Implementation of Advanced Search Tools for Talent Identification in the Sports Industry: A Case Study of Basketball

This thesis aims to explore the potential of data-driven methodologies in basketball scouting and player performance evaluation, with the objective of enhancing the efficiency, objectivity, and effectiveness of player analysis and decision-making processes in professional basketball teams. The study leverages a specially curated dataset that incorporates performance metrics and personal attributes of athletes, including a regional perspective to identify talent prospects based on their birth region. By utilizing advanced analytics and appropriate tools, professional teams can make more informed decisions, gain valuable insights into player performance and potential, and bridge the gap between traditional scouting practices and modern data-driven approaches. The thesis proposes the development of a comprehensive platform, including the implementation of a dashboard, to provide scouts, coaches, and decision-makers with access to player information and facilitate quick and efficient decision-making processes. The dashboard serves as a central hub, enabling rapid and comprehensive access to crucial information for player evaluation and talent identification. Additionally, the thesis introduces the Optimal Growth Path algorithm, a model that integrates player evaluation criteria to predict the most suitable career paths for athletes, suggesting leagues where players statistically exhibit the most growth. This data-driven framework supports optimal player development and performance, providing insights into their potential and facilitating strategic planning. Through careful utilization of data, the thesis demonstrates the potential for improving decision-making, reducing biases, and enhancing athlete scouting. However, teams must carefully explore the opportunities and limitations to maximize the benefits and ensure the success of their scouting efforts. The findings of this thesis contribute to advancing basketball scouting practices by offering valuable insights into the integration of data-driven methodologies in player evaluation and talent identification. By embracing these approaches, teams can unlock new possibilities, make more informed decisions, and drive progress in the field of basketball scouting.