Cricket is an increasingly popular sport due to the change in format and tournaments played. One of the most popular of these is the Twenty20 Cricket IPL, the financial stakes in which are growing sig- nificantly. A critical feature of the IPL are the player auctions that take place before the tournaments. Securing the best players at these auctions is key to performing well in the tournament. On the oth- er hand, bidding too high involves unnecessary spending. It would, therefore, be helpful to be able to forecast the winning price of a player. To this end, we used sports analytics, which is new sports analytics, which is a newly developing area of research aimed at deriving information based on data collected from previous games. For this study, we collected 15 indicators of earlier seasons. We first cleaned the data by performing a correlation analysis, which removed five indicators. Then, we used artificial natural networks to forecast auction prices. The Levenberg-Marquardt backpropagation model was found to be the best, with an accuracy of 99%.

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