الانتقاء الرياضي باستخدام أدوات الذكاء الاصطناعي

Abstract

This thesis aims to design an intelligent model based on artificial intelligence and machine learning techniques to guide football players towards optimal playing positions based on their physical and morphological characteristics. The study adopted a descriptive survey methodology, analyzing data from 938 players in the French League (Ligue 1 and Ligue 2) for the 2022-2023 sports season. Physical and morphological measurements were collected, and machine learning algorithms (KNN, SVM, Random Forest) were applied to classify players into positions based on physical metrics. The results revealed that the machine learning models were able to classify players with an accuracy ranging between 70.21% and 89.36%, with clear physical differences observed between various positions. Central defenders were characterized by height, strength, and jumping ability; midfielders excelled in endurance and agility; while attacking players were distinguished by speed, acceleration, and explosive power. The results also showed a slight superiority of the Random Forest algorithm in most classification scenarios. The study provides a practical framework that coaches and football academies can rely on to enhance sports selection and orientation processes