A novel approach for decision support system in cricket using machine learning

In shorter format of cricket, the choice of a bowler has three main parameters namely: economy, strike rate and dot balls delivered. In most of the cases, the most hitting parameters are economy rate and number of wickets taken, which again are inter-related with the dot balls delivered. This paper presents a survey operational linear approach which comparatively analyses the above-cited three parameters and suggests a solution-based approach to choose a best bowler in ‘Playing Eleven’ with highest preference to the dot balls delivered. The bowler with highest dot ball delivered is considered as highest preference bowler. The inter-relationships among these parameters are established based on collected data. The proposed indicator is proved useful while making decisions. A software-based architecture is also proposed relating to decision support system for selecting a bowler in playing eleven using past data.