Abstract: With the advent of statistical modelling in sports, predicting the outcome of a game has been established as a fundamental problem. Game consists of 11 player team sport played on ground. Cricket has huge fan base in India. With are great spectator support and many people try to predict the outcome of matches based on their individual cricket sense. The games has some rules and scoring system. Factors viz, match location and individual player performance have great impact on outcome of the match. Such various parameters are highly interdependent on each other which makes it heard to make precise prediction of the match. In this project, we are going to build prediction system that takes in data of matches played in past and makes a prediction of future match events such as final score and results in a gain or loss. Our system will predict match outcome by analysing pre-stored match data using various machine learning algorithms . We intend to use more features such as pitch condition, weather condition, outcome of toss, individual player performance with respect to match venue. Our system finally present quantitative results displayed by best suited algorithm having highest accuracy. Also, demonstrating the performance of our algorithms in predicting the number of runs scored which is one of the most important parameter of match outcome This work suggests that the relative team strength between the competing teams forms distinctive feature for predicting the winner. Modeling the team strength boils down to modeling individual player's batting and bowling performances, forming the basis of proposed approach. The career statistics as well as the recent performances of a player to model this. Player independent factors have also been considered in order to predict the outcome of match . The algorithm used are Decision Tree, Logistic regression and Support Vector Classifier (SVC) yields better results in the experimental evaluations.
Keywords : Prediction ; Cricket Match outcome prediction; Machine learning techniques
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DOI:
10.17148/IJARCCE.2022.11619
