Methodology of Talent Selection and Long-term Performance Forecasting in Belt Wrestling Based on Artificial Intelligence Algorithms
Year: 2026
Type: article
Abstract: The modern era of high-performance sports is characterized by a transition toward "Sports 4.0," where data-driven decision-making replaces traditional scouting. This paper presents a comprehensive methodology for talent identification and long-term performance forecasting in Belt Wrestling (Belbog‘li Kurash) using advanced Artificial Intelligence (AI) and Machine Learning (ML). Traditional selection methods often suffer from "coaching subjectivism" and a bias toward early-maturing athletes (Relative Age Effect). To solve this, we developed a multi-factorial diagnostic system based on a dataset of 450 wrestlers. The "Kurash-Intellekt" software, utilizing Artificial Neural Networks (ANN) and Random Forest algorithms, was designed to process 42 unique biometric and technical indicators. Our longitudinal study (2024–2026) demonstrates that the AI-driven model identifies elite potential with 87% accuracy, representing a 25.1% improvement over traditional pedagogical methods (p < 0.05). Furthermore, the integration of "Digital Twins" allows for the simulation of individualized adaptive training trajectories. The findings suggest that AI can effectively predict athlete success over a 7-year horizon by prioritizing neurodynamic stability and biological age over static physical strength.
Author: Kuvondikov, Jaloliddin
Institution: Uzbek State University of Physical Culture and Sport
Language: English
Subfield: Orthopedics and Sports Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7218289475
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