Predictive analytics within the collegiate wrestling recruitment process

Abstract

In this study, we dive into the world of sports analytics, specifically focusing on wrestling. By harnessing data-driven insights, we aim to revolutionize student-athlete recruitment and development. The research focuses on Folkstyle wrestling at the high school and collegiate levels, where performance data is collected and analyzed. A new prediction model offers a fresh perspective on identifying promising wrestlers. Through rigorous statistical analysis, the model uncovered key factors that correlate with success on the mat. These insights empower coaches, recruiters, and student-athletes alike, providing a competitive edge in talent acquisition. However, the journey does not end here. We acknowledge the limitations of the study--namely, its applicability to other wrestling styles such as Freestyle and Greco-Roman. As we move forward, we encourage fellow researchers to build upon the foundation, expanding data collection and refining predictive models. In conclusion, this thesis bridges the gap between sports and data science, opening doors to transformative practices in wrestling. This study enables us to redefine how champions are discovered and nurtured.