Published Journal Link:

https://www.sciencedirect.com/science/article/abs/pii/S1568494622006044?CMX_ID=&SIS_ID=&dgcid=STMJ_AUTH_SERV_PUBLISHED&utm_acid=255637210&utm_campaign=STMJ_AUTH_SERV_PUBLISHED&utm_in=DM288869&utm_medium=email&utm_source=AC_

Undergraduate-Thesis

Athletic events attract enormous investment to the sporting world, providing both good entertainment and huge money to the sport's sector. These phenomena occur on a regular basis in all parts of the world and take diverse forms. Franchise-based cricket tournaments are growing increasingly popular. These competitions draw participants from all around the world who come to compete. As computer technology has enabled us to replicate the human brain, it has also enabled us to study previous data in order to predict the future.We utilize this opportunity to anticipate player performance, and based on that performance, we attempt to design some optimal squads that may be used to aid team owners in forming squads for tournaments. In this article, we learn how to implement the prediction process using machine learning methods, and we explore how multi-objective optimization approaches may help us create optimum squads more precisely by exploiting projected performances.