In the Indian Premier League (IPL), a player auction process determines a player’s auction price. Before the start of each IPL season, teams bid on the players they want to buy. Past performance, current form, experience, and reputation can all have an impact on bidding for certain players. To find auction price of player, different systems like performance analysis, scouting, auction strategy, etc., are used. These techniques have few detriments like limited information, subjectivity, time-consuming. Due to this, the auction price can result in overvaluing or undervaluing certain players. To overcome these limitations, we have proposed model that uses multiple regression, as it allows for the analysis of many elements that may have an impact on performance and worth of players. In this task, we are using different cricketing attributes like rank, run above average (RAA), Wins, Eigen-factor score (EFscore), and Value in the proposed model to approximate the auction price of players. We design two models: the Value-Rank-Wins (VRW) and the Value-Rank-RAA-Wins-EFScore (VRRWE) model. These models are aligned to calculate the player’s auction price based on their past performances in the IPL and check whether the auction price assigned to the player is more or less. The dataset used for training and testing of model contains information of 659 players who played in at least one season of the IPL from 2008 to 2022.