A wicket, usually made of wood, is used as a target of the bowler. At both ends of the pitch, 20 m apart, wickets are placed. Cricket matches are played on a grass field, in the center of which is a flat strip of ground 20 m long and 3 m wide called the pitch.

Each team consists of eleven players that include several batsmen, several bowlers, and a wicket keeper. Given the rising popularity of cricket, and the increasing revenue generated by the sport, the use of such a prediction tool could be of tremendous benefit to decision-makers in cricket.Ĭricket is a field game played between two teams. While all the six methods performed well, logistic regression, linear discriminant function, quadratic discriminant function, and support vector machine showed outstanding performance suggesting that these methods can be used to develop an automated classification rule to classify all-rounders in cricket. Evaluation of the performance of the classification methods was done using the metrics accuracy and area under the ROC curve. In particular, logistic regression, linear discriminant function, quadratic discriminant function, naïve Bayes, support vector machine, and random forest classification methods were explored. In this study, the authors have explored different machine learning techniques to classify all-rounders into bowling all-rounders or batting all-rounders based on their observed performance statistics. Current practice is to do so by mostly subjective methods. However, these players still have their dominant role by which we categorize them as batting all-rounders or bowling all-rounders. A good all-rounder should be able to contribute to the team by both bat and ball as needed. In cricket, all-rounders play an important role.

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