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dc.contributor.authorPandey, Niravkumar-
dc.description.abstractPlayer selection is one of the most important tasks for any sport and cricket is no exception. The performance of the players depends on various factors such as the opposition team, the venue, his current form etc. The team management, the coach and the captain select eleven players for each match from a squad of 15 to 20 players. They analyze different characteristics and the statistics of the players to select the best playing 11 for each match. Each batsman contributes by scoring maximum runs possible and each bowler contributes by taking maximum wickets and conceding minimum runs. This thesis attempts to predict the performance of players as how many runs each batsman will score and how many wickets each bowler will take for both teams in one-day international cricket matches. Both the problems are targeted as classification problems where number of runs and number of wickets are classified in different ranges. We used Naïve Bayes, Random Forest, multiclass SVM and Decision Tree classifiers to generate the prediction models for both the problems. Random Forest classifier was found to be the most accurate for both problems.en_CA
dc.subjectOne Day International (ODI)en_CA
dc.subjectsupervised learningen_CA
dc.subjectNaïve Bayesen_CA
dc.subjectRandom Foresten_CA
dc.subjectmulticlass SVMen_CA
dc.subjectdecision treesen_CA
dc.titlePredicting players’ performance in the game of cricket using machine learningen_CA
dc.description.degreeMaster of Science (MSc) in Computational Sciencesen_CA
dc.publisher.grantorLaurentian University of Sudburyen_CA
Appears in Collections:Computational Sciences - Master's theses
Master's Theses

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