Machine learning approach for identification of peer quality factors among sportsman

Abstract

The process of identification multi-talent game players improves the chance of substitution of players among different games when situation demands. The application of machine learning and knowledge engineering techniques over player’s statistical data is a novel approach using Data mining techniques for this purpose. In this paper some standard machine learning techniques applied over training data collected from two different games (Volley-Ball and Basket-Ball). The physical characteristics are used for identification of quality factors among players which helps to estimate the player’s correlation in abilities among two games. The strong ARM (Association Rule Mining) applied for selecting highly cohesive qualities which improves player skills suitable for both games. When in National or International championship games substitutions for players is in scarcity for specific games this approach provides multigame players and increases the chance of winning trust in teams.