Machine Learning Based Assessment of Elite Football Players Based on Anthropometric and Motor Fitness Parameters with Regard to their Playing Positions

• Published in: SN computer science 2024-10, Vol.5 (8), p.974, Article 974
• Main Authors: Sneha, Santoshi, Prithvi, B. S., Niranjanamurthy, M., Kiran Kumar, H. K., Dayananda, P.
• Format: Article
• Language: English
• Subjects: Advanced Computing and Data Sciences; Anthropometry; Body size; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data Structures and Information Theory; Datasets; Football; Information Systems and Communication Service; Machine learning; Original Research; Parameter identification; Pattern Recognition and Graphics; Physical factors; Players; Psychological factors; Software Engineering/Programming and Operating Systems; Sports; Support vector machines; Survival analysis; Teams; Vision
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-024-03261-x

Sports players strive to be the epitome of human excellence, pushing the barrier of skill and execution with training, focus and direction, amplified by regular training and practice. This could be attributed to various factors such as response to stimuli, physical factors, psychological factors etc. The present study incorporated the prediction of the most suited playing position of elite male football players using machine learning approaches based on their Anthropometric Parameters (AP–11 parameters) and Motor Fitness Parameters (MFP–7 parameters). Of the features analysed, results identified the position indicative nature of some parameters among6 AP (Height, Body Mass Index, Basal Metabolic Rate, Fat %, Thigh Circumference, Calf circumference) and 4 MFP (120 m, 80 m, 40 m dash and T-Test) by Spearman’s Rank Correlation Test. Further, the prediction of ideal playing position was achieved using various classifiers such as Support Vector Machine (SVM), SVM with over sampled data, SVM with hyperparameter tuning, SVM with variable scaling and Extreme Gradient Boosting (XG Boost). Among these, the highest classification accuracy and f1-score at 92% and 0.92 respectively were obtained for XG Boost Classifier which portrayed a faster performance as compared to the other approaches. The present study could be useful in professional sports training and rehabilitation so as to help the players perform better in the football game.