2014) applied human capital theory to show the influence of formal education and on-the-job learning in wage outcomes ([PERSON], 2007). Other works, such as that of [PERSON] and [PERSON] (2007), emphasized brand value and player visibility, while [PERSON] et al. (2017) highlighted the role of negotiation power and market dynamics alongside performance ([PERSON], 2007; [PERSON], 2007).  
The application of machine learning (ML), a subfield of artificial intelligence (AI), offers advanced capabilities for predicting soccer players' salaries and market values ([PERSON], 2007; [PERSON] et al., 2014). While many studies have explored player performance through on-field metrics such as expected goals (\(xG\)), actual goals (\(aG\)), assists, and playing time, they often focus more on transfer valuation than direct salary prediction ([PERSON] et al., 2014; [PERSON] et al., 2014). Despite growing interest, salary modeling remains underexplored due to the complex interplay between performance, market dynamics, and contextual variables ([PERSON], 2007; [PERSON], 2007). Early ML-based research typically relied on core statistics, such as goals, assists, and minutes played ([PERSON], 2007). More recent work has expanded feature sets to include defensive metrics (e.g., interceptions, clearances), career dynamics (e.g., appearances, starting frequency), and contextual variables (e.g., age, nationality, and market value) ([PERSON], 2007; [PERSON], 2007). Several studies have integrated financial data like transfer fees and existing salaries, combining them with performance metrics to enhance predictive accuracy ([PERSON], 2007; [PERSON], 2007). These multi-dimensional models underscore the importance of modeling both on-field output (e.g., xG, aG) and broader influences on compensation. Advanced ML models, such as Random Forest, XGBoost, and Support Vector Regression, show strong predictive performance. For instance, [PERSON] et al. (2014) used pattern recognition to achieve a Pearson correlation of 0.77, while [PERSON] et al. (2011) optimized MLS salary distributions using ensemble learning. Hybrid approaches combining game statistics, FIFA data, and metaheuristic optimization techniques further demonstrate the versatility of ML in soccer salary prediction ([PERSON], 2007). Moreover, building upon earlier work, more recent studies have further advanced this line of analysis. For instance, [PERSON] and [PERSON] (2023) focused on goalkeeper-specific wage determinants, highlighting clean sheets and save efficiency as key factors ([PERSON], 2014). Moreover, [PERSON] and [PERSON] (2024) introduced behavioral insights into salary structures, emphasizing perceived fairness and institutional inequality ([PERSON] and [PERSON], 2014). Together, these studies enriched the traditional econometric approach by incorporating position-specific performance metrics, advanced regression techniques, and interdisciplinary perspectives, thereby providing a more nuanced understanding of soccer salary determinants.