# Predicting Soccer Player Salaries with Both Traditional and Automated Machine Learning Approaches
###### Abstract  
Soccer's global popularity as the world's favorite sport is driven by many factors, with high player salaries being one of the key reasons behind its appeal. These salaries not only reflect on-field performance, but also capture a broader evaluation of player value. Despite the increasing use of performance data in sports analytics, a critical gap remains in establishing fair compensation models that comprehensively account for both quantifiable and intangible contributions. To address these challenges, this study adopts machine learning (ML) techniques that model player salaries based on a combination of performance metrics and contextual features. This research focuses on reducing bias and improving transparency in salary decisions through a systematic, data-driven approach. Utilizing a dataset spanning the 2016-2022 seasons, we apply both traditional and automated ML frameworks to uncover the most influential factors in salary determination. The results indicate a nearly 17% improvement in R\({}^{2}\) and about a 30% reduction in MAE after incorporating the newly constructed features and methods, demonstrating a significant enhancement in model performance. Gradient Boosting demonstrates superior effectiveness, revealing a group of significantly underestimated and overestimated players, and showcasing the model's proficiency in detecting valuation discrepancies. soccer player salaries; salary prediction; performance metrics; predictive modeling; actual goals; expected goals; machine learning; European soccer leagues 2020  
10 June 20251  
Player salaries are influenced by factors such as club wealth, league exposure, endorsements, and global fanbase. On-field performance metrics, such as assists, expected goals (\(xG\)), and actual goals (\(aG\)), also play key roles in salary evaluation [4]. Additionally, demographic and contextual features, such as age, nationality, and career experience, impact marketability and salary within league and club dynamics [5]. However, prior research often neglects positional roles and league-specific factors [6; 7], focusing mainly on transfer valuations rather than salaries [8; 9]. Salary decisions in professional soccer are frequently influenced by incomplete data and subjective judgments, leading to the potential undervaluation of players who contribute in less visible but essential roles, as well as the overvaluation of more popular or high-scoring players. This imbalance can affect team dynamics, player motivation, and financial planning within clubs. Establishing a transparent, data-driven framework for salary evaluation not only fosters fairness and equity across different playing positions but also provides clubs with more reliable tools for contract negotiations and budgeting. Moreover, as the use of advanced analytics becomes increasingly integral to sports management, developing interpretable and unbiased machine learning models is crucial for gaining trust among stakeholders and facilitating informed decision-making in a competitive and financially complex environment. Despite advances in machine learning, accurately predicting soccer salaries remains complex due to many interacting factors [6; 10; 11]. Furthermore, automated machine learning (AutoML) techniques, which automate key tasks such as model selection, tuning, and feature engineering, are increasingly applied in various fields [12]. However, their use in soccer salary prediction remains limited, with most studies relying on traditional models. Yet, even with advanced modeling approaches like AutoML, determining fair and accurate salaries involves a complex interplay of measurable and intangible factors. Qualitative elements such as leadership, team influence, and positional roles often difficult to quantify continue to play a crucial role in salary decisions. ML offers a promising approach but also brings its own challenges, such as difficulty in capturing intangible factors and ensuring balanced, unbiased predictions across different player positions.