Development of the mathematical model for calculating player ratings using soft calculations

Abstract

This research signifies an ambitious step forward in sports analytics, aiming to formulate a novel mathematical model that assesses team sports players’ performance with higher precision. It aspires to unravel a deeper understanding of player abilities, a complex task that requires advanced computational modeling and statistical analysis. The proposed model is built upon cutting-edge soft computing techniques. These techniques – fuzzy logic, neural networks, and genetic algorithms - are expertly integrated, each contributing unique elements to enhance the model’s accuracy and dependability. Fuzzy logic, with its capacity to handle ambiguity, provides nuanced evaluations, accounting for sports’ inherent uncertainties. Neural networks offer the model a capacity to learn and evolve, refining its evaluations as it processes new data. Genetic algorithms, modeled on natural evolution, optimize the model’s decision-making process, highlighting the most successful player strategies. This innovative approach could reshape player evaluations, replacing one-dimensional, static metrics with a dynamic, multi-faceted framework. Coaches, managers, and analysts will be equipped with a robust tool for decision-making and talent sourcing, ushering in a new era of sports analytics.