Cricket Squad Analysis Using Multiple Random Forest Regression

In the game of cricket, analyzing the performance of a player is very crucial so as to have a well-balanced squad. Different tours demand various combinations of players as the conditions differ from stadium to stadium. Thus, the selectors have to consider various attributes of a player along with certain other attributes like experience of the player, performance in a particular condition and many more attributes. Such information can be obtained from the player’s career record. This paper covers the concepts of Multiple Random Forest Regression to be used to predict the value of the attributes of the batsmen and the bowlers in the given match, which will help in selecting the players for the given tour.The model will be used for the ODI format of the game. The past record of a player against a particular opposition is used as the dataset to train the model. The touring team, the opposition and the venue of the match are taken as input by the model. A rank-wise list of all the batsmen and bowlers is generated based on the input fields which can be used by the selectors to select the team as per the desired combination.