Abstract
Cricket is one of the most celebrated open-air sports which have a huge amount of measurable information in the genuine world. As PSL games grow in popularity, the potential predictors influencing the outcome of the matches need to be investigated. The several years of PSL data containing the specifics of the player, match venue details, squads, ball-by-ball details, are taken and analyzed in this paper to draw different conclusions that offer assistance within the enhancement of a player’s execution. Through the expanding number of matches day by day, it has ended up troublesome to oversee or extricate valuable data from the accessible information of all the matches. This paper presents pre-processing of data, visualization, and prediction. It centers on measuring the result of Pakistan Super League (PSL) matches by applying the existing information mining algorithms. It includes factors like team1, team2, toss winner, toss decision, and predicts the match winner with the help of the Random Forest algorithm.
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