Navigating Player Performance Clusters for Sports Excellence

A strategic tactics is required to navigate the challenging process of athlete selection within budgetary limits, seeing it as a multiple-objective, multi-criteria optimization challenge. However, judgments made by humans are fallible and subjective, and choices made using a single criterion are insufficient and erratic. An approximate decision making in the calculation of cricket players selection is addressed in this work. The player can be selection based on the batsman and bowler object. The value is calculated using the various bowler and batsmen parameters. The performance of the selection of players are evaluated with using K-mean and GMM (Gaussian Mixture Model) machine learning algorithms and Recommender System Method.