Crowd-scouting: enhancing football talent identification through the use of machine learning and wisdom of crowds

Abstract

The identification of talented young footballers is a cornerstone of success in professional football. This capability empowers established clubs to nurture potential superstars who elevate team performance and propel them towards championship contention. Smaller clubs strategically leverage this skill set to develop talent for an eventual sale, boosting their financial situation and, in some instances, even mounting their own title challenges. Ultimately, the ability to recognize future elite players has consistently translated into a significant competitive advantage throughout the history of the sport. This thesis delves into this domain by comparing the performance of three supervised machine learning models (Random Forest, Gradient Boosting, and Support Vector Machines). The models were trained using two comprehensive datasets encompassing data for 1,086 male professional footballers. The first one incorporates player statistics, game-related attributes, and transfer market values. The second one incorporates YouTube metrics to leverage the well-established concept of the wisdom of crowds. This concept presumes that the collective intelligence of a large group can outperform individual judgment. The wisdom of the fans has the potential to optimize scouting efforts. Historical and literary evidence suggests that the most effective strategies combine data with human judgment, particularly for complex tasks such as talent identification. SVM demonstrated the highest effectiveness, achieving superior sensitivity and identifying the greatest proportion of elite players within the dataset under the baseline scenario following a 5-fold cross-validation. Although its performance declined after the inclusion of crowd-sourced features, SVM continued to capture the largest portion of elite players, despite its lower precision score. The crowd-sourced features exhibited surprising potential when integrated with tree-based models, enhancing both sensitivity and precision in identifying the minority class. These models successfully captured a significantly larger share of the minority class while preserving their discriminative capacity. Integrating the collective knowledge of football fans improved the performance of a classification algorithm in identifying elite players using the selected features; thus, thereby validating the hypothesis stated in this dissertation. Furthermore, the feature importance analysis and other valuable insights gleaned from the study pave the way for further research endeavors. By providing this comparative analysis, the study aims to encourage the adoption of advanced data analytics, statistical methods, and more crowd-sourced data within football clubs worldwide. This approach can empower them to optimize resource allocation and refine their talent identification strategies.