Abstract

The use of data analytics in professional football has changed the way clubs view, value and invest in players. Meanwhile, elite European teams have consistently exploited the data-driven strategies that offer them a competitive edge, but in up-and-coming football markets, such as the UAE Pro League, nepotism has remained strong, with reputation-based and agentdriven recruitment being the transformation choice of even the biggest profile moves. This work explores whether predictive analytics can discover hidden high-quality football talent from ”hotbeds” of talent including Brazil and Argentina, in a way that matches the economic capabilities of clubs in the UAE, given the constraints of the local league. Amidst increasing transfer fees and uncertain performances, this research investigates not only the theoretically and practically relevant intersections of talent identification, machine learning-based valuation models, and financial risk management. There are two main research questions of interest: (1) What approaches can be used through data-driven models to better estimate player value for regions that receive less attention from scouts? (2) Which performance metrics are best at predicting future market growth and on-field success? (3) What way do clubs fit these models into their strategy process around scouting and investment? We use a mixed-methods approach: supervised learning models that compare past input data to prediction output with statistical regression techniques and datasets fromTransfermarkt andWyscout. A total of 9596 players across various South America leagues were filtered by age, position and the trajectory of their value. These are then measured against UAE clubs for tactical fit and historical markers of success. To validate some observed trends in performance from model recommendations we study case studies. The findings suggest predictive models have utility in making scouting more effective and cost-efficient, and that over years predictive models can offer significant ROI particularly as qualitative scouting and video analysis are integrated with model reccomendations. The paper ends with suggestions on how clubs in the UAE can implement optimal, cost-effective, analytics-based recruitment strategies along with a plea for more research exploring ethical data use, contextual model tuning and cultural suitability in emerging markets.

Publication Date

12-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Khalid Ezzeldeen

Campus

RIT Dubai

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