Assembling an Optimal Cricket Team to Enhance the Winnability Using Machine Learning Techniques

IPL is a franchise system based, annual cricket tournament. IPL deals with millions of dollars. This imposes high pressure on team owners to search victories, which depends on team performance. The aim of this research is to assemble an optimal cricket team within a given budget to enhance the winnability. Several efforts have already been taken to address this problem without much success. They focused on identifying different performance metrics based on their domain knowledge of cricket. Essentially, it is critical to find the right set of metrics that would lead to assemble a team with the highest chance of winning. The proposed solution is, rely on statistical analysis and machine learning while minimizing the use of domain knowledge. This study has started with gathering and refining necessary data. Then an optimal set of attributes has been identified, which impose the high impact on the end results of a cricket match. For this, Classification algorithms have been used and SVM gave the best accuracy. Thereafter, a bid value prediction system has been implemented to predict bid values of players. Regression techniques used for this. Finally, a mathematical equation formed to calculate the winnability of a team. This equation is a linear relationship between a team’s winnability and a weighted sum of players’ performances. Using this equation we can then assemble an optimal cricket team to enhance the winnability by using constraint optimization