Construction and Optimization of Sports Athlete Selection and Talent Cultivation System Based on Data Analysis

Abstract

Talent cultivation is the deliberate and systematic process of identifying, nurturing, and developing individuals' innate abilities and potential across various domains. It involves providing structured opportunities, resources, and guidance to help individuals flourish and excel in their chosen fields. This process encompasses education, mentorship, training programs, and practical experiences tailored to the specific needs and interests of each individual. This paper presents a novel approach to the construction and optimization of a Sports Athlete Selection and Talent Cultivation System through the integration of data analysis techniques, with a particular emphasis on the Anthropometric Invariance Gait Recognition for Pattern (AIGRP) system. Leveraging advanced algorithms, the AIGRP system enables precise characterization of athletes' biomechanical profiles based on anthropometric measurements and gait characteristics. By analyzing data collected from athletes, including age, gender, height, weight, and specific performance metrics, such as speed, strength, and endurance, the system facilitates personalized athlete selection and talent cultivation strategies. Furthermore, the objective and data-driven nature of the AIGRP system reduces biases inherent in traditional selection methods, leading to more efficient and effective talent identification. By analyzing data collected from athletes, including age, gender, height (mean: 175 cm), weight (mean: 70 kg), and specific performance metrics, such as speed (mean: 6.5 m/s), strength (mean: 140 kg), and endurance (mean: 47 min), the system facilitates personalized athlete selection and talent cultivation strategies.