Publication

Abstract : Cricket is one of the most popular sports in the world. In India, admiration for cricket is very huge for playing and as well as in viewing. When considering the aspect of players, who wanted to represent their country in this sport is also high. So, a need arises to find an easy way to filter out the players considering their skills and talent. Therefore, one of the simple ways to achieve the above is by proposing the best ML model. The fundamental intention of this model is to design a sophisticated prototype that assists cricket team selectors in making smart decisions regarding player selection. By analyzing player statistics like runs scored, wickets taken in different pitch conditions, and various other relevant factors, this model will provide valuable information to optimize team composition. Also comparing the performance of individuals in ODI and TEST conditions. At present country cricket board has a lot of options or choices because of the great talent and skill of the players. This model focuses on Indian players based on their category of role in the squad. It also makes the task easy for selectors to find the particular player for the required role for example consider batsmen there are different categories like Top Order, Middle Order, Wicketkeeper batsmen, etc. This model shows the compatibility of a particular player in the team based on statistics. The machine learning model that is developed shows the best-fit regression algorithm for players to be selected in ODI and TEST based on the required role needed for the team and also considers the individual player strengths for selecting them in the team. For predicting a player to be contoured in the ODI team algorithms like SVM and Decision Tree produce fewer error results whereas for predicting new players in the TEST team algorithms like LSTM and SVM produce fewer errors. So, the SVM model is the best regression technique for this kind of work.