Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach

Abstract

Athletes represent the apex of physical capacity filling in a social picture of performance and build. In light of the fundamental contrasts in athletic capacities required for different games, each game demands an alternate body type standard. Because of the decent variety of these body types, each can have an altogether different body standard. Nowadays, a large number of athletes participate in assessments and a large number of human hours are spent on playing out these assessments every year. These assessments are performed to check the physical strength of athletes and evaluate them for different games. This paper presents a machine learning approach to the physical assessment of athletes known as NueroFATH. The proposed NueroFATH approach relies on neuro-fuzzy analytics that involves the deployment of neural networks and fuzzy c-means techniques to predict the athletes for the potential of winning medals. This can be achieved using athletes’ physical assessment parameters. The goal of this study is not only to identify the athletes based on which group they fall into (gold/silver/bronze), but also to understand which physical characteristic is important to identify them and categorize them in a medal group. It was determined that features, namely height, body mass, body mass index, 40 m and vertical jump are the most important for achieving 98.40% accuracy for athletes to classify them in the gold category when they are in the bronze category. Unsupervised learning showed that features, namely body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity have the highest variability. We can achieve upto 97.06% accuracy when features, i.e., body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity were used.